From 94fc70ccc521c967be0d8e06a7bb8d4fcb41386e Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Sat, 8 Feb 2025 03:56:59 +0000 Subject: [PATCH] Updated datasets 2025-02-08 UTC --- datasets/ATL07QL_006.json | 2 +- datasets/ATL08QL_006.json | 2 +- datasets/ATL09QL_006.json | 2 +- datasets/ATL10QL_006.json | 2 +- datasets/ATL13QL_006.json | 2 +- datasets/ATSM2AEF_001.json | 3 +- datasets/ATSM2LSF_001.json | 7 + datasets/ATSM2STF_001.json | 7 + datasets/BUVN04L2_1.json | 21 + datasets/BUVN04L3zm_1.json | 21 + datasets/BUVN4L1DCM_001.json | 21 + datasets/BUVN4L1DCW_001.json | 21 + datasets/BUVN4L1PDB_001.json | 21 + datasets/BUVN4L1RUT_001.json | 21 + datasets/BUVN4L2CPOZ_005.json | 21 + datasets/BUVN4L2HDBUV_005.json | 21 + datasets/BUVN4L3ZMT_005.json | 21 + datasets/CZCS_L1_2.json | 6 +- .../DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3.json | 157 ++ datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json | 21 + datasets/MASTER_GEMx_Summer_2023_2319_1.json | 2 +- datasets/MI1AC_2.json | 7 + datasets/MI1AOBC_2.json | 7 + datasets/MI3DAENF_002.json | 7 + datasets/MI3DALF_002.json | 3 +- datasets/MI3DALNF_002.json | 7 + datasets/MI3DCDF_002.json | 7 + datasets/MI3DCDNF_002.json | 7 + datasets/MI3DLSF_002.json | 7 + datasets/MI3DLSNF_002.json | 7 + datasets/MI3DRDF_002.json | 7 + datasets/MI3MAEF_002.json | 7 + datasets/MI3MAENF_002.json | 7 + datasets/MI3MALF_002.json | 7 + datasets/MI3MALNF_002.json | 7 + datasets/MI3MCDF_002.json | 7 + datasets/MI3MCDNF_002.json | 7 + datasets/MI3MLSF_002.json | 7 + datasets/MI3MLSNF_2.json | 7 + datasets/MI3MRDF_002.json | 7 + datasets/MIL1A_2.json | 1 - datasets/MIL2ASAF_002.json | 3 +- datasets/MIL2ASLF_002.json | 7 + datasets/MIL2TCAF_001.json | 7 + datasets/MIL2TCCF_001.json | 7 + datasets/MIL2TCSF_001.json | 7 + datasets/MIRCCMF_001.json | 7 + datasets/MultiInstrumentFusedXCO2_4.json | 21 + datasets/OCO2GriddedXCO2_4.json | 21 + datasets/OCO2GriddedXCO2_SIF_4.json | 21 + datasets/OCO3_L1aAE_11.json | 21 + datasets/OCTS_L1_2.json | 10 +- datasets/OCTS_L2_IOP_2022.0.json | 12 +- datasets/OCTS_L2_OC_2022.0.json | 18 +- datasets/OCTS_L3b_CHL_2022.0.json | 12 +- datasets/OCTS_L3b_IOP_2022.0.json | 16 +- datasets/OCTS_L3b_KD_2022.0.json | 12 +- datasets/OCTS_L3b_PAR_2022.0.json | 10 +- datasets/OCTS_L3b_PIC_2022.0.json | 10 +- datasets/OCTS_L3b_POC_2022.0.json | 8 +- datasets/OCTS_L3b_RRS_2022.0.json | 10 +- datasets/OCTS_L3m_CHL_2022.0.json | 8 +- datasets/OCTS_L3m_IOP_2022.0.json | 18 +- datasets/OCTS_L3m_KD_2022.0.json | 10 +- datasets/OCTS_L3m_PAR_2022.0.json | 6 +- datasets/OCTS_L3m_PIC_2022.0.json | 10 +- datasets/OCTS_L3m_POC_2022.0.json | 10 +- datasets/OCTS_L3m_RRS_2022.0.json | 10 +- datasets/OMTO3G_004.json | 2 +- datasets/OMTO3_004.json | 2 +- datasets/SPL1BTB_NRT_105.json | 2 +- datasets/SPL2SMP_NRT_107.json | 2 +- datasets/SeaWiFS_L1_GAC_2.json | 2 +- datasets/SeaWiFS_L1_MLAC_2.json | 2 +- datasets/VIIRSJ1_L2_IOP_2022.0.json | 158 ++ datasets/VIIRSJ1_L2_IOP_NRT_2022.0.json | 158 ++ datasets/VIIRSJ1_L2_OC_2022.0.json | 162 ++ datasets/VIIRSJ1_L2_OC_NRT_2022.0.json | 162 ++ datasets/VIIRSJ1_L3b_CHL_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_CHL_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_IOP_2022.0.json | 158 ++ datasets/VIIRSJ1_L3b_IOP_NRT_2022.0.json | 158 ++ datasets/VIIRSJ1_L3b_KD_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_KD_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_LAND_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_LAND_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_PAR_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_PAR_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_PIC_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_PIC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_POC_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_POC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3b_RRS_2022.0.json | 154 ++ datasets/VIIRSJ1_L3b_RRS_NRT_2022.0.json | 154 ++ datasets/VIIRSJ1_L3m_CHL_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_CHL_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_IOP_2022.0.json | 158 ++ datasets/VIIRSJ1_L3m_IOP_NRT_2022.0.json | 158 ++ datasets/VIIRSJ1_L3m_KD_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_KD_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_LAND_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_LAND_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_PAR_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_PAR_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_PIC_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_PIC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_POC_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_POC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ1_L3m_RRS_2022.0.json | 154 ++ datasets/VIIRSJ1_L3m_RRS_NRT_2022.0.json | 154 ++ datasets/VIIRSJ2_L2_IOP_2022.0.json | 158 ++ datasets/VIIRSJ2_L2_IOP_NRT_2022.0.json | 158 ++ datasets/VIIRSJ2_L2_OC_2022.0.json | 162 ++ datasets/VIIRSJ2_L2_OC_NRT_2022.0.json | 162 ++ datasets/VIIRSJ2_L3b_CHL_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_CHL_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_IOP_2022.0.json | 158 ++ datasets/VIIRSJ2_L3b_IOP_NRT_2022.0.json | 158 ++ datasets/VIIRSJ2_L3b_KD_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_KD_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_PAR_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_PAR_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_PIC_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_PIC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_POC_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_POC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3b_RRS_2022.0.json | 154 ++ datasets/VIIRSJ2_L3b_RRS_NRT_2022.0.json | 154 ++ datasets/VIIRSJ2_L3m_CHL_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_CHL_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_IOP_2022.0.json | 158 ++ datasets/VIIRSJ2_L3m_IOP_NRT_2022.0.json | 158 ++ datasets/VIIRSJ2_L3m_KD_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_KD_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_PAR_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_PAR_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_PIC_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_PIC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_POC_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_POC_NRT_2022.0.json | 150 ++ datasets/VIIRSJ2_L3m_RRS_2022.0.json | 154 ++ datasets/VIIRSJ2_L3m_RRS_NRT_2022.0.json | 154 ++ nasa_cmr_catalog.json | 2344 ++++++++++++----- nasa_cmr_catalog.tsv | 722 ++--- 144 files changed, 13124 insertions(+), 1174 deletions(-) create mode 100644 datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3.json create mode 100644 datasets/VIIRSJ1_L2_IOP_2022.0.json create mode 100644 datasets/VIIRSJ1_L2_IOP_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L2_OC_2022.0.json create mode 100644 datasets/VIIRSJ1_L2_OC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_CHL_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_CHL_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_IOP_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_IOP_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_KD_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_KD_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_LAND_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_LAND_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_PAR_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_PAR_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_PIC_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_PIC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_POC_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_POC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_RRS_2022.0.json create mode 100644 datasets/VIIRSJ1_L3b_RRS_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_CHL_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_CHL_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_IOP_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_IOP_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_KD_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_KD_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_LAND_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_LAND_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_PAR_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_PAR_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_PIC_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_PIC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_POC_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_POC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_RRS_2022.0.json create mode 100644 datasets/VIIRSJ1_L3m_RRS_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L2_IOP_2022.0.json create mode 100644 datasets/VIIRSJ2_L2_IOP_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L2_OC_2022.0.json create mode 100644 datasets/VIIRSJ2_L2_OC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_CHL_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_CHL_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_IOP_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_IOP_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_KD_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_KD_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_PAR_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_PAR_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_PIC_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_PIC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_POC_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_POC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_RRS_2022.0.json create mode 100644 datasets/VIIRSJ2_L3b_RRS_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_CHL_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_CHL_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_IOP_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_IOP_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_KD_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_KD_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_PAR_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_PAR_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_PIC_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_PIC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_POC_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_POC_NRT_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_RRS_2022.0.json create mode 100644 datasets/VIIRSJ2_L3m_RRS_NRT_2022.0.json diff --git a/datasets/ATL07QL_006.json b/datasets/ATL07QL_006.json index c3f39b137d..2043e1123a 100644 --- a/datasets/ATL07QL_006.json +++ b/datasets/ATL07QL_006.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2024-08-29T00:00:00Z", + "2024-11-08T00:00:00Z", null ] ] diff --git a/datasets/ATL08QL_006.json b/datasets/ATL08QL_006.json index 41794b69c0..9e2f4e9c79 100644 --- a/datasets/ATL08QL_006.json +++ b/datasets/ATL08QL_006.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2024-08-29T00:00:00Z", + "2024-11-07T00:00:00Z", null ] ] diff --git a/datasets/ATL09QL_006.json b/datasets/ATL09QL_006.json index 0aa25a5b5f..62d20be7d8 100644 --- a/datasets/ATL09QL_006.json +++ b/datasets/ATL09QL_006.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2024-08-29T00:00:00Z", + "2024-11-08T00:00:00Z", null ] ] diff --git a/datasets/ATL10QL_006.json b/datasets/ATL10QL_006.json index 8494068d1b..28b20aed50 100644 --- a/datasets/ATL10QL_006.json +++ b/datasets/ATL10QL_006.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2024-08-29T00:00:00Z", + "2024-11-08T00:00:00Z", null ] ] diff --git a/datasets/ATL13QL_006.json b/datasets/ATL13QL_006.json index 05d9d54572..59aa6ea53b 100644 --- a/datasets/ATL13QL_006.json +++ b/datasets/ATL13QL_006.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2024-08-30T00:00:00Z", + "2024-11-07T00:00:00Z", null ] ] diff --git a/datasets/ATSM2AEF_001.json b/datasets/ATSM2AEF_001.json index 60a3c04d2c..053567334d 100644 --- a/datasets/ATSM2AEF_001.json +++ b/datasets/ATSM2AEF_001.json @@ -130,9 +130,8 @@ ] }, "provider_metadata": { - "href": "https://asdc.larc.nasa.gov/project/ARCTAS/ATSM2AEF_001", + "href": "https://asdc.larc.nasa.gov/project/MISR/ATSM2AEF_001", "title": "Provider Metadata", - "description": "Data set landing page for ATSM2AEF_1", "roles": [ "metadata" ] diff --git a/datasets/ATSM2LSF_001.json b/datasets/ATSM2LSF_001.json index e1d5b68ac3..e29420739b 100644 --- a/datasets/ATSM2LSF_001.json +++ b/datasets/ATSM2LSF_001.json @@ -134,6 +134,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/ATSM2LSF_001", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000581-LARC.xml", "type": "application/xml", diff --git a/datasets/ATSM2STF_001.json b/datasets/ATSM2STF_001.json index 09186adf91..0e747e0fb5 100644 --- a/datasets/ATSM2STF_001.json +++ b/datasets/ATSM2STF_001.json @@ -126,6 +126,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/ATSM2STF_001", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000563-LARC.xml", "type": "application/xml", diff --git a/datasets/BUVN04L2_1.json b/datasets/BUVN04L2_1.json index 2981fc928c..225738091b 100644 --- a/datasets/BUVN04L2_1.json +++ b/datasets/BUVN04L2_1.json @@ -141,6 +141,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Ozone_BUVN04L2_1_": { + "href": "s3://gesdisc-cumulus-prod-protected/Ozone/BUVN04L2.1/", + "title": "gesdisc_cumulus_prod_protected_Ozone_BUVN04L2_1_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1251051137-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN04L3zm_1.json b/datasets/BUVN04L3zm_1.json index b8400daccd..61321a402b 100644 --- a/datasets/BUVN04L3zm_1.json +++ b/datasets/BUVN04L3zm_1.json @@ -141,6 +141,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Ozone_BUVN04L3zm_1_": { + "href": "s3://gesdisc-cumulus-prod-protected/Ozone/BUVN04L3zm.1/", + "title": "gesdisc_cumulus_prod_protected_Ozone_BUVN04L3zm_1_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1251051178-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L1DCM_001.json b/datasets/BUVN4L1DCM_001.json index d136d4865e..bd07baf7c1 100644 --- a/datasets/BUVN4L1DCM_001.json +++ b/datasets/BUVN4L1DCM_001.json @@ -148,6 +148,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1DCM_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1DCM.001/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1DCM_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652183-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L1DCW_001.json b/datasets/BUVN4L1DCW_001.json index c7228d8487..320757e80f 100644 --- a/datasets/BUVN4L1DCW_001.json +++ b/datasets/BUVN4L1DCW_001.json @@ -148,6 +148,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1DCW_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1DCW.001/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1DCW_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652163-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L1PDB_001.json b/datasets/BUVN4L1PDB_001.json index 39426e0841..b86377f9a4 100644 --- a/datasets/BUVN4L1PDB_001.json +++ b/datasets/BUVN4L1PDB_001.json @@ -148,6 +148,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1PDB_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1PDB.001/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1PDB_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652184-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L1RUT_001.json b/datasets/BUVN4L1RUT_001.json index 526250508f..afeedbf916 100644 --- a/datasets/BUVN4L1RUT_001.json +++ b/datasets/BUVN4L1RUT_001.json @@ -148,6 +148,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1RUT_001_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level1/BUVN4L1RUT.001/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level1_BUVN4L1RUT_001_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652150-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L2CPOZ_005.json b/datasets/BUVN4L2CPOZ_005.json index 45f16df760..bfc68dfe01 100644 --- a/datasets/BUVN4L2CPOZ_005.json +++ b/datasets/BUVN4L2CPOZ_005.json @@ -150,6 +150,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level2_BUVN4L2CPOZ_005_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level2/BUVN4L2CPOZ.005/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level2_BUVN4L2CPOZ_005_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652185-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L2HDBUV_005.json b/datasets/BUVN4L2HDBUV_005.json index fc531c67cf..ba667a81d2 100644 --- a/datasets/BUVN4L2HDBUV_005.json +++ b/datasets/BUVN4L2HDBUV_005.json @@ -150,6 +150,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level2_BUVN4L2HDBUV_005_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level2/BUVN4L2HDBUV.005/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level2_BUVN4L2HDBUV_005_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652186-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/BUVN4L3ZMT_005.json b/datasets/BUVN4L3ZMT_005.json index e522443801..c62e6fb8b5 100644 --- a/datasets/BUVN4L3ZMT_005.json +++ b/datasets/BUVN4L3ZMT_005.json @@ -150,6 +150,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level3_BUVN4L3ZMT_005_": { + "href": "s3://gesdisc-cumulus-prod-protected/Nimbus4_BUV_Level3/BUVN4L3ZMT.005/", + "title": "gesdisc_cumulus_prod_protected_Nimbus4_BUV_Level3_BUVN4L3ZMT_005_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1273652151-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/CZCS_L1_2.json b/datasets/CZCS_L1_2.json index cc1754af55..fe9f40c2a8 100644 --- a/datasets/CZCS_L1_2.json +++ b/datasets/CZCS_L1_2.json @@ -81,10 +81,10 @@ }, "license": "proprietary", "keywords": [ - "Ocean Optics", - "Earth Science", "Oceans", - "Ocean Color" + "Earth Science", + "Ocean Color", + "Ocean Optics" ], "providers": [ { diff --git a/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3.json b/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3.json new file mode 100644 index 0000000000..daf25b035c --- /dev/null +++ b/datasets/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3.json @@ -0,0 +1,157 @@ +{ + "type": "Collection", + "id": "DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3", + "stac_version": "1.1.0", + "description": "This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) from 2021-03-27 to 2021-04-06 and 2021-08-18 to 2021-09-25. Reflectance data are provided for each flight line. In addition, ten files of mosaicked flight lines, by time period and over four locations (labeled Terre, Atcha, TerreEast, and Bara), are included. Data are provided as binary ENVI image and header files. Only land pixels were corrected; mask files for the mosaic file coverage showing presence/absence of water and clouds are also included. For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise.", + "links": [ + { + "rel": "license", + "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy", + "type": "text/html", + "title": "EOSDIS Data Use Policy" + }, + { + "rel": "about", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.html", + "type": "text/html", + "title": "HTML metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.native", + "type": "application/xml", + "title": "Native metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.echo10", + "type": "application/echo10+xml", + "title": "ECHO10 metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.json", + "type": "application/json", + "title": "CMR JSON metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.umm_json", + "type": "application/vnd.nasa.cmr.umm+json", + "title": "CMR UMM_JSON metadata for collection" + }, + { + "rel": "self", + "href": "https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD/collections/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3", + "type": "application/json" + }, + { + "rel": "root", + "href": "https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD", + "type": "application/json", + "title": "ORNL_CLOUD STAC Catalog" + }, + { + "rel": "items", + "href": "https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD/collections/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3/items", + "type": "application/geo+json", + "title": "Collection Items" + } + ], + "title": "Delta-X: AVIRIS-NG BRDF-Adjusted Surface Reflectance, MRD, LA, 2021, V3", + "extent": { + "spatial": { + "bbox": [ + [ + -91.59, + 29.05, + -89.07, + 30.23 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2021-03-27T00:00:00Z", + "2021-09-25T23:59:59Z" + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "EARTH SCIENCE", + "LAND SURFACE", + "EROSION/SEDIMENTATION", + "GEOMORPHIC LANDFORMS/PROCESSES", + "COASTAL PROCESSES", + "SEDIMENTATION", + "SURFACE RADIATIVE PROPERTIES", + "REFLECTANCE" + ], + "providers": [ + { + "name": "ORNL_CLOUD", + "roles": [ + "producer" + ] + }, + { + "name": "NASA EOSDIS", + "roles": [ + "host" + ] + } + ], + "summaries": { + "platform": [ + "B-200" + ], + "instruments": [ + "AVIRIS-NG" + ] + }, + "assets": { + "browse": { + "href": "https://daac.ornl.gov/DELTAX/guides/DeltaX_L2A_AVIRIS-NG_BRDF_V3_Fig1.jpg", + "type": "image/jpeg", + "title": "Download DeltaX_L2A_AVIRIS-NG_BRDF_V3_Fig1.jpg", + "roles": [ + "browse" + ] + }, + "thumbnail": { + "href": "https://daac.ornl.gov/DELTAX/guides/DeltaX_L2A_AVIRIS-NG_BRDF_V3_Fig1.jpg", + "title": "Thumbnail", + "description": "Figure 1: A BRDF and sunglint-corrected image of the Atchafalaya basin in coastal Louisiana, U.S.", + "roles": [ + "thumbnail" + ] + }, + "nasa": { + "href": "https://search.earthdata.nasa.gov/search?q=DeltaX_L2A_AVIRIS-NG_BRDF_V3", + "title": "Direct Download", + "description": "This link allows direct data access via Earthdata login", + "roles": [ + "data" + ] + }, + "provider_metadata": { + "href": "https://doi.org/10.3334/ORNLDAAC/2355", + "title": "Provider Metadata", + "description": "Data set Landing Page DOI URL", + "roles": [ + "metadata" + ] + }, + "metadata": { + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.xml", + "type": "application/xml", + "title": "CMR XML metadata for C3397061771-ORNL_CLOUD", + "roles": [ + "metadata" + ] + } + } +} \ No newline at end of file diff --git a/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json b/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json index 9b98183695..049df0c8cb 100644 --- a/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json +++ b/datasets/GPM_3GPROFNOAA18MHS_CLIM_07.json @@ -149,6 +149,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_GPM_L3_GPM_3GPROFNOAA18MHS_CLIM_07_": { + "href": "s3://gesdisc-cumulus-prod-protected/GPM_L3/GPM_3GPROFNOAA18MHS_CLIM.07/", + "title": "gesdisc_cumulus_prod_protected_GPM_L3_GPM_3GPROFNOAA18MHS_CLIM_07_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C2264135951-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/MASTER_GEMx_Summer_2023_2319_1.json b/datasets/MASTER_GEMx_Summer_2023_2319_1.json index 6091cb5323..118c4e287d 100644 --- a/datasets/MASTER_GEMx_Summer_2023_2319_1.json +++ b/datasets/MASTER_GEMx_Summer_2023_2319_1.json @@ -129,7 +129,7 @@ ] }, "ornl": { - "href": "https://daac.ornl.gov/daacdata/master/MASTER_GEMx_Summer_2023/", + "href": "https://daac.ornl.gov/daacdata/master/MASTER_GEMx_Summer_2023/data/", "title": "Direct Download", "description": "This link allows direct data access via Earthdata login", "roles": [ diff --git a/datasets/MI1AC_2.json b/datasets/MI1AC_2.json index b44d003038..0791c5644c 100644 --- a/datasets/MI1AC_2.json +++ b/datasets/MI1AC_2.json @@ -126,6 +126,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI1AC_2", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C179031451-LARC.xml", "type": "application/xml", diff --git a/datasets/MI1AOBC_2.json b/datasets/MI1AOBC_2.json index 1d8339fb0f..e0d833225c 100644 --- a/datasets/MI1AOBC_2.json +++ b/datasets/MI1AOBC_2.json @@ -125,6 +125,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI1AOBC_2", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C179031458-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DAENF_002.json b/datasets/MI3DAENF_002.json index 43e5645347..eed1937507 100644 --- a/datasets/MI3DAENF_002.json +++ b/datasets/MI3DAENF_002.json @@ -129,6 +129,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DAENF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141688-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DALF_002.json b/datasets/MI3DALF_002.json index 4daa595d94..861d612a67 100644 --- a/datasets/MI3DALF_002.json +++ b/datasets/MI3DALF_002.json @@ -129,9 +129,8 @@ ] }, "provider_metadata": { - "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DALF_2", + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DALF_002", "title": "Provider Metadata", - "description": "Data set landing page for MI3DALF_2", "roles": [ "metadata" ] diff --git a/datasets/MI3DALNF_002.json b/datasets/MI3DALNF_002.json index b04bd1b107..517e83528c 100644 --- a/datasets/MI3DALNF_002.json +++ b/datasets/MI3DALNF_002.json @@ -128,6 +128,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DALNF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141686-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DCDF_002.json b/datasets/MI3DCDF_002.json index b58a40ad9f..79bf8ac885 100644 --- a/datasets/MI3DCDF_002.json +++ b/datasets/MI3DCDF_002.json @@ -129,6 +129,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DCDF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141685-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DCDNF_002.json b/datasets/MI3DCDNF_002.json index 268660bfb4..d7a9cfb247 100644 --- a/datasets/MI3DCDNF_002.json +++ b/datasets/MI3DCDNF_002.json @@ -126,6 +126,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DCDNF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141684-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DLSF_002.json b/datasets/MI3DLSF_002.json index 4ebb45b699..ff440ba614 100644 --- a/datasets/MI3DLSF_002.json +++ b/datasets/MI3DLSF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DLSF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141682-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DLSNF_002.json b/datasets/MI3DLSNF_002.json index 9623fd3b29..9aa866d200 100644 --- a/datasets/MI3DLSNF_002.json +++ b/datasets/MI3DLSNF_002.json @@ -139,6 +139,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DLSNF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141683-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3DRDF_002.json b/datasets/MI3DRDF_002.json index 43a5c90bdf..cb77a69d54 100644 --- a/datasets/MI3DRDF_002.json +++ b/datasets/MI3DRDF_002.json @@ -126,6 +126,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3DRDF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141692-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MAEF_002.json b/datasets/MI3MAEF_002.json index 11a54bcea6..d2a1969945 100644 --- a/datasets/MI3MAEF_002.json +++ b/datasets/MI3MAEF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MAEF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141689-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MAENF_002.json b/datasets/MI3MAENF_002.json index f0b5350e78..29443aa695 100644 --- a/datasets/MI3MAENF_002.json +++ b/datasets/MI3MAENF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MAENF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141690-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MALF_002.json b/datasets/MI3MALF_002.json index 6dd39d0ac0..422b5db2a3 100644 --- a/datasets/MI3MALF_002.json +++ b/datasets/MI3MALF_002.json @@ -128,6 +128,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MALF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141695-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MALNF_002.json b/datasets/MI3MALNF_002.json index 66e1ea573c..54baf04bb4 100644 --- a/datasets/MI3MALNF_002.json +++ b/datasets/MI3MALNF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MALNF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141694-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MCDF_002.json b/datasets/MI3MCDF_002.json index c19278e45b..a5cfdf362a 100644 --- a/datasets/MI3MCDF_002.json +++ b/datasets/MI3MCDF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MCDF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141693-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MCDNF_002.json b/datasets/MI3MCDNF_002.json index 0c11f75320..9455f39cd0 100644 --- a/datasets/MI3MCDNF_002.json +++ b/datasets/MI3MCDNF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MCDNF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141680-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MLSF_002.json b/datasets/MI3MLSF_002.json index c36665ecde..c229acdefa 100644 --- a/datasets/MI3MLSF_002.json +++ b/datasets/MI3MLSF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MLSF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141679-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MLSNF_2.json b/datasets/MI3MLSNF_2.json index 81b4a73ee4..493fb16697 100644 --- a/datasets/MI3MLSNF_2.json +++ b/datasets/MI3MLSNF_2.json @@ -138,6 +138,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MLSNF_2", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141677-LARC.xml", "type": "application/xml", diff --git a/datasets/MI3MRDF_002.json b/datasets/MI3MRDF_002.json index 9c832351e3..8943b49df8 100644 --- a/datasets/MI3MRDF_002.json +++ b/datasets/MI3MRDF_002.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MI3MRDF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C156141696-LARC.xml", "type": "application/xml", diff --git a/datasets/MIL1A_2.json b/datasets/MIL1A_2.json index 4066e5dc74..cb1f836310 100644 --- a/datasets/MIL1A_2.json +++ b/datasets/MIL1A_2.json @@ -130,7 +130,6 @@ "provider_metadata": { "href": "https://asdc.larc.nasa.gov/project/MISR/MIL1A_2", "title": "Provider Metadata", - "description": "Data set landing page for MIL1A_2", "roles": [ "metadata" ] diff --git a/datasets/MIL2ASAF_002.json b/datasets/MIL2ASAF_002.json index 9cb4b7889a..9d07b0e86e 100644 --- a/datasets/MIL2ASAF_002.json +++ b/datasets/MIL2ASAF_002.json @@ -130,9 +130,8 @@ ] }, "provider_metadata": { - "href": "https://asdc.larc.nasa.gov/project/MISR/MIL2ASAF_2", + "href": "https://asdc.larc.nasa.gov/project/MISR/MIL2ASAF_002", "title": "Provider Metadata", - "description": "Data set landing page for MIL2ASAF_2", "roles": [ "metadata" ] diff --git a/datasets/MIL2ASLF_002.json b/datasets/MIL2ASLF_002.json index 58ba6f4426..8b5fe852fc 100644 --- a/datasets/MIL2ASLF_002.json +++ b/datasets/MIL2ASLF_002.json @@ -134,6 +134,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MIL2ASLF_002", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C1542382559-LARC.xml", "type": "application/xml", diff --git a/datasets/MIL2TCAF_001.json b/datasets/MIL2TCAF_001.json index 7113b69e7b..1d53a55834 100644 --- a/datasets/MIL2TCAF_001.json +++ b/datasets/MIL2TCAF_001.json @@ -131,6 +131,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MIL2TCAF_001", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C135857533-LARC.xml", "type": "application/xml", diff --git a/datasets/MIL2TCCF_001.json b/datasets/MIL2TCCF_001.json index d974716d34..743e07ddef 100644 --- a/datasets/MIL2TCCF_001.json +++ b/datasets/MIL2TCCF_001.json @@ -126,6 +126,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MIL2TCCF_001", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C135857531-LARC.xml", "type": "application/xml", diff --git a/datasets/MIL2TCSF_001.json b/datasets/MIL2TCSF_001.json index 9c38ee3ad0..e89c868fd3 100644 --- a/datasets/MIL2TCSF_001.json +++ b/datasets/MIL2TCSF_001.json @@ -126,6 +126,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MIL2TCSF_001", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C135857534-LARC.xml", "type": "application/xml", diff --git a/datasets/MIRCCMF_001.json b/datasets/MIRCCMF_001.json index dcfb9fcdd0..884b57549b 100644 --- a/datasets/MIRCCMF_001.json +++ b/datasets/MIRCCMF_001.json @@ -124,6 +124,13 @@ "data" ] }, + "provider_metadata": { + "href": "https://asdc.larc.nasa.gov/project/MISR/MIRCCMF_001", + "title": "Provider Metadata", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C135857530-LARC.xml", "type": "application/xml", diff --git a/datasets/MultiInstrumentFusedXCO2_4.json b/datasets/MultiInstrumentFusedXCO2_4.json index d9a0b90c49..3c62d0590c 100644 --- a/datasets/MultiInstrumentFusedXCO2_4.json +++ b/datasets/MultiInstrumentFusedXCO2_4.json @@ -151,6 +151,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_CO2_MultiInstrumentFusedXCO2_4_": { + "href": "s3://gesdisc-cumulus-prod-protected/CO2/MultiInstrumentFusedXCO2.4/", + "title": "gesdisc_cumulus_prod_protected_CO2_MultiInstrumentFusedXCO2_4_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3278456754-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/OCO2GriddedXCO2_4.json b/datasets/OCO2GriddedXCO2_4.json index 1fe114b070..dfdf272c9f 100644 --- a/datasets/OCO2GriddedXCO2_4.json +++ b/datasets/OCO2GriddedXCO2_4.json @@ -149,6 +149,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_CO2_OCO2GriddedXCO2_4_": { + "href": "s3://gesdisc-cumulus-prod-protected/CO2/OCO2GriddedXCO2.4/", + "title": "gesdisc_cumulus_prod_protected_CO2_OCO2GriddedXCO2_4_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3278456736-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/OCO2GriddedXCO2_SIF_4.json b/datasets/OCO2GriddedXCO2_SIF_4.json index 9a5fd3eb36..8e9483b469 100644 --- a/datasets/OCO2GriddedXCO2_SIF_4.json +++ b/datasets/OCO2GriddedXCO2_SIF_4.json @@ -149,6 +149,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_CO2_OCO2GriddedXCO2_SIF_4_": { + "href": "s3://gesdisc-cumulus-prod-protected/CO2/OCO2GriddedXCO2_SIF.4/", + "title": "gesdisc_cumulus_prod_protected_CO2_OCO2GriddedXCO2_SIF_4_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3278456620-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/OCO3_L1aAE_11.json b/datasets/OCO3_L1aAE_11.json index 2abf26d161..032fa602cf 100644 --- a/datasets/OCO3_L1aAE_11.json +++ b/datasets/OCO3_L1aAE_11.json @@ -152,6 +152,27 @@ "metadata" ] }, + "s3_gesdisc_cumulus_prod_protected_OCO3_DATA_OCO3_L1aAE_11_": { + "href": "s3://gesdisc-cumulus-prod-protected/OCO3_DATA/OCO3_L1aAE.11/", + "title": "gesdisc_cumulus_prod_protected_OCO3_DATA_OCO3_L1aAE_11_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3277749779-GES_DISC.xml", "type": "application/xml", diff --git a/datasets/OCTS_L1_2.json b/datasets/OCTS_L1_2.json index 516e719207..737e9c1e8a 100644 --- a/datasets/OCTS_L1_2.json +++ b/datasets/OCTS_L1_2.json @@ -82,9 +82,9 @@ "license": "proprietary", "keywords": [ "Ocean Optics", - "Earth Science", + "Ocean Color", "Oceans", - "Ocean Color" + "Earth Science" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L2_IOP_2022.0.json b/datasets/OCTS_L2_IOP_2022.0.json index 679c27715b..028f5f8f46 100644 --- a/datasets/OCTS_L2_IOP_2022.0.json +++ b/datasets/OCTS_L2_IOP_2022.0.json @@ -81,18 +81,18 @@ }, "license": "proprietary", "keywords": [ + "Reflectance", "Ocean Optics", "Earth Science", - "Reflectance", "Oceans", "Absorption", "Scattering", "Gelbstoff", - "Ecosystems", "Biosphere", + "Plankton", "Phytoplankton", "Aquatic Ecosystems", - "Plankton" + "Ecosystems" ], "providers": [ { @@ -125,9 +125,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L2_OC_2022.0.json b/datasets/OCTS_L2_OC_2022.0.json index a38a213ba6..02eed9491d 100644 --- a/datasets/OCTS_L2_OC_2022.0.json +++ b/datasets/OCTS_L2_OC_2022.0.json @@ -81,20 +81,20 @@ }, "license": "proprietary", "keywords": [ - "Ocean Color", - "Ocean Optics", "Earth Science", + "Ocean Optics", + "Ocean Color", "Oceans", "Reflectance", - "Aerosol Optical Depth/Thickness", - "Aerosols", "Atmosphere", + "Aerosols", + "Aerosol Optical Depth/Thickness", "Angstrom Exponent", - "Chlorophyll", "Chlorophyll Concentration", + "Chlorophyll", "Attenuation/Transmission", - "Ocean Chemistry", "Organic Carbon", + "Ocean Chemistry", "Fluorescence", "Photosynthetically Active Radiation" ], @@ -129,9 +129,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_CHL_2022.0.json b/datasets/OCTS_L3b_CHL_2022.0.json index 8efc451fb4..d53aec7dd1 100644 --- a/datasets/OCTS_L3b_CHL_2022.0.json +++ b/datasets/OCTS_L3b_CHL_2022.0.json @@ -81,10 +81,10 @@ }, "license": "proprietary", "keywords": [ - "Chlorophyll", + "Oceans", "Ocean Optics", - "Earth Science", - "Oceans" + "Chlorophyll", + "Earth Science" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_IOP_2022.0.json b/datasets/OCTS_L3b_IOP_2022.0.json index 5c21a0225a..10fdef3235 100644 --- a/datasets/OCTS_L3b_IOP_2022.0.json +++ b/datasets/OCTS_L3b_IOP_2022.0.json @@ -81,18 +81,18 @@ }, "license": "proprietary", "keywords": [ - "Reflectance", "Earth Science", - "Oceans", + "Reflectance", "Ocean Optics", + "Oceans", "Absorption", "Scattering", "Gelbstoff", - "Phytoplankton", "Plankton", "Ecosystems", - "Biosphere", - "Aquatic Ecosystems" + "Aquatic Ecosystems", + "Phytoplankton", + "Biosphere" ], "providers": [ { @@ -125,9 +125,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_KD_2022.0.json b/datasets/OCTS_L3b_KD_2022.0.json index b886c43d12..f1a7143d6f 100644 --- a/datasets/OCTS_L3b_KD_2022.0.json +++ b/datasets/OCTS_L3b_KD_2022.0.json @@ -81,10 +81,10 @@ }, "license": "proprietary", "keywords": [ - "Oceans", + "Ocean Optics", "Attenuation/Transmission", - "Earth Science", - "Ocean Optics" + "Oceans", + "Earth Science" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_PAR_2022.0.json b/datasets/OCTS_L3b_PAR_2022.0.json index efb6a05d02..f334d94e9a 100644 --- a/datasets/OCTS_L3b_PAR_2022.0.json +++ b/datasets/OCTS_L3b_PAR_2022.0.json @@ -81,10 +81,10 @@ }, "license": "proprietary", "keywords": [ - "Ocean Optics", + "Oceans", "Photosynthetically Active Radiation", "Earth Science", - "Oceans" + "Ocean Optics" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_PIC_2022.0.json b/datasets/OCTS_L3b_PIC_2022.0.json index fb072f4485..0fb8864fe1 100644 --- a/datasets/OCTS_L3b_PIC_2022.0.json +++ b/datasets/OCTS_L3b_PIC_2022.0.json @@ -81,10 +81,10 @@ }, "license": "proprietary", "keywords": [ - "Earth Science", "Oceans", + "Inorganic Carbon", "Ocean Chemistry", - "Inorganic Carbon" + "Earth Science" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_POC_2022.0.json b/datasets/OCTS_L3b_POC_2022.0.json index 02ca0d0e44..7963fdf052 100644 --- a/datasets/OCTS_L3b_POC_2022.0.json +++ b/datasets/OCTS_L3b_POC_2022.0.json @@ -81,9 +81,9 @@ }, "license": "proprietary", "keywords": [ - "Earth Science", "Organic Carbon", "Oceans", + "Earth Science", "Ocean Chemistry" ], "providers": [ @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3b_RRS_2022.0.json b/datasets/OCTS_L3b_RRS_2022.0.json index 6ab8823c3a..1fda15a337 100644 --- a/datasets/OCTS_L3b_RRS_2022.0.json +++ b/datasets/OCTS_L3b_RRS_2022.0.json @@ -82,12 +82,12 @@ "license": "proprietary", "keywords": [ "Reflectance", - "Oceans", "Ocean Optics", "Earth Science", - "Aerosol Optical Depth/Thickness", + "Oceans", "Atmosphere", "Aerosols", + "Aerosol Optical Depth/Thickness", "Angstrom Exponent" ], "providers": [ @@ -121,9 +121,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_CHL_2022.0.json b/datasets/OCTS_L3m_CHL_2022.0.json index 9e6404e695..a9bfcf6b38 100644 --- a/datasets/OCTS_L3m_CHL_2022.0.json +++ b/datasets/OCTS_L3m_CHL_2022.0.json @@ -81,9 +81,9 @@ }, "license": "proprietary", "keywords": [ - "Ocean Optics", "Earth Science", "Chlorophyll", + "Ocean Optics", "Oceans" ], "providers": [ @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_IOP_2022.0.json b/datasets/OCTS_L3m_IOP_2022.0.json index 49f5996e78..b50511aebb 100644 --- a/datasets/OCTS_L3m_IOP_2022.0.json +++ b/datasets/OCTS_L3m_IOP_2022.0.json @@ -81,18 +81,18 @@ }, "license": "proprietary", "keywords": [ - "Reflectance", + "Ocean Optics", "Oceans", "Earth Science", - "Ocean Optics", + "Reflectance", "Absorption", "Scattering", "Gelbstoff", - "Biosphere", - "Aquatic Ecosystems", - "Phytoplankton", + "Ecosystems", "Plankton", - "Ecosystems" + "Phytoplankton", + "Biosphere", + "Aquatic Ecosystems" ], "providers": [ { @@ -125,9 +125,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_KD_2022.0.json b/datasets/OCTS_L3m_KD_2022.0.json index 6405c5d52c..5c814bc786 100644 --- a/datasets/OCTS_L3m_KD_2022.0.json +++ b/datasets/OCTS_L3m_KD_2022.0.json @@ -82,9 +82,9 @@ "license": "proprietary", "keywords": [ "Earth Science", - "Oceans", + "Attenuation/Transmission", "Ocean Optics", - "Attenuation/Transmission" + "Oceans" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_PAR_2022.0.json b/datasets/OCTS_L3m_PAR_2022.0.json index 11f61fa54d..b07d094213 100644 --- a/datasets/OCTS_L3m_PAR_2022.0.json +++ b/datasets/OCTS_L3m_PAR_2022.0.json @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_PIC_2022.0.json b/datasets/OCTS_L3m_PIC_2022.0.json index bd603233a5..8e0096862c 100644 --- a/datasets/OCTS_L3m_PIC_2022.0.json +++ b/datasets/OCTS_L3m_PIC_2022.0.json @@ -83,8 +83,8 @@ "keywords": [ "Earth Science", "Inorganic Carbon", - "Ocean Chemistry", - "Oceans" + "Oceans", + "Ocean Chemistry" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_POC_2022.0.json b/datasets/OCTS_L3m_POC_2022.0.json index e24ada9d19..80f1201962 100644 --- a/datasets/OCTS_L3m_POC_2022.0.json +++ b/datasets/OCTS_L3m_POC_2022.0.json @@ -81,10 +81,10 @@ }, "license": "proprietary", "keywords": [ - "Oceans", "Organic Carbon", "Ocean Chemistry", - "Earth Science" + "Earth Science", + "Oceans" ], "providers": [ { @@ -117,9 +117,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OCTS_L3m_RRS_2022.0.json b/datasets/OCTS_L3m_RRS_2022.0.json index 285013a043..34106439e6 100644 --- a/datasets/OCTS_L3m_RRS_2022.0.json +++ b/datasets/OCTS_L3m_RRS_2022.0.json @@ -81,13 +81,13 @@ }, "license": "proprietary", "keywords": [ - "Reflectance", "Ocean Optics", "Oceans", "Earth Science", + "Reflectance", + "Aerosol Optical Depth/Thickness", "Atmosphere", "Aerosols", - "Aerosol Optical Depth/Thickness", "Angstrom Exponent" ], "providers": [ @@ -121,9 +121,9 @@ "data" ] }, - "s3_ob_cumulus_public_": { - "href": "s3://ob-cumulus-public/", - "title": "ob_cumulus_public_", + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", "roles": [ "data" ] diff --git a/datasets/OMTO3G_004.json b/datasets/OMTO3G_004.json index c374f6531c..cc4074604c 100644 --- a/datasets/OMTO3G_004.json +++ b/datasets/OMTO3G_004.json @@ -2,7 +2,7 @@ "type": "Collection", "id": "OMTO3G_004", "stac_version": "1.1.0", - "description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags.\n\nThe OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.", + "description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.\n\nThe OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags.\n\nThe OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.", "links": [ { "rel": "license", diff --git a/datasets/OMTO3_004.json b/datasets/OMTO3_004.json index ff83b847d7..b6a026fb0f 100644 --- a/datasets/OMTO3_004.json +++ b/datasets/OMTO3_004.json @@ -2,7 +2,7 @@ "type": "Collection", "id": "OMTO3_004", "stac_version": "1.1.0", - "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia.\n\nThe OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.", + "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm for this product was originally developed by a team led by Dr. Pawan K. Bhartia at NASA Goddard Space Flight Center. The current product lead is Dr. Can Li.\n\nThe OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.", "links": [ { "rel": "license", diff --git a/datasets/SPL1BTB_NRT_105.json b/datasets/SPL1BTB_NRT_105.json index c4dc391086..d08738ac87 100644 --- a/datasets/SPL1BTB_NRT_105.json +++ b/datasets/SPL1BTB_NRT_105.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2025-01-09T00:00:00Z", + "2025-01-10T00:00:00Z", null ] ] diff --git a/datasets/SPL2SMP_NRT_107.json b/datasets/SPL2SMP_NRT_107.json index 0514078297..4ecdcf96fa 100644 --- a/datasets/SPL2SMP_NRT_107.json +++ b/datasets/SPL2SMP_NRT_107.json @@ -73,7 +73,7 @@ "temporal": { "interval": [ [ - "2025-01-09T00:00:00Z", + "2025-01-10T00:00:00Z", null ] ] diff --git a/datasets/SeaWiFS_L1_GAC_2.json b/datasets/SeaWiFS_L1_GAC_2.json index d84b6ad10d..6f4a63cc7d 100644 --- a/datasets/SeaWiFS_L1_GAC_2.json +++ b/datasets/SeaWiFS_L1_GAC_2.json @@ -81,9 +81,9 @@ }, "license": "proprietary", "keywords": [ + "Earth Science", "Ocean Color", "Ocean Optics", - "Earth Science", "Oceans" ], "providers": [ diff --git a/datasets/SeaWiFS_L1_MLAC_2.json b/datasets/SeaWiFS_L1_MLAC_2.json index d1b5241966..03d7e634d9 100644 --- a/datasets/SeaWiFS_L1_MLAC_2.json +++ b/datasets/SeaWiFS_L1_MLAC_2.json @@ -81,9 +81,9 @@ }, "license": "proprietary", "keywords": [ + "Earth Science", "Oceans", "Ocean Optics", - "Earth Science", "Ocean Color" ], "providers": [ diff --git a/datasets/VIIRSJ1_L2_IOP_2022.0.json b/datasets/VIIRSJ1_L2_IOP_2022.0.json new file mode 100644 index 0000000000..e4f3972fba --- /dev/null +++ b/datasets/VIIRSJ1_L2_IOP_2022.0.json @@ -0,0 +1,158 @@ +{ + "type": "Collection", + "id": "VIIRSJ1_L2_IOP_2022.0", + "stac_version": "1.1.0", + "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", + "links": [ + { + "rel": "license", + "href": "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy", + "type": "text/html", + "title": "EOSDIS Data Use Policy" + }, + { + "rel": "about", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.html", + "type": "text/html", + "title": "HTML metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.native", + "type": "application/xml", + "title": "Native metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.echo10", + "type": "application/echo10+xml", + "title": "ECHO10 metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.json", + "type": "application/json", + "title": "CMR JSON metadata for collection" + }, + { + "rel": "via", + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.umm_json", + "type": "application/vnd.nasa.cmr.umm+json", + "title": "CMR UMM_JSON metadata for collection" + }, + { + "rel": "self", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ1_L2_IOP_2022.0", + "type": "application/json" + }, + { + "rel": "root", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD", + "type": "application/json", + "title": "OB_CLOUD STAC Catalog" + }, + { + "rel": "items", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ1_L2_IOP_2022.0/items", + "type": "application/geo+json", + "title": "Collection Items" + } + ], + "title": "NOAA-20 VIIRS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0", + "extent": { + "spatial": { + "bbox": [ + [ + -180, + -90, + 180, + 90 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2017-11-29T00:00:00Z", + null + ] + ] + } + }, + "license": "proprietary", + "keywords": [ + "Ocean Optics", + "Oceans", + "Reflectance", + "Earth Science", + "Absorption", + "Scattering", + "Gelbstoff", + "Aquatic Ecosystems", + "Phytoplankton", + "Ecosystems", + "Plankton", + "Biosphere" + ], + "providers": [ + { + "name": "OB_CLOUD", + "roles": [ + "producer" + ] + }, + { + "name": "NASA EOSDIS", + "roles": [ + "host" + ] + } + ], + "summaries": { + "platform": [ + "NOAA-20" + ], + "instruments": [ + "VIIRS" + ] + }, + "assets": { + "nasa": { + "href": "https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/NOAA20-VIIRS/", + "title": "Direct Download", + "description": "NASA Ocean Color Web - Data Distribution Site", + "roles": [ + "data" + ] + }, + "s3_ob_cumulus_prod_public_": { + "href": "s3://ob-cumulus-prod-public/", + "title": "ob_cumulus_prod_public_", + "roles": [ + "data" + ] + }, + "s3_credentials": { + "href": "https://obdaac-tea.earthdatacloud.nasa.gov/s3credentials", + "title": "S3 credentials API endpoint", + "roles": [ + "metadata" + ] + }, + "s3_credentials_documentation": { + "href": "https://obdaac-tea.earthdatacloud.nasa.gov/s3credentialsREADME", + "title": "S3 credentials API endpoint documentation", + "roles": [ + "metadata" + ] + }, + "metadata": { + "href": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.xml", + "type": "application/xml", + "title": "CMR XML metadata for C3396928893-OB_CLOUD", + "roles": [ + "metadata" + ] + } + } +} \ No newline at end of file diff --git a/datasets/VIIRSJ1_L2_IOP_NRT_2022.0.json b/datasets/VIIRSJ1_L2_IOP_NRT_2022.0.json new file mode 100644 index 0000000000..8019cf2b82 --- /dev/null +++ b/datasets/VIIRSJ1_L2_IOP_NRT_2022.0.json @@ -0,0 +1,158 @@ +{ + "type": "Collection", + "id": "VIIRSJ1_L2_IOP_NRT_2022.0", + "stac_version": "1.1.0", + "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. 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"state_date": "2024-08-29", + "state_date": "2024-11-08", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json", @@ -40745,7 +40745,7 @@ "id": "ATL08QL_006", "title": "ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006", "catalog": "NSIDC_ECS STAC Catalog", - "state_date": "2024-08-29", + "state_date": "2024-11-07", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json", @@ -40784,7 +40784,7 @@ "id": "ATL09QL_006", "title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006", "catalog": "NSIDC_ECS STAC Catalog", - "state_date": "2024-08-29", + "state_date": "2024-11-08", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json", @@ -40823,7 +40823,7 @@ "id": "ATL10QL_006", "title": "ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006", "catalog": "NSIDC_ECS STAC Catalog", - "state_date": "2024-08-29", + "state_date": "2024-11-08", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json", @@ -40914,7 +40914,7 @@ "id": "ATL13QL_006", "title": "ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006", "catalog": "NSIDC_ECS STAC Catalog", - "state_date": "2024-08-30", + "state_date": "2024-11-07", "end_date": "", "bbox": "-180, -90, 180, 90", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json", @@ -73255,16 +73255,16 @@ "license": "proprietary" }, { - "id": "DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2", - "title": "Delta-X: AVIRIS-NG L2B BRDF-Adjusted Surface Reflectance, MRD, LA, 2021, V2", + "id": "DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3", + "title": "Delta-X: AVIRIS-NG BRDF-Adjusted Surface Reflectance, MRD, LA, 2021, V3", "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2021-03-27", "end_date": "2021-09-25", - "bbox": "-91.59, 29.05, -89.67, 29.85", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2707162636-ORNL_CLOUD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2707162636-ORNL_CLOUD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD/collections/DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2", - "description": "This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. 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For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise.", + "bbox": "-91.59, 29.05, -89.07, 30.23", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD/collections/DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3", + "description": "This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) from 2021-03-27 to 2021-04-06 and 2021-08-18 to 2021-09-25. Reflectance data are provided for each flight line. In addition, ten files of mosaicked flight lines, by time period and over four locations (labeled Terre, Atcha, TerreEast, and Bara), are included. Data are provided as binary ENVI image and header files. Only land pixels were corrected; mask files for the mosaic file coverage showing presence/absence of water and clouds are also included. For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise.", "license": "proprietary" }, { @@ -116391,7 +116391,7 @@ { "id": "KOPRI-KPDC-00000623_1", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2015-03-01", "end_date": "2016-02-01", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -116404,7 +116404,7 @@ { "id": "KOPRI-KPDC-00000623_1", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-03-01", "end_date": "2016-02-01", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -117483,7 +117483,7 @@ { "id": "KOPRI-KPDC-00000707_3", "title": "3D floorplan for CAD of Jang Bogo Station", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-01-01", "end_date": "2011-01-31", "bbox": "164.228817, -74.624017, 164.228817, -74.624017", @@ -117496,7 +117496,7 @@ { "id": "KOPRI-KPDC-00000707_3", "title": "3D floorplan for CAD of Jang Bogo Station", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2011-01-01", "end_date": "2011-01-31", "bbox": "164.228817, -74.624017, 164.228817, -74.624017", @@ -117704,7 +117704,7 @@ { "id": "KOPRI-KPDC-00000723_1", "title": "Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2014-10-08", "end_date": "2014-10-08", "bbox": "-58.766667, -62.216667, -58.766667, -62.216667", @@ -117717,7 +117717,7 @@ { "id": "KOPRI-KPDC-00000723_1", "title": "Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-10-08", "end_date": "2014-10-08", "bbox": "-58.766667, -62.216667, -58.766667, -62.216667", @@ -117730,7 +117730,7 @@ { "id": "KOPRI-KPDC-00000724_1", "title": "Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2014-10-08", "end_date": "2014-10-08", "bbox": "-58.766667, -62.216667, -58.766667, -62.216667", @@ -117743,7 +117743,7 @@ { "id": "KOPRI-KPDC-00000724_1", "title": "Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-10-08", "end_date": "2014-10-08", "bbox": "-58.766667, -62.216667, -58.766667, -62.216667", @@ -118224,7 +118224,7 @@ { "id": "KOPRI-KPDC-00000760_1", "title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-12-28", "end_date": "2017-02-15", "bbox": "153.936483, -75.389942, 159.216086, -75.059956", @@ -118237,7 +118237,7 @@ { "id": "KOPRI-KPDC-00000760_1", "title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2016-12-28", "end_date": "2017-02-15", "bbox": "153.936483, -75.389942, 159.216086, -75.059956", @@ -118380,7 +118380,7 @@ { "id": "KOPRI-KPDC-00000770_1", "title": "Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016.", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2010-01-01", "end_date": "2016-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -118393,7 +118393,7 @@ { "id": "KOPRI-KPDC-00000770_1", "title": "Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016.", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-01-01", "end_date": "2016-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -118458,7 +118458,7 @@ { "id": "KOPRI-KPDC-00000775_1", "title": "Aerosol Size Distribution from King Sejong Station collected in 2010-2016.", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-01-01", "end_date": "2016-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -118471,7 +118471,7 @@ { "id": "KOPRI-KPDC-00000775_1", "title": "Aerosol Size Distribution from King Sejong Station collected in 2010-2016.", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2010-01-01", "end_date": "2016-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -118692,7 +118692,7 @@ { "id": "KOPRI-KPDC-00000792_3", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2016", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-01-10", "end_date": "2017-02-02", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -118705,7 +118705,7 @@ { "id": "KOPRI-KPDC-00000792_3", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2016", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2016-01-10", "end_date": "2017-02-02", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -119108,7 +119108,7 @@ { "id": "KOPRI-KPDC-00000822_2", "title": "All-Sky airglow image, King Sejong Station, Antarctica, 2016", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2016-01-01", "end_date": "2016-10-01", "bbox": "-58.7766, -62.2206, -58.7766, -62.2206", @@ -119121,7 +119121,7 @@ { "id": "KOPRI-KPDC-00000822_2", "title": "All-Sky airglow image, King Sejong Station, Antarctica, 2016", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-01-01", "end_date": "2016-10-01", "bbox": "-58.7766, -62.2206, -58.7766, -62.2206", @@ -119849,7 +119849,7 @@ { "id": "KOPRI-KPDC-00000879_1", "title": "Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-06-19", "end_date": "2017-06-18", "bbox": "-105.133333, 69.1, -105.133333, 69.1", @@ -119862,7 +119862,7 @@ { "id": "KOPRI-KPDC-00000879_1", "title": "Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2016-06-19", "end_date": "2017-06-18", "bbox": "-105.133333, 69.1, -105.133333, 69.1", @@ -120759,7 +120759,7 @@ { "id": "KOPRI-KPDC-00000947_1", "title": "Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2015-03-03", "end_date": "2016-02-15", "bbox": "164.233333, -74.616667, 164.233333, -74.616667", @@ -120772,7 +120772,7 @@ { "id": "KOPRI-KPDC-00000947_1", "title": "Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-03-03", "end_date": "2016-02-15", "bbox": "164.233333, -74.616667, 164.233333, -74.616667", @@ -121422,7 +121422,7 @@ { "id": "KOPRI-KPDC-00000999_2", "title": "2018 Multibeam bathymetry data in the Ross Sea, Antarctica", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-03-13", "end_date": "", "bbox": "164.4, -75.5, 165.9, -75.1", @@ -121435,7 +121435,7 @@ { "id": "KOPRI-KPDC-00000999_2", "title": "2018 Multibeam bathymetry data in the Ross Sea, Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-03-13", "end_date": "", "bbox": "164.4, -75.5, 165.9, -75.1", @@ -122813,7 +122813,7 @@ { "id": "KOPRI-KPDC-00001103_3", "title": "All-Sky airglow image, King Sejong Station, Antarctica, 2018", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-01-01", "end_date": "2018-10-01", "bbox": "-58.7766, -62.2206, -58.7766, -62.2206", @@ -122826,7 +122826,7 @@ { "id": "KOPRI-KPDC-00001103_3", "title": "All-Sky airglow image, King Sejong Station, Antarctica, 2018", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-01-01", "end_date": "2018-10-01", "bbox": "-58.7766, -62.2206, -58.7766, -62.2206", @@ -122956,7 +122956,7 @@ { "id": "KOPRI-KPDC-00001112_4", "title": "All-sky aurora (proton) image, Longyearbyen, Norway, 2018", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-01-01", "end_date": "2018-02-28", "bbox": "16.040746, 78.147909, 16.040746, 78.147909", @@ -122969,7 +122969,7 @@ { "id": "KOPRI-KPDC-00001112_4", "title": "All-sky aurora (proton) image, Longyearbyen, Norway, 2018", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-01-01", "end_date": "2018-02-28", "bbox": "16.040746, 78.147909, 16.040746, 78.147909", @@ -123125,7 +123125,7 @@ { "id": "KOPRI-KPDC-00001124_4", "title": "All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-03-01", "end_date": "2018-10-31", "bbox": "164.2273, -74.6202, 164.2273, -74.6202", @@ -123138,7 +123138,7 @@ { "id": "KOPRI-KPDC-00001124_4", "title": "All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-03-01", "end_date": "2018-10-31", "bbox": "164.2273, -74.6202, 164.2273, -74.6202", @@ -123203,7 +123203,7 @@ { "id": "KOPRI-KPDC-00001129_1", "title": "Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2017-06-19", "end_date": "2018-06-18", "bbox": "-105.133333, 69.1, -105.133333, 69.1", @@ -123216,7 +123216,7 @@ { "id": "KOPRI-KPDC-00001129_1", "title": "Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-06-19", "end_date": "2018-06-18", "bbox": "-105.133333, 69.1, -105.133333, 69.1", @@ -123827,7 +123827,7 @@ { "id": "KOPRI-KPDC-00001177_3", "title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-11-18", "end_date": "2019-01-14", "bbox": "154.838627, -75.536572, 155.93514, -75.246428", @@ -123840,7 +123840,7 @@ { "id": "KOPRI-KPDC-00001177_3", "title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-11-18", "end_date": "2019-01-14", "bbox": "154.838627, -75.536572, 155.93514, -75.246428", @@ -124347,7 +124347,7 @@ { "id": "KOPRI-KPDC-00001219_3", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2017-01-01", "end_date": "2017-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124360,7 +124360,7 @@ { "id": "KOPRI-KPDC-00001219_3", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-01-01", "end_date": "2017-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124373,7 +124373,7 @@ { "id": "KOPRI-KPDC-00001220_2", "title": "Aerosol Size Distribution from King Sejong Station collected in 2019.", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-01", "end_date": "2019-06-30", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124386,7 +124386,7 @@ { "id": "KOPRI-KPDC-00001220_2", "title": "Aerosol Size Distribution from King Sejong Station collected in 2019.", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-01-01", "end_date": "2019-06-30", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -125049,7 +125049,7 @@ { "id": "KOPRI-KPDC-00001275_3", "title": "All-sky airglow image, King Sejong Station, 2019", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-03-11", "end_date": "2019-09-30", "bbox": "-58.78804, -62.22268, -58.78804, -62.22268", @@ -125062,7 +125062,7 @@ { "id": "KOPRI-KPDC-00001275_3", "title": "All-sky airglow image, King Sejong Station, 2019", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-03-11", "end_date": "2019-09-30", "bbox": "-58.78804, -62.22268, -58.78804, -62.22268", @@ -126856,7 +126856,7 @@ { "id": "KOPRI-KPDC-00001412_1", "title": "Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2019", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-06-18", "end_date": "2019-06-30", "bbox": "-105.133333, 69.1, -105.133333, 69.1", @@ -126869,7 +126869,7 @@ { "id": "KOPRI-KPDC-00001412_1", "title": "Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2019", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-06-18", "end_date": "2019-06-30", "bbox": "-105.133333, 69.1, -105.133333, 69.1", @@ -126999,7 +126999,7 @@ { "id": "KOPRI-KPDC-00001423_2", "title": "2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores)", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-08-29", "end_date": "2019-09-20", "bbox": "167.676767, 73.69587, 179.98125, 77.132017", @@ -127012,7 +127012,7 @@ { "id": "KOPRI-KPDC-00001423_2", "title": "2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores)", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-08-29", "end_date": "2019-09-20", "bbox": "167.676767, 73.69587, 179.98125, 77.132017", @@ -128091,7 +128091,7 @@ { "id": "KOPRI-KPDC-00001508_4", "title": "All-sky aurora (proton) image, KHO Longyearbyen, 2020", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-10-19", "bbox": "16.12, 78.48, 16.12, 78.48", @@ -128104,7 +128104,7 @@ { "id": "KOPRI-KPDC-00001508_4", "title": "All-sky aurora (proton) image, KHO Longyearbyen, 2020", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-10-19", "bbox": "16.12, 78.48, 16.12, 78.48", @@ -128117,7 +128117,7 @@ { "id": "KOPRI-KPDC-00001509_1", "title": "2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-01-19", "end_date": "2020-01-26", "bbox": "-58.788436, -62.240056, -58.719694, -62.218583", @@ -128130,7 +128130,7 @@ { "id": "KOPRI-KPDC-00001509_1", "title": "2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-19", "end_date": "2020-01-26", "bbox": "-58.788436, -62.240056, -58.719694, -62.218583", @@ -129573,7 +129573,7 @@ { "id": "KOPRI-KPDC-00001632_1", "title": "A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2010-12-20", "end_date": "2011-01-20", "bbox": "-145, -74.6, -112, -72.5", @@ -129586,7 +129586,7 @@ { "id": "KOPRI-KPDC-00001632_1", "title": "A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-12-20", "end_date": "2011-01-20", "bbox": "-145, -74.6, -112, -72.5", @@ -130080,7 +130080,7 @@ { "id": "KOPRI-KPDC-00001671_3", "title": "2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station)", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-02-14", "end_date": "2019-02-15", "bbox": "163.984928, -74.73604, 164.57053, -74.610485", @@ -130093,7 +130093,7 @@ { "id": "KOPRI-KPDC-00001671_3", "title": "2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station)", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-02-14", "end_date": "2019-02-15", "bbox": "163.984928, -74.73604, 164.57053, -74.610485", @@ -131432,7 +131432,7 @@ { "id": "KOPRI-KPDC-00001778_2", "title": "2020/21 season Korean Route Traverse based GPS GIS data", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2020-12-01", "end_date": "2020-12-31", "bbox": "164.2362, -74.6281, 164.2362, -74.6281", @@ -131445,7 +131445,7 @@ { "id": "KOPRI-KPDC-00001778_2", "title": "2020/21 season Korean Route Traverse based GPS GIS data", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-12-01", "end_date": "2020-12-31", "bbox": "164.2362, -74.6281, 164.2362, -74.6281", @@ -131757,7 +131757,7 @@ { "id": "KOPRI-KPDC-00001804_2", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-10", "end_date": "2021-03-11", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -131770,7 +131770,7 @@ { "id": "KOPRI-KPDC-00001804_2", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2020-01-10", "end_date": "2021-03-11", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -131887,7 +131887,7 @@ { "id": "KOPRI-KPDC-00001817_2", "title": "All-sky airglow image, King Sejong Station, 2021", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-02-01", "end_date": "2021-08-31", "bbox": "-58.47, -62.13, -58.47, -62.13", @@ -131900,7 +131900,7 @@ { "id": "KOPRI-KPDC-00001817_2", "title": "All-sky airglow image, King Sejong Station, 2021", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2021-02-01", "end_date": "2021-08-31", "bbox": "-58.47, -62.13, -58.47, -62.13", @@ -132316,7 +132316,7 @@ { "id": "KOPRI-KPDC-00001851_2", "title": "All-sky aurora (electron) image, Jang Bogo Station, 2021", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2021-03-01", "end_date": "2021-09-30", "bbox": "164.2, -74.623333, 164.2, -74.623333", @@ -132329,7 +132329,7 @@ { "id": "KOPRI-KPDC-00001851_2", "title": "All-sky aurora (electron) image, Jang Bogo Station, 2021", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-03-01", "end_date": "2021-09-30", "bbox": "164.2, -74.623333, 164.2, -74.623333", @@ -132914,7 +132914,7 @@ { "id": "KOPRI-KPDC-00001905_1", "title": "2015 ARA06C-01JPC: Lipid biomarkers (HBIs, sterols) from core sediments", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2021-01-01", "end_date": "2021-12-31", "bbox": "-166.428882, 73.620361, -166.428882, 73.620361", @@ -132927,7 +132927,7 @@ { "id": "KOPRI-KPDC-00001905_1", "title": "2015 ARA06C-01JPC: Lipid biomarkers (HBIs, sterols) from core sediments", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-01-01", "end_date": "2021-12-31", "bbox": "-166.428882, 73.620361, -166.428882, 73.620361", @@ -133642,7 +133642,7 @@ { "id": "L1B_Wind_Products_3.0", "title": "Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers", - "catalog": "ALL STAC Catalog", + "catalog": "ESA STAC Catalog", "state_date": "2020-04-20", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133655,7 +133655,7 @@ { "id": "L1B_Wind_Products_3.0", "title": "Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers", - "catalog": "ESA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-04-20", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133694,7 +133694,7 @@ { "id": "L2C_Wind_products_5.0", "title": "Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing", - "catalog": "ALL STAC Catalog", + "catalog": "ESA STAC Catalog", "state_date": "2020-07-09", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133707,7 +133707,7 @@ { "id": "L2C_Wind_products_5.0", "title": "Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing", - "catalog": "ESA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-07-09", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -135475,7 +135475,7 @@ { "id": "LDEO_INDICES_INDIA", "title": "All-India Monsoon Rainfall Index at LDEO/IRI Climate Data Library", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1813-06-01", "end_date": "1998-09-30", "bbox": "70, -10, 90, 40", @@ -135488,7 +135488,7 @@ { "id": "LDEO_INDICES_INDIA", "title": "All-India Monsoon Rainfall Index at LDEO/IRI Climate Data Library", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1813-06-01", "end_date": "1998-09-30", "bbox": "70, -10, 90, 40", @@ -135566,7 +135566,7 @@ { "id": "LGB_10m_traverse_1", "title": "10 m firn temperature data: LGB traverses 1990-95", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1989-11-01", "end_date": "1995-02-28", "bbox": "54, -77, 78, -69", @@ -135579,7 +135579,7 @@ { "id": "LGB_10m_traverse_1", "title": "10 m firn temperature data: LGB traverses 1990-95", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1989-11-01", "end_date": "1995-02-28", "bbox": "54, -77, 78, -69", @@ -136762,7 +136762,7 @@ { "id": "LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001", "title": "Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2018-01-12", "end_date": "2019-03-01", "bbox": "123, -75, 123, -75", @@ -136775,7 +136775,7 @@ { "id": "LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001", "title": "Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-01-12", "end_date": "2019-03-01", "bbox": "123, -75, 123, -75", @@ -137282,7 +137282,7 @@ { "id": "Last_Day_Spring_Snow_1528_1", "title": "ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2000-04-01", "end_date": "2016-07-02", "bbox": "-175.76, 52.17, -97.95, 68.97", @@ -137295,7 +137295,7 @@ { "id": "Last_Day_Spring_Snow_1528_1", "title": "ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-04-01", "end_date": "2016-07-02", "bbox": "-175.76, 52.17, -97.95, 68.97", @@ -137308,7 +137308,7 @@ { "id": "Leaf_Carbon_Nutrients_1106_1", "title": "A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1970-01-01", "end_date": "2009-12-31", "bbox": "-159.7, -50, 176.9, 68.5", @@ -137321,7 +137321,7 @@ { "id": "Leaf_Carbon_Nutrients_1106_1", "title": "A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "2009-12-31", "bbox": "-159.7, -50, 176.9, 68.5", @@ -137334,7 +137334,7 @@ { "id": "Leaf_Photosynthesis_Traits_1224_1", "title": "A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1993-01-01", "end_date": "2010-12-31", "bbox": "-122.4, -43.2, 176.13, 58.42", @@ -137347,7 +137347,7 @@ { "id": "Leaf_Photosynthesis_Traits_1224_1", "title": "A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1993-01-01", "end_date": "2010-12-31", "bbox": "-122.4, -43.2, 176.13, 58.42", @@ -137438,7 +137438,7 @@ { "id": "Lidar_Bibliography_1", "title": "A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1961-01-01", "end_date": "", "bbox": "62, -68, 159, -65", @@ -137451,7 +137451,7 @@ { "id": "Lidar_Bibliography_1", "title": "A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1961-01-01", "end_date": "", "bbox": "62, -68, 159, -65", @@ -143171,7 +143171,7 @@ { "id": "MI2010_11_Alien-plant-survey_JDS_1", "title": "Alien plant survey Macquarie Island 2010_11", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-10-13", "end_date": "2011-01-31", "bbox": "158.8, -54.7, 158.9, -54.6", @@ -143184,7 +143184,7 @@ { "id": "MI2010_11_Alien-plant-survey_JDS_1", "title": "Alien plant survey Macquarie Island 2010_11", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2010-10-13", "end_date": "2011-01-31", "bbox": "158.8, -54.7, 158.9, -54.6", @@ -151335,7 +151335,7 @@ { "id": "MODIS_CCaN_NDVI_Trends_Alaska_1666_1", "title": "ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2000-01-01", "end_date": "2015-12-31", "bbox": "-166.85, 66.99, -140.98, 71.38", @@ -151348,7 +151348,7 @@ { "id": "MODIS_CCaN_NDVI_Trends_Alaska_1666_1", "title": "ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-01-01", "end_date": "2015-12-31", "bbox": "-166.85, 66.99, -140.98, 71.38", @@ -152609,7 +152609,7 @@ { "id": "MURI_Camouflage_0", "title": "A Multi University Research Initiative (MURI) Camouflage Project", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-06-14", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -152622,7 +152622,7 @@ { "id": "MURI_Camouflage_0", "title": "A Multi University Research Initiative (MURI) Camouflage Project", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "2010-06-14", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -154559,7 +154559,7 @@ { "id": "MassGIS_GISDATA.COQHMOSAICS_POLY", "title": "2001 MrSID Mosaics Index", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2002-08-01", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154572,7 +154572,7 @@ { "id": "MassGIS_GISDATA.COQHMOSAICS_POLY", "title": "2001 MrSID Mosaics Index", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-08-01", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154585,7 +154585,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICS2005_POLY", "title": "2005 MrSID Mosaics Index", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154598,7 +154598,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICS2005_POLY", "title": "2005 MrSID Mosaics Index", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154611,7 +154611,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICSCDS2005_POLY.", "title": "2005 MrSID Mosaics CD-ROM Index", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154624,7 +154624,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICSCDS2005_POLY.", "title": "2005 MrSID Mosaics CD-ROM Index", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154637,7 +154637,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY", "title": "2005 MrSID Mosaics DVD Index", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2007-02-01", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154650,7 +154650,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY", "title": "2005 MrSID Mosaics DVD Index", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-02-01", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -154845,7 +154845,7 @@ { "id": "McMurdo_Predator_Prey_Acoustics", "title": "Acoustic records near McMurdo Station, Antarctica, 2012 - 2015.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -154858,7 +154858,7 @@ { "id": "McMurdo_Predator_Prey_Acoustics", "title": "Acoustic records near McMurdo Station, Antarctica, 2012 - 2015.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -154975,7 +154975,7 @@ { "id": "Meteorology_Log_Commonwealth_Bay_1977_1978_1", "title": "A log of meteorological observations made at Commonwealth Bay between 1977 and 1978", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1977-01-01", "end_date": "1978-12-31", "bbox": "142.5, -67, 142.5, -67", @@ -154988,7 +154988,7 @@ { "id": "Meteorology_Log_Commonwealth_Bay_1977_1978_1", "title": "A log of meteorological observations made at Commonwealth Bay between 1977 and 1978", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1977-01-01", "end_date": "1978-12-31", "bbox": "142.5, -67, 142.5, -67", @@ -157211,7 +157211,7 @@ { "id": "NBId0006_101", "title": "African Meteorology (GIS Coverage of Precipitation and Winds)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157224,7 +157224,7 @@ { "id": "NBId0006_101", "title": "African Meteorology (GIS Coverage of Precipitation and Winds)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157237,7 +157237,7 @@ { "id": "NBId0007_101", "title": "Africa Administrative Units (GIS Coverage of Administrative Boundaries)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157250,7 +157250,7 @@ { "id": "NBId0007_101", "title": "Africa Administrative Units (GIS Coverage of Administrative Boundaries)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157302,7 +157302,7 @@ { "id": "NBId0018_101", "title": "Africa FAO Major Infrastructure and Human Settlements (GIS Coverage)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157315,7 +157315,7 @@ { "id": "NBId0018_101", "title": "Africa FAO Major Infrastructure and Human Settlements (GIS Coverage)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157380,7 +157380,7 @@ { "id": "NBId0023_101", "title": "Africa Holdridge Life Zone Classification (Vegetation and Climate)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "16, -35, 55, 40", @@ -157393,7 +157393,7 @@ { "id": "NBId0023_101", "title": "Africa Holdridge Life Zone Classification (Vegetation and Climate)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "16, -35, 55, 40", @@ -157406,7 +157406,7 @@ { "id": "NBId0024_101", "title": "Africa Soil Classification by Wilson and Henderson-Sellers", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "12.88, 6.67, 24.97, 24.19", @@ -157419,7 +157419,7 @@ { "id": "NBId0024_101", "title": "Africa Soil Classification by Wilson and Henderson-Sellers", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "12.88, 6.67, 24.97, 24.19", @@ -157432,7 +157432,7 @@ { "id": "NBId0025_101", "title": "Africa Soil Classification by Zobler", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157445,7 +157445,7 @@ { "id": "NBId0025_101", "title": "Africa Soil Classification by Zobler", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157458,7 +157458,7 @@ { "id": "NBId0036_101", "title": "Africa Lakes and Rivers (World Data Bank II)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157471,7 +157471,7 @@ { "id": "NBId0036_101", "title": "Africa Lakes and Rivers (World Data Bank II)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157510,7 +157510,7 @@ { "id": "NBId0043_101", "title": "Africa Integrated Elevation and Bathymetry", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157523,7 +157523,7 @@ { "id": "NBId0043_101", "title": "Africa Integrated Elevation and Bathymetry", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157536,7 +157536,7 @@ { "id": "NBId0044_101", "title": "Africa Ocean Mask", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157549,7 +157549,7 @@ { "id": "NBId0044_101", "title": "Africa Ocean Mask", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -157809,7 +157809,7 @@ { "id": "NBId0203_101", "title": "Africa Water Balance high/lowland crops, 1987", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157822,7 +157822,7 @@ { "id": "NBId0203_101", "title": "Africa Water Balance high/lowland crops, 1987", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157848,7 +157848,7 @@ { "id": "NBId0208_101", "title": "Africa Major Human Settlements and Landuse, 1984", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157861,7 +157861,7 @@ { "id": "NBId0208_101", "title": "Africa Major Human Settlements and Landuse, 1984", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157874,7 +157874,7 @@ { "id": "NBId0211_101", "title": "Africa Irrigation Potential, Best soils, 1987", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157887,7 +157887,7 @@ { "id": "NBId0211_101", "title": "Africa Irrigation Potential, Best soils, 1987", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157926,7 +157926,7 @@ { "id": "NBId0218_101", "title": "Africa Surface Hydrography, 1984", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157939,7 +157939,7 @@ { "id": "NBId0218_101", "title": "Africa Surface Hydrography, 1984", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157978,7 +157978,7 @@ { "id": "NBId0223_101", "title": "Africa Zobler Soils (Texture Classes, Slope, Phases), 1987", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157991,7 +157991,7 @@ { "id": "NBId0223_101", "title": "Africa Zobler Soils (Texture Classes, Slope, Phases), 1987", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -158004,7 +158004,7 @@ { "id": "NBId0233_101", "title": "Africa Population Density Model (Land Degradation Project), 1992", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -158017,7 +158017,7 @@ { "id": "NBId0233_101", "title": "Africa Population Density Model (Land Degradation Project), 1992", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -158108,7 +158108,7 @@ { "id": "NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1", "title": "2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-12-17", "end_date": "2005-11-30", "bbox": "-179.488, -77.642, -166.989, -49.014", @@ -158121,7 +158121,7 @@ { "id": "NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1", "title": "2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-12-17", "end_date": "2005-11-30", "bbox": "-179.488, -77.642, -166.989, -49.014", @@ -158160,7 +158160,7 @@ { "id": "NCAR_DS474.0", "title": "AARI Russian North Polar Drifting Station Data, from NSIDC", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1937-05-01", "end_date": "1991-03-31", "bbox": "-180, -90, 180, 90", @@ -158173,7 +158173,7 @@ { "id": "NCAR_DS474.0", "title": "AARI Russian North Polar Drifting Station Data, from NSIDC", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1937-05-01", "end_date": "1991-03-31", "bbox": "-180, -90, 180, 90", @@ -158186,7 +158186,7 @@ { "id": "NCAR_DS510.5", "title": "A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1890-01-01", "end_date": "2007-05-31", "bbox": "-180, -90, 180, 90", @@ -158199,7 +158199,7 @@ { "id": "NCAR_DS510.5", "title": "A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1890-01-01", "end_date": "2007-05-31", "bbox": "-180, -90, 180, 90", @@ -158264,7 +158264,7 @@ { "id": "NCEI DSI 1167_01_Not Applicable", "title": "Active Marine Station Metadata", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2012-05-18", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -158277,7 +158277,7 @@ { "id": "NCEI DSI 1167_01_Not Applicable", "title": "Active Marine Station Metadata", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-05-18", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -158706,7 +158706,7 @@ { "id": "NCEI WebARTIS: WBAN31_Not Applicable", "title": "Adiabatic Charts", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1929-01-01", "end_date": "1995-06-30", "bbox": "-180, -90, 180, 90", @@ -158719,7 +158719,7 @@ { "id": "NCEI WebARTIS: WBAN31_Not Applicable", "title": "Adiabatic Charts", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1929-01-01", "end_date": "1995-06-30", "bbox": "-180, -90, 180, 90", @@ -159421,7 +159421,7 @@ { "id": "NESP_2015_SRW", "title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-02-09", "end_date": "2015-07-09", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159434,7 +159434,7 @@ { "id": "NESP_2015_SRW", "title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2015-02-09", "end_date": "2015-07-09", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159447,7 +159447,7 @@ { "id": "NESP_2015_SRW_3", "title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-02-09", "end_date": "2015-07-09", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159460,7 +159460,7 @@ { "id": "NESP_2015_SRW_3", "title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2015-02-09", "end_date": "2015-07-09", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159473,7 +159473,7 @@ { "id": "NESP_2016_SRW_3", "title": "2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2016-08-24", "end_date": "2016-08-29", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159486,7 +159486,7 @@ { "id": "NESP_2016_SRW_3", "title": "2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-08-24", "end_date": "2016-08-29", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159499,7 +159499,7 @@ { "id": "NESP_2017_SRW_1", "title": "2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-08-23", "end_date": "2017-08-27", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159512,7 +159512,7 @@ { "id": "NESP_2017_SRW_1", "title": "2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2017-08-23", "end_date": "2017-08-27", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159525,7 +159525,7 @@ { "id": "NESP_2018_SRW_1", "title": "2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-08-18", "end_date": "2018-08-23", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159538,7 +159538,7 @@ { "id": "NESP_2018_SRW_1", "title": "2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2018-08-18", "end_date": "2018-08-23", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159551,7 +159551,7 @@ { "id": "NESP_2019_SRW_1", "title": "2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-08-18", "end_date": "2019-08-24", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159564,7 +159564,7 @@ { "id": "NESP_2019_SRW_1", "title": "2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2019-08-18", "end_date": "2019-08-24", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -159655,7 +159655,7 @@ { "id": "NGA178\n _1.0", "title": "Advanced Terrestrial Simulator", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -159668,7 +159668,7 @@ { "id": "NGA178\n _1.0", "title": "Advanced Terrestrial Simulator", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -159824,7 +159824,7 @@ { "id": "NIPR-GEO-1", "title": "Airborne Magnetic Survey Data in Antarctica by JARE", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "", "bbox": "20, -72, 60, -68", @@ -159837,7 +159837,7 @@ { "id": "NIPR-GEO-1", "title": "Airborne Magnetic Survey Data in Antarctica by JARE", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1980-01-01", "end_date": "", "bbox": "20, -72, 60, -68", @@ -159876,7 +159876,7 @@ { "id": "NIPR_PMG_AIR_ARCHIVE_ANT", "title": "Air samples for archive", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-02-01", "end_date": "2009-01-31", "bbox": "39.5, -69, 39.5, -69", @@ -159889,7 +159889,7 @@ { "id": "NIPR_PMG_AIR_ARCHIVE_ANT", "title": "Air samples for archive", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1995-02-01", "end_date": "2009-01-31", "bbox": "39.5, -69, 39.5, -69", @@ -161579,7 +161579,7 @@ { "id": "NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002", "title": "Alien Plants Ranking System (APRS) Implementation Team", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-115, 30, -85, 45", @@ -161592,7 +161592,7 @@ { "id": "NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002", "title": "Alien Plants Ranking System (APRS) Implementation Team", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-115, 30, -85, 45", @@ -162125,7 +162125,7 @@ { "id": "NSF-ANT-1142074-penguins_1.0", "title": "Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-12-15", "end_date": "2013-01-31", "bbox": "165.9, -77.6, 169.4, -76.9", @@ -162138,7 +162138,7 @@ { "id": "NSF-ANT-1142074-penguins_1.0", "title": "Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-12-15", "end_date": "2013-01-31", "bbox": "165.9, -77.6, 169.4, -76.9", @@ -162177,7 +162177,7 @@ { "id": "NSF-ANT04-39906_1", "title": "Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2005-09-15", "end_date": "2009-08-31", "bbox": "162, -78, 168, -72", @@ -162190,7 +162190,7 @@ { "id": "NSF-ANT04-39906_1", "title": "Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-09-15", "end_date": "2009-08-31", "bbox": "162, -78, 168, -72", @@ -162216,7 +162216,7 @@ { "id": "NSF-ANT05-37371", "title": "A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-10-01", "end_date": "2013-09-30", "bbox": "40, -84, 140, -76", @@ -162229,7 +162229,7 @@ { "id": "NSF-ANT05-37371", "title": "A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2007-10-01", "end_date": "2013-09-30", "bbox": "40, -84, 140, -76", @@ -162294,7 +162294,7 @@ { "id": "NSF-ANT06-36928", "title": "A VLF Beacon Transmitter at South Pole", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2007-09-15", "end_date": "2011-08-31", "bbox": "-180, -90, 180, -90", @@ -162307,7 +162307,7 @@ { "id": "NSF-ANT06-36928", "title": "A VLF Beacon Transmitter at South Pole", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-09-15", "end_date": "2011-08-31", "bbox": "-180, -90, 180, -90", @@ -162580,7 +162580,7 @@ { "id": "NSF-ANT10-43517", "title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-07-01", "end_date": "2015-06-30", "bbox": "163.5, -78.32, 165.35, -77.57", @@ -162593,7 +162593,7 @@ { "id": "NSF-ANT10-43517", "title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-07-01", "end_date": "2015-06-30", "bbox": "163.5, -78.32, 165.35, -77.57", @@ -162632,7 +162632,7 @@ { "id": "NSF-ANT10-43621", "title": "A Comparison of Conjugate Auroral Electojet Indices", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-06-01", "end_date": "2013-05-31", "bbox": "-180, -79.5, 180, -54.5", @@ -162645,7 +162645,7 @@ { "id": "NSF-ANT10-43621", "title": "A Comparison of Conjugate Auroral Electojet Indices", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-06-01", "end_date": "2013-05-31", "bbox": "-180, -79.5, 180, -54.5", @@ -162697,7 +162697,7 @@ { "id": "NSF-ANT11-42018_1", "title": "Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-05-15", "end_date": "2015-04-30", "bbox": "-75.8, -67.12, -62.37, -61.08", @@ -162710,7 +162710,7 @@ { "id": "NSF-ANT11-42018_1", "title": "Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-05-15", "end_date": "2015-04-30", "bbox": "-75.8, -67.12, -62.37, -61.08", @@ -162736,7 +162736,7 @@ { "id": "NSF-ANT12-41487", "title": "A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2012-06-01", "end_date": "2013-05-31", "bbox": "-180, -90, 180, 90", @@ -162749,7 +162749,7 @@ { "id": "NSF-ANT12-41487", "title": "A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-01", "end_date": "2013-05-31", "bbox": "-180, -90, 180, 90", @@ -163594,7 +163594,7 @@ { "id": "NSIDC-0212_1", "title": "Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1", - "catalog": "NSIDCV0 STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-01-14", "end_date": "2003-02-03", "bbox": "130, 30, 150, 40", @@ -163607,7 +163607,7 @@ { "id": "NSIDC-0212_1", "title": "Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1", - "catalog": "ALL STAC Catalog", + "catalog": "NSIDCV0 STAC Catalog", "state_date": "2003-01-14", "end_date": "2003-02-03", "bbox": "130, 30, 150, 40", @@ -163906,7 +163906,7 @@ { "id": "NSIDC-0326_1", "title": "Ablation Rates of Taylor Glacier, Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-11-19", "end_date": "2011-01-12", "bbox": "160.1, -77.9, 162.2, -77.6", @@ -163919,7 +163919,7 @@ { "id": "NSIDC-0326_1", "title": "Ablation Rates of Taylor Glacier, Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2002-11-19", "end_date": "2011-01-12", "bbox": "160.1, -77.9, 162.2, -77.6", @@ -163932,7 +163932,7 @@ { "id": "NSIDC-0334_1", "title": "Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-12-10", "end_date": "2005-01-29", "bbox": "-130, -80, -95, -75", @@ -163945,7 +163945,7 @@ { "id": "NSIDC-0334_1", "title": "Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2004-12-10", "end_date": "2005-01-29", "bbox": "-130, -80, -95, -75", @@ -164296,7 +164296,7 @@ { "id": "NSIDC-0504_1", "title": "Alkanes in Firn Air Samples, Antarctica and Greenland", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-12-01", "end_date": "2009-01-31", "bbox": "-38.3833, -79.47, 112.09, 72.5833", @@ -164309,7 +164309,7 @@ { "id": "NSIDC-0504_1", "title": "Alkanes in Firn Air Samples, Antarctica and Greenland", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2005-12-01", "end_date": "2009-01-31", "bbox": "-38.3833, -79.47, 112.09, 72.5833", @@ -164348,7 +164348,7 @@ { "id": "NSIDC-0517_1", "title": "AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-12-10", "end_date": "2005-01-29", "bbox": "-125, -83, -90, -73", @@ -164361,7 +164361,7 @@ { "id": "NSIDC-0517_1", "title": "AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2004-12-10", "end_date": "2005-01-29", "bbox": "-125, -83, -90, -73", @@ -164491,7 +164491,7 @@ { "id": "NSIDC-0539_1", "title": "Abrupt Change in Atmospheric CO2 During the Last Ice Age", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-01-01", "end_date": "2012-12-31", "bbox": "-148.82, -81.66, -119.83, -80.01", @@ -164504,7 +164504,7 @@ { "id": "NSIDC-0539_1", "title": "Abrupt Change in Atmospheric CO2 During the Last Ice Age", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2009-01-01", "end_date": "2012-12-31", "bbox": "-148.82, -81.66, -119.83, -80.01", @@ -164712,7 +164712,7 @@ { "id": "NSIDC-0634_1", "title": "Alaska Tidewater Glacier Terminus Positions, Version 1", - "catalog": "ALL STAC Catalog", + "catalog": "NSIDCV0 STAC Catalog", "state_date": "1948-01-01", "end_date": "2012-12-31", "bbox": "-151, 56.5, -132, 61.5", @@ -164725,7 +164725,7 @@ { "id": "NSIDC-0634_1", "title": "Alaska Tidewater Glacier Terminus Positions, Version 1", - "catalog": "NSIDCV0 STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1948-01-01", "end_date": "2012-12-31", "bbox": "-151, 56.5, -132, 61.5", @@ -165531,7 +165531,7 @@ { "id": "NWT_Burn_Severity_Maps_1694_1", "title": "ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2014-05-01", "end_date": "2015-10-01", "bbox": "-124.03, 58.29, -108.83, 65.55", @@ -165544,7 +165544,7 @@ { "id": "NWT_Burn_Severity_Maps_1694_1", "title": "ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-05-01", "end_date": "2015-10-01", "bbox": "-124.03, 58.29, -108.83, 65.55", @@ -165986,7 +165986,7 @@ { "id": "NorthSlope_NEE_TVPRM_1920_1", "title": "ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2008-01-01", "end_date": "2017-12-31", "bbox": "-177.47, 56.09, -128.59, 77.26", @@ -165999,7 +165999,7 @@ { "id": "NorthSlope_NEE_TVPRM_1920_1", "title": "ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2008-01-01", "end_date": "2017-12-31", "bbox": "-177.47, 56.09, -128.59, 77.26", @@ -167559,7 +167559,7 @@ { "id": "OCTS_L1_1", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167572,7 +167572,7 @@ { "id": "OCTS_L1_1", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167741,7 +167741,7 @@ { "id": "OCTS_L3b_CHL_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167754,7 +167754,7 @@ { "id": "OCTS_L3b_CHL_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167793,7 +167793,7 @@ { "id": "OCTS_L3b_IOP_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167806,7 +167806,7 @@ { "id": "OCTS_L3b_IOP_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167819,7 +167819,7 @@ { "id": "OCTS_L3b_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167832,7 +167832,7 @@ { "id": "OCTS_L3b_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167975,7 +167975,7 @@ { "id": "OCTS_L3b_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167988,7 +167988,7 @@ { "id": "OCTS_L3b_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168001,7 +168001,7 @@ { "id": "OCTS_L3b_POC_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -168014,7 +168014,7 @@ { "id": "OCTS_L3b_POC_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -168027,7 +168027,7 @@ { "id": "OCTS_L3b_RRS_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168040,7 +168040,7 @@ { "id": "OCTS_L3b_RRS_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168053,7 +168053,7 @@ { "id": "OCTS_L3b_RRS_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -168066,7 +168066,7 @@ { "id": "OCTS_L3b_RRS_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -168079,7 +168079,7 @@ { "id": "OCTS_L3m_CHL_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168092,7 +168092,7 @@ { "id": "OCTS_L3m_CHL_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168131,7 +168131,7 @@ { "id": "OCTS_L3m_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168144,7 +168144,7 @@ { "id": "OCTS_L3m_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168157,7 +168157,7 @@ { "id": "OCTS_L3m_IOP_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -168170,7 +168170,7 @@ { "id": "OCTS_L3m_IOP_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -168183,7 +168183,7 @@ { "id": "OCTS_L3m_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168196,7 +168196,7 @@ { "id": "OCTS_L3m_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168287,7 +168287,7 @@ { "id": "OCTS_L3m_PIC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168300,7 +168300,7 @@ { "id": "OCTS_L3m_PIC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168339,7 +168339,7 @@ { "id": "OCTS_L3m_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168352,7 +168352,7 @@ { "id": "OCTS_L3m_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168391,7 +168391,7 @@ { "id": "OCTS_L3m_RRS_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168404,7 +168404,7 @@ { "id": "OCTS_L3m_RRS_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -168469,7 +168469,7 @@ { "id": "OFR_94-212", "title": "A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1980-05-01", "end_date": "1988-09-06", "bbox": "-122, 46, -122, 46", @@ -168482,7 +168482,7 @@ { "id": "OFR_94-212", "title": "A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-05-01", "end_date": "1988-09-06", "bbox": "-122, 46, -122, 46", @@ -169688,19 +169688,6 @@ "description": "This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes.", "license": "proprietary" }, - { - "id": "OMAERUV_003", - "title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT", - "catalog": "OMINRT STAC Catalog", - "state_date": "2004-07-15", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMAERUV_003", - "description": "The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ ", - "license": "proprietary" - }, { "id": "OMAERUV_003", "title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 (OMAERUV) at GES DISC", @@ -169714,6 +169701,19 @@ "description": "The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes.", "license": "proprietary" }, + { + "id": "OMAERUV_003", + "title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT", + "catalog": "OMINRT STAC Catalog", + "state_date": "2004-07-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMAERUV_003", + "description": "The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ ", + "license": "proprietary" + }, { "id": "OMAERUV_004", "title": "OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V004 (OMAERUV) at GES DISC", @@ -170923,19 +170923,6 @@ "description": "This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags. The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.", "license": "proprietary" }, - { - "id": "OMSO2_003", - "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT", - "catalog": "OMINRT STAC Catalog", - "state_date": "2004-07-15", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMSO2_003", - "description": "The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/", - "license": "proprietary" - }, { "id": "OMSO2_003", "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC", @@ -170949,6 +170936,19 @@ "description": "The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li. The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.", "license": "proprietary" }, + { + "id": "OMSO2_003", + "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT", + "catalog": "OMINRT STAC Catalog", + "state_date": "2004-07-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMSO2_003", + "description": "The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/", + "license": "proprietary" + }, { "id": "OMSO2_CPR_003", "title": "OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC", @@ -170998,7 +170998,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3377057175-GES_DISC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3377057175-GES_DISC.html", "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMTO3G_004", - "description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.", + "description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.", "license": "proprietary" }, { @@ -171037,7 +171037,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3377057082-GES_DISC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3377057082-GES_DISC.html", "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMTO3_004", - "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.", + "description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm for this product was originally developed by a team led by Dr. Pawan K. Bhartia at NASA Goddard Space Flight Center. The current product lead is Dr. Can Li. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.", "license": "proprietary" }, { @@ -173786,7 +173786,7 @@ { "id": "PASSCAL_ALAR", "title": "Aleutian Arc Seismic Experiment", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -173799,7 +173799,7 @@ { "id": "PASSCAL_ALAR", "title": "Aleutian Arc Seismic Experiment", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -174267,7 +174267,7 @@ { "id": "POSTER-03CYCLONE_Not Applicable", "title": "2003 Tropical Cyclones of the World", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2003-01-08", "end_date": "2003-12-21", "bbox": "-180, -65, 180, 65", @@ -174280,7 +174280,7 @@ { "id": "POSTER-03CYCLONE_Not Applicable", "title": "2003 Tropical Cyclones of the World", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-01-08", "end_date": "2003-12-21", "bbox": "-180, -65, 180, 65", @@ -174293,7 +174293,7 @@ { "id": "POSTER-2004 Hurricanes_Not Applicable", "title": "2004 Landfalling Hurricanes Poster", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-08-13", "end_date": "2004-09-25", "bbox": "-91, 8, -33, 46.5", @@ -174306,7 +174306,7 @@ { "id": "POSTER-2004 Hurricanes_Not Applicable", "title": "2004 Landfalling Hurricanes Poster", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2004-08-13", "end_date": "2004-09-25", "bbox": "-91, 8, -33, 46.5", @@ -174345,7 +174345,7 @@ { "id": "POSTER-2005 Sig Hurricanes_Not Applicable", "title": "2005 Significant U.S. Hurricane Strikes Poster", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2005-07-10", "end_date": "2005-10-24", "bbox": "-102, 12, -69, 40.5", @@ -174358,7 +174358,7 @@ { "id": "POSTER-2005 Sig Hurricanes_Not Applicable", "title": "2005 Significant U.S. Hurricane Strikes Poster", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-07-10", "end_date": "2005-10-24", "bbox": "-102, 12, -69, 40.5", @@ -175008,7 +175008,7 @@ { "id": "Passive_Microwave_Snowoff_Data_1711_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1988-01-01", "end_date": "2018-12-31", "bbox": "-180, 37.98, 180, 90", @@ -175021,7 +175021,7 @@ { "id": "Passive_Microwave_Snowoff_Data_1711_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1988-01-01", "end_date": "2018-12-31", "bbox": "-180, 37.98, 180, 90", @@ -175125,7 +175125,7 @@ { "id": "Permafrost_Thaw_Depth_YK_1598_1", "title": "ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2009-06-27", "end_date": "2016-07-17", "bbox": "-165.69, 61.17, -165.03, 61.29", @@ -175138,7 +175138,7 @@ { "id": "Permafrost_Thaw_Depth_YK_1598_1", "title": "ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-06-27", "end_date": "2016-07-17", "bbox": "-165.69, 61.17, -165.03, 61.29", @@ -175346,7 +175346,7 @@ { "id": "PolInSAR_Canopy_Height_1589_1", "title": "AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-02-27", "end_date": "2016-03-08", "bbox": "9.29, -0.35, 11.83, 0.24", @@ -175359,7 +175359,7 @@ { "id": "PolInSAR_Canopy_Height_1589_1", "title": "AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-02-27", "end_date": "2016-03-08", "bbox": "9.29, -0.35, 11.83, 0.24", @@ -175411,7 +175411,7 @@ { "id": "Polarimetric_CT_1601_1", "title": "AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-02-25", "end_date": "2016-03-08", "bbox": "9.17, -2.08, 11.86, 0.61", @@ -175424,7 +175424,7 @@ { "id": "Polarimetric_CT_1601_1", "title": "AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-02-25", "end_date": "2016-03-08", "bbox": "9.17, -2.08, 11.86, 0.61", @@ -175437,7 +175437,7 @@ { "id": "Polarimetric_height_profile_1577_1", "title": "AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-02-25", "end_date": "2016-02-28", "bbox": "9.67, -2.08, 11.86, 0.1", @@ -175450,7 +175450,7 @@ { "id": "Polarimetric_height_profile_1577_1", "title": "AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-02-25", "end_date": "2016-02-28", "bbox": "9.67, -2.08, 11.86, 0.1", @@ -175476,7 +175476,7 @@ { "id": "PostFire_Tree_Regeneration_1955_1.1", "title": "ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1989-01-01", "end_date": "2018-12-31", "bbox": "-152.2, 49.12, -71.01, 66.96", @@ -175489,7 +175489,7 @@ { "id": "PostFire_Tree_Regeneration_1955_1.1", "title": "ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1989-01-01", "end_date": "2018-12-31", "bbox": "-152.2, 49.12, -71.01, 66.96", @@ -175502,7 +175502,7 @@ { "id": "Post_Fire_C_Emissions_1787_1", "title": "ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2015-04-06", "end_date": "2015-08-11", "bbox": "-116.06, 51.19, -100.17, 61.24", @@ -175515,7 +175515,7 @@ { "id": "Post_Fire_C_Emissions_1787_1", "title": "ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-04-06", "end_date": "2015-08-11", "bbox": "-116.06, 51.19, -100.17, 61.24", @@ -175775,7 +175775,7 @@ { "id": "Profile_based_PBL_heights_1706_1.1", "title": "ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-07-18", "end_date": "2019-07-26", "bbox": "-106.36, 28.65, -73.13, 49.49", @@ -175788,7 +175788,7 @@ { "id": "Profile_based_PBL_heights_1706_1.1", "title": "ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-07-18", "end_date": "2019-07-26", "bbox": "-106.36, 28.65, -73.13, 49.49", @@ -176724,7 +176724,7 @@ { "id": "RSFDCE_KLIM4", "title": "Absolute Minimum of Air Temperature. Year By Year Data", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1881-01-01", "end_date": "1965-12-31", "bbox": "25, 23.21, -175, 71", @@ -176737,7 +176737,7 @@ { "id": "RSFDCE_KLIM4", "title": "Absolute Minimum of Air Temperature. Year By Year Data", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1881-01-01", "end_date": "1965-12-31", "bbox": "25, 23.21, -175, 71", @@ -176815,7 +176815,7 @@ { "id": "Radial_Growth_PRI_1781_1", "title": "ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-05-01", "end_date": "2019-09-13", "bbox": "-149.76, 67.97, -149.72, 68.02", @@ -176828,7 +176828,7 @@ { "id": "Radial_Growth_PRI_1781_1", "title": "ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2018-05-01", "end_date": "2019-09-13", "bbox": "-149.76, 67.97, -149.72, 68.02", @@ -177049,7 +177049,7 @@ { "id": "RiSCC_Outcomes_Bibliography_1", "title": "A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1994-01-01", "end_date": "2006-12-31", "bbox": "-180, -70, 180, -50", @@ -177062,7 +177062,7 @@ { "id": "RiSCC_Outcomes_Bibliography_1", "title": "A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-01-01", "end_date": "2006-12-31", "bbox": "-180, -70, 180, -50", @@ -179792,7 +179792,7 @@ { "id": "SEAGLIDER_GUAM_2019_V1", "title": "Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (Guam 2019-2020)", - "catalog": "POCLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-10-03", "end_date": "2020-01-15", "bbox": "143.63035, 13.39476, 144.613, 14.71229", @@ -179805,7 +179805,7 @@ { "id": "SEAGLIDER_GUAM_2019_V1", "title": "Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (Guam 2019-2020)", - "catalog": "ALL STAC Catalog", + "catalog": "POCLOUD STAC Catalog", "state_date": "2019-10-03", "end_date": "2020-01-15", "bbox": "143.63035, 13.39476, 144.613, 14.71229", @@ -181365,7 +181365,7 @@ { "id": "SIZEX-89-SAR", "title": "Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1989-02-15", "end_date": "1989-02-27", "bbox": "15, 74, 25, 77", @@ -181378,7 +181378,7 @@ { "id": "SIZEX-89-SAR", "title": "Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1989-02-15", "end_date": "1989-02-27", "bbox": "15, 74, 25, 77", @@ -181911,7 +181911,7 @@ { "id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0", "title": "ACEX 2004 ODEN TRACK", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "19.045, 69.727, 175.94, 89.999", @@ -181924,7 +181924,7 @@ { "id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0", "title": "ACEX 2004 ODEN TRACK", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "19.045, 69.727, 175.94, 89.999", @@ -181963,7 +181963,7 @@ { "id": "SMHI_IPY_ACEX-2004-Sites_1.0", "title": "ACEX 2004 Sites", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "-4.05029, 69.727, 19.045, 89.999", @@ -181976,7 +181976,7 @@ { "id": "SMHI_IPY_ACEX-2004-Sites_1.0", "title": "ACEX 2004 Sites", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "-4.05029, 69.727, 19.045, 89.999", @@ -181989,7 +181989,7 @@ { "id": "SMHI_IPY_AGAVE2007-track_1.0", "title": "AGAVE2007 track", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2007-07-01", "end_date": "2007-08-09", "bbox": "-180, -90, 180, 90", @@ -182002,7 +182002,7 @@ { "id": "SMHI_IPY_AGAVE2007-track_1.0", "title": "AGAVE2007 track", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-07-01", "end_date": "2007-08-09", "bbox": "-180, -90, 180, 90", @@ -182015,7 +182015,7 @@ { "id": "SMHI_IPY_ALIS", "title": "ALIS, Auroral Large Imaging System", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1993-12-23", "end_date": "2009-02-18", "bbox": "18.8, 67.3, 21.7, 69.3", @@ -182028,7 +182028,7 @@ { "id": "SMHI_IPY_ALIS", "title": "ALIS, Auroral Large Imaging System", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1993-12-23", "end_date": "2009-02-18", "bbox": "18.8, 67.3, 21.7, 69.3", @@ -185577,7 +185577,7 @@ { "id": "SOAR2_UTIG", "title": "Airborne Geophysical Data acquired and reduced by The University of Texas Institute for Geophysics, 2000-2001.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "95, -82, 160, -77", @@ -185590,7 +185590,7 @@ { "id": "SOAR2_UTIG", "title": "Airborne Geophysical Data acquired and reduced by The University of Texas Institute for Geophysics, 2000-2001.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "95, -82, 160, -77", @@ -187072,26 +187072,26 @@ { "id": "SPL1BTB_006", "title": "SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -86.4, 180, 86.4", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1BTB_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1BTB_006", "description": "This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.", "license": "proprietary" }, { "id": "SPL1BTB_006", "title": "SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -86.4, 180, 86.4", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1BTB_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1BTB_006", "description": "This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.", "license": "proprietary" }, @@ -187099,7 +187099,7 @@ "id": "SPL1BTB_NRT_105", "title": "Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105", "catalog": "NSIDC_ECS STAC Catalog", - "state_date": "2025-01-09", + "state_date": "2025-01-10", "end_date": "", "bbox": "-180, -86.4, 180, 86.4", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json", @@ -187228,26 +187228,26 @@ { "id": "SPL1CTB_006", "title": "SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_006", "description": "This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product.", "license": "proprietary" }, { "id": "SPL1CTB_006", "title": "SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_006", "description": "This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product.", "license": "proprietary" }, @@ -187423,104 +187423,104 @@ { "id": "SPL2SMAP_S_003", "title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -60, 180, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "license": "proprietary" }, { "id": "SPL2SMAP_S_003", "title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -60, 180, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "license": "proprietary" }, { "id": "SPL2SMA_003", "title": "SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMA_003", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL2SMA_003", "title": "SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMA_003", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL2SMP_009", "title": "SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_009", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data.", "license": "proprietary" }, { "id": "SPL2SMP_009", "title": "SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_009", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data.", "license": "proprietary" }, { "id": "SPL2SMP_E_006", "title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "license": "proprietary" }, { "id": "SPL2SMP_E_006", "title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "license": "proprietary" }, @@ -187528,7 +187528,7 @@ "id": "SPL2SMP_NRT_107", "title": "Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107", "catalog": "NSIDC_ECS STAC Catalog", - "state_date": "2025-01-09", + "state_date": "2025-01-10", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json", @@ -187566,52 +187566,52 @@ { "id": "SPL3FTP_004", "title": "SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_004", "description": "This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0.", "license": "proprietary" }, { "id": "SPL3FTP_004", "title": "SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_004", "description": "This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0.", "license": "proprietary" }, { "id": "SPL3FTP_E_004", "title": "SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_E_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_E_004", "description": "This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal.", "license": "proprietary" }, { "id": "SPL3FTP_E_004", "title": "SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_E_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_E_004", "description": "This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal.", "license": "proprietary" }, @@ -187774,52 +187774,52 @@ { "id": "SPL4SMGP_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMGP_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMLM_007", "title": "SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMLM_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMLM_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMLM_007", "title": "SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMLM_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMLM_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, @@ -188424,7 +188424,7 @@ { "id": "SRDB_V5_1827_5", "title": "A Global Database of Soil Respiration Data, Version 5.0", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1961-01-01", "end_date": "2017-12-31", "bbox": "-163.71, -78.02, 175.9, 81.8", @@ -188437,7 +188437,7 @@ { "id": "SRDB_V5_1827_5", "title": "A Global Database of Soil Respiration Data, Version 5.0", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1961-01-01", "end_date": "2017-12-31", "bbox": "-163.71, -78.02, 175.9, 81.8", @@ -188749,7 +188749,7 @@ { "id": "SSEC-AMRC-AIRCRAFT", "title": "Aircraft meteorological reports over Antarctica", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-04-04", "end_date": "2015-08-31", "bbox": "-180, -90, 180, 0", @@ -188762,7 +188762,7 @@ { "id": "SSEC-AMRC-AIRCRAFT", "title": "Aircraft meteorological reports over Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-04-04", "end_date": "2015-08-31", "bbox": "-180, -90, 180, 0", @@ -191206,7 +191206,7 @@ { "id": "Scambos_PLR1441432", "title": "A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-06-01", "end_date": "2015-05-31", "bbox": "-180, -90, 180, 90", @@ -191219,7 +191219,7 @@ { "id": "Scambos_PLR1441432", "title": "A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2014-06-01", "end_date": "2015-05-31", "bbox": "-180, -90, 180, 90", @@ -192220,7 +192220,7 @@ { "id": "Skelton_Aeromag_Data", "title": "Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-01-01", "end_date": "1998-12-31", "bbox": "153.5, -79.7, 166.7, -77.5", @@ -192233,7 +192233,7 @@ { "id": "Skelton_Aeromag_Data", "title": "Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1997-01-01", "end_date": "1998-12-31", "bbox": "153.5, -79.7, 166.7, -77.5", @@ -192285,7 +192285,7 @@ { "id": "SnowMeltDuration_PMicrowave_1843_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1988-02-09", "end_date": "2018-07-20", "bbox": "-180, 51.6, -107.83, 72.41", @@ -192298,7 +192298,7 @@ { "id": "SnowMeltDuration_PMicrowave_1843_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1988-02-09", "end_date": "2018-07-20", "bbox": "-180, 51.6, -107.83, 72.41", @@ -192311,7 +192311,7 @@ { "id": "Snow_Cover_Extent_and_Depth_1757_1", "title": "ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2001-01-01", "end_date": "2017-12-30", "bbox": "-179.18, 55.57, -132.58, 71.42", @@ -192324,7 +192324,7 @@ { "id": "Snow_Cover_Extent_and_Depth_1757_1", "title": "ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-01", "end_date": "2017-12-30", "bbox": "-179.18, 55.57, -132.58, 71.42", @@ -192441,7 +192441,7 @@ { "id": "Soil_ActiveLayer_Properties_AK_2315_1", "title": "ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-08-09", "end_date": "2018-07-07", "bbox": "-149.53, 63.88, -146.56, 68.56", @@ -192454,7 +192454,7 @@ { "id": "Soil_ActiveLayer_Properties_AK_2315_1", "title": "ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-08-09", "end_date": "2018-07-07", "bbox": "-149.53, 63.88, -146.56, 68.56", @@ -192584,7 +192584,7 @@ { "id": "Southern_Boreal_Plot_Attribute_1740_1", "title": "ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-05-30", "end_date": "2016-06-16", "bbox": "-109.17, 54.09, -104.69, 57.36", @@ -192597,7 +192597,7 @@ { "id": "Southern_Boreal_Plot_Attribute_1740_1", "title": "ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-05-30", "end_date": "2016-06-16", "bbox": "-109.17, 54.09, -104.69, 57.36", @@ -193611,7 +193611,7 @@ { "id": "TEMR_RSFCE", "title": "Air Temperature Time Series", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1883-01-01", "end_date": "1987-12-31", "bbox": "25, 23.21, -175, 71", @@ -193624,7 +193624,7 @@ { "id": "TEMR_RSFCE", "title": "Air Temperature Time Series", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1883-01-01", "end_date": "1987-12-31", "bbox": "25, 23.21, -175, 71", @@ -202477,7 +202477,7 @@ { "id": "UM0708_25_multi-frequency_acoustic", "title": "Acoustic data of multi-frequency acoustic system", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-12-24", "end_date": "2008-02-14", "bbox": "-180, -90, 180, 90", @@ -202490,7 +202490,7 @@ { "id": "UM0708_25_multi-frequency_acoustic", "title": "Acoustic data of multi-frequency acoustic system", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2007-12-24", "end_date": "2008-02-14", "bbox": "-180, -90, 180, 90", @@ -202542,7 +202542,7 @@ { "id": "UNEP_GRID_SF_AFRICA_third version", "title": "Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1960-01-01", "end_date": "1990-12-31", "bbox": "-18, -35, 52, 35", @@ -202555,7 +202555,7 @@ { "id": "UNEP_GRID_SF_AFRICA_third version", "title": "Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1960-01-01", "end_date": "1990-12-31", "bbox": "-18, -35, 52, 35", @@ -202711,7 +202711,7 @@ { "id": "USAP-1043623_1", "title": "Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-06-15", "end_date": "2015-05-31", "bbox": "117.5, -67.4, 146, -47", @@ -202724,7 +202724,7 @@ { "id": "USAP-1043623_1", "title": "Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-06-15", "end_date": "2015-05-31", "bbox": "117.5, -67.4, 146, -47", @@ -202958,7 +202958,7 @@ { "id": "USAP-1543498_1", "title": "A Full Lifecycle Approach to Understanding Ad\u00e9lie Penguin Response to Changing Pack Ice Conditions in the Ross Sea", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-06-01", "end_date": "", "bbox": "165, -78, -150, -60", @@ -202971,7 +202971,7 @@ { "id": "USAP-1543498_1", "title": "A Full Lifecycle Approach to Understanding Ad\u00e9lie Penguin Response to Changing Pack Ice Conditions in the Ross Sea", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2016-06-01", "end_date": "", "bbox": "165, -78, -150, -60", @@ -202984,7 +202984,7 @@ { "id": "USAP-1544526_1", "title": "Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-09-01", "end_date": "2017-08-31", "bbox": "160, -77.8, 163.7, -76.5", @@ -202997,7 +202997,7 @@ { "id": "USAP-1544526_1", "title": "Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2016-09-01", "end_date": "2017-08-31", "bbox": "160, -77.8, 163.7, -76.5", @@ -203127,7 +203127,7 @@ { "id": "USAP-1656344_1", "title": "A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-08-01", "end_date": "2018-07-31", "bbox": "-64.1, -65, -63.9, -64.75", @@ -203140,7 +203140,7 @@ { "id": "USAP-1656344_1", "title": "A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2016-08-01", "end_date": "2018-07-31", "bbox": "-64.1, -65, -63.9, -64.75", @@ -203153,7 +203153,7 @@ { "id": "USAP-1744755_1", "title": "A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-05-01", "end_date": "2022-04-30", "bbox": "-80, -70, -30, -45", @@ -203166,7 +203166,7 @@ { "id": "USAP-1744755_1", "title": "A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2018-05-01", "end_date": "2022-04-30", "bbox": "-80, -70, -30, -45", @@ -203387,7 +203387,7 @@ { "id": "USAP-1947094_1", "title": "A non-amniote perspective on the recovery from the end-Permian extinction at high latitudes: paleobiology of Early Triassic temnospondyls from Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2020-05-01", "end_date": "2022-04-30", "bbox": "-180, -90, 180, -60", @@ -203400,7 +203400,7 @@ { "id": "USAP-1947094_1", "title": "A non-amniote perspective on the recovery from the end-Permian extinction at high latitudes: paleobiology of Early Triassic temnospondyls from Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-05-01", "end_date": "2022-04-30", "bbox": "-180, -90, 180, -60", @@ -203530,7 +203530,7 @@ { "id": "USAP-2130663_1", "title": "2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2021-06-01", "end_date": "2023-05-31", "bbox": "-180, -90, 180, -60", @@ -203543,7 +203543,7 @@ { "id": "USAP-2130663_1", "title": "2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-06-01", "end_date": "2023-05-31", "bbox": "-180, -90, 180, -60", @@ -203647,7 +203647,7 @@ { "id": "USAP-9615281_1", "title": "Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-08-15", "end_date": "2002-07-31", "bbox": "-170, -84, -135, -76", @@ -203660,7 +203660,7 @@ { "id": "USAP-9615281_1", "title": "Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "1997-08-15", "end_date": "2002-07-31", "bbox": "-170, -84, -135, -76", @@ -203686,7 +203686,7 @@ { "id": "USARC_AERIAL_PHOTOS", "title": "Aerial Photography of Antarctica", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -62.83", @@ -203699,7 +203699,7 @@ { "id": "USARC_AERIAL_PHOTOS", "title": "Aerial Photography of Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -62.83", @@ -203712,7 +203712,7 @@ { "id": "USArray_Ground_Temperature_1680_1.1", "title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-05-13", "end_date": "2021-07-08", "bbox": "-165.35, 59.25, -141.59, 71", @@ -203725,7 +203725,7 @@ { "id": "USArray_Ground_Temperature_1680_1.1", "title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-05-13", "end_date": "2021-07-08", "bbox": "-165.35, 59.25, -141.59, 71", @@ -203868,7 +203868,7 @@ { "id": "USGS-DDS-3", "title": "A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-71.5, 42, -70, 43", @@ -203881,7 +203881,7 @@ { "id": "USGS-DDS-3", "title": "A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-71.5, 42, -70, 43", @@ -203894,7 +203894,7 @@ { "id": "USGS-DDS-33_1.0", "title": "3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-111.4, 40.65, -103.7, 45.35", @@ -203907,7 +203907,7 @@ { "id": "USGS-DDS-33_1.0", "title": "3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-111.4, 40.65, -103.7, 45.35", @@ -203972,7 +203972,7 @@ { "id": "USGS-DDS_30_P10_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-121.388916, 34.890034, -118.58517, 37.83907", @@ -203985,7 +203985,7 @@ { "id": "USGS-DDS_30_P10_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-121.388916, 34.890034, -118.58517, 37.83907", @@ -204349,7 +204349,7 @@ { "id": "USGS_DDS_P12_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-121.977486, 34.488464, -119.44189, 36.40565", @@ -204362,7 +204362,7 @@ { "id": "USGS_DDS_P12_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-121.977486, 34.488464, -119.44189, 36.40565", @@ -204375,7 +204375,7 @@ { "id": "USGS_DDS_P12_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-121.977486, 34.488464, -119.44189, 36.40565", @@ -204388,7 +204388,7 @@ { "id": "USGS_DDS_P12_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-121.977486, 34.488464, -119.44189, 36.40565", @@ -204453,7 +204453,7 @@ { "id": "USGS_DDS_P14_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-119.63631, 32.7535, -117.52315, 34.17464", @@ -204466,7 +204466,7 @@ { "id": "USGS_DDS_P14_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-119.63631, 32.7535, -117.52315, 34.17464", @@ -204479,7 +204479,7 @@ { "id": "USGS_DDS_P14_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-119.63631, 32.7535, -117.52315, 34.17464", @@ -204492,7 +204492,7 @@ { "id": "USGS_DDS_P14_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-119.63631, 32.7535, -117.52315, 34.17464", @@ -204583,7 +204583,7 @@ { "id": "USGS_DDS_P17_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", @@ -204596,7 +204596,7 @@ { "id": "USGS_DDS_P17_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", @@ -204661,7 +204661,7 @@ { "id": "USGS_DDS_P19_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", @@ -204674,7 +204674,7 @@ { "id": "USGS_DDS_P19_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", @@ -204687,7 +204687,7 @@ { "id": "USGS_DDS_P19_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", @@ -204700,7 +204700,7 @@ { "id": "USGS_DDS_P19_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", @@ -204739,7 +204739,7 @@ { "id": "USGS_DDS_P20_continuous", "title": "1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -204752,7 +204752,7 @@ { "id": "USGS_DDS_P20_continuous", "title": "1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -204765,7 +204765,7 @@ { "id": "USGS_DDS_P20_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -204778,7 +204778,7 @@ { "id": "USGS_DDS_P20_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -204791,7 +204791,7 @@ { "id": "USGS_DDS_P2_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", @@ -204804,7 +204804,7 @@ { "id": "USGS_DDS_P2_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", @@ -204817,7 +204817,7 @@ { "id": "USGS_DDS_P2_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", @@ -204830,7 +204830,7 @@ { "id": "USGS_DDS_P2_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", @@ -206273,7 +206273,7 @@ { "id": "USGS_Map_MF-2381-D_1.0", "title": "Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-118, 35, -115, 38.25", @@ -206286,7 +206286,7 @@ { "id": "USGS_Map_MF-2381-D_1.0", "title": "Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-118, 35, -115, 38.25", @@ -206364,7 +206364,7 @@ { "id": "USGS_NPS_AcadiaAccuracy_Final", "title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-75.262726, 43.99941, -68.044304, 44.48051", @@ -206377,7 +206377,7 @@ { "id": "USGS_NPS_AcadiaAccuracy_Final", "title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-75.262726, 43.99941, -68.044304, 44.48051", @@ -206390,7 +206390,7 @@ { "id": "USGS_NPS_AcadiaFieldPlots_Final", "title": "Acadia National Park Vegetation Mapping Project - Field Plot Points", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-68.65603, 44.017136, -68.045715, 44.404953", @@ -206403,7 +206403,7 @@ { "id": "USGS_NPS_AcadiaFieldPlots_Final", "title": "Acadia National Park Vegetation Mapping Project - Field Plot Points", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-68.65603, 44.017136, -68.045715, 44.404953", @@ -208197,7 +208197,7 @@ { "id": "USGS_OFR_2004_1058", "title": "2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-01-01", "end_date": "", "bbox": "-168, 46, -126, 76", @@ -208210,7 +208210,7 @@ { "id": "USGS_OFR_2004_1058", "title": "2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2002-01-01", "end_date": "", "bbox": "-168, 46, -126, 76", @@ -208769,7 +208769,7 @@ { "id": "USGS_OFR_2005_1148_1.0", "title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-80.82, 39.43, -74.41, 42.56", @@ -208782,7 +208782,7 @@ { "id": "USGS_OFR_2005_1148_1.0", "title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-80.82, 39.43, -74.41, 42.56", @@ -209159,7 +209159,7 @@ { "id": "USGS_OFR_2006_1136", "title": "Aeromagnetic Survey of Dillingham Area in Southwest Alaska, A Website for the Preliminary Distribution of Data", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2005-09-01", "end_date": "2005-10-22", "bbox": "-159.19, 58.3, -155.45, 60.06", @@ -209172,7 +209172,7 @@ { "id": "USGS_OFR_2006_1136", "title": "Aeromagnetic Survey of Dillingham Area in Southwest Alaska, A Website for the Preliminary Distribution of Data", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-09-01", "end_date": "2005-10-22", "bbox": "-159.19, 58.3, -155.45, 60.06", @@ -209458,7 +209458,7 @@ { "id": "USGS_OFR_2007_1169", "title": "2005 Hydrographic Survey of South San Francisco Bay, California", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-126, 37, -122, 42", @@ -209471,7 +209471,7 @@ { "id": "USGS_OFR_2007_1169", "title": "2005 Hydrographic Survey of South San Francisco Bay, California", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-126, 37, -122, 42", @@ -210173,7 +210173,7 @@ { "id": "USGS_OFR_Acid_Deposition", "title": "Acid Deposition Sensitivity of the Southern Appalachian Assessment Area", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-87, 31, -77, 39", @@ -210186,7 +210186,7 @@ { "id": "USGS_OFR_Acid_Deposition", "title": "Acid Deposition Sensitivity of the Southern Appalachian Assessment Area", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-87, 31, -77, 39", @@ -210212,7 +210212,7 @@ { "id": "USGS_P-11_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", @@ -210225,7 +210225,7 @@ { "id": "USGS_P-11_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", @@ -210420,7 +210420,7 @@ { "id": "USGS_SESC_SturgeonBiblio_3", "title": "A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi.", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -210433,7 +210433,7 @@ { "id": "USGS_SESC_SturgeonBiblio_3", "title": "A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi.", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -210628,7 +210628,7 @@ { "id": "USGS_SOFIA_Eco_hist_db_2008_present_2", "title": "2008 - Present Ecosystem History of South Florida's Estuaries Database version 2", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2008-03-16", "end_date": "2012-09-30", "bbox": "-81.83, 24.75, -80, 26.5", @@ -210641,7 +210641,7 @@ { "id": "USGS_SOFIA_Eco_hist_db_2008_present_2", "title": "2008 - Present Ecosystem History of South Florida's Estuaries Database version 2", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2008-03-16", "end_date": "2012-09-30", "bbox": "-81.83, 24.75, -80, 26.5", @@ -210953,7 +210953,7 @@ { "id": "USGS_SOFIA_aerial-photos", "title": "Aerial Photos of the 1940s", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1940-02-14", "end_date": "1940-08-21", "bbox": "-81.9, 24.41, -79.98, 26.22", @@ -210966,7 +210966,7 @@ { "id": "USGS_SOFIA_aerial-photos", "title": "Aerial Photos of the 1940s", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1940-02-14", "end_date": "1940-08-21", "bbox": "-81.9, 24.41, -79.98, 26.22", @@ -211005,7 +211005,7 @@ { "id": "USGS_SOFIA_atlss_prog", "title": "Across Trophic Level System Simulation (ATLSS) Program", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "", "bbox": "-81.30333, 24.696152, -80.26212, 25.847113", @@ -211018,7 +211018,7 @@ { "id": "USGS_SOFIA_atlss_prog", "title": "Across Trophic Level System Simulation (ATLSS) Program", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "", "bbox": "-81.30333, 24.696152, -80.26212, 25.847113", @@ -211239,7 +211239,7 @@ { "id": "USGS_SOFIA_coupled_sw-gw_model", "title": "A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-01-01", "end_date": "2009-09-30", "bbox": "-81.56, 25.02, -80, 25.75", @@ -211252,7 +211252,7 @@ { "id": "USGS_SOFIA_coupled_sw-gw_model", "title": "A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1995-01-01", "end_date": "2009-09-30", "bbox": "-81.56, 25.02, -80, 25.75", @@ -211382,7 +211382,7 @@ { "id": "USGS_SOFIA_eco_hist_db1995-2007_version 7", "title": "1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-09-27", "end_date": "2007-04-03", "bbox": "-81.83, 24.75, -80, 26.5", @@ -211395,7 +211395,7 @@ { "id": "USGS_SOFIA_eco_hist_db1995-2007_version 7", "title": "1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1994-09-27", "end_date": "2007-04-03", "bbox": "-81.83, 24.75, -80, 26.5", @@ -212383,7 +212383,7 @@ { "id": "USGS_SOFIA_la_florida", "title": "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from \"Down-Scaled\" AOGCM Climate Scenarios in Combination with Ecological Modeling", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "2000-12-31", "bbox": "-92, 23, -75, 38.24", @@ -212396,7 +212396,7 @@ { "id": "USGS_SOFIA_la_florida", "title": "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from \"Down-Scaled\" AOGCM Climate Scenarios in Combination with Ecological Modeling", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "2000-12-31", "bbox": "-92, 23, -75, 38.24", @@ -213163,7 +213163,7 @@ { "id": "USGS_cont1992", "title": "1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.652695, 34.364513, -116.55357, 35.081955", @@ -213176,7 +213176,7 @@ { "id": "USGS_cont1992", "title": "1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.652695, 34.364513, -116.55357, 35.081955", @@ -213189,7 +213189,7 @@ { "id": "USGS_cont1994", "title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.07194, 34.095333, -115.98976, 34.64026", @@ -213202,7 +213202,7 @@ { "id": "USGS_cont1994", "title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.07194, 34.095333, -115.98976, 34.64026", @@ -213215,7 +213215,7 @@ { "id": "USGS_cont1996", "title": "1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.63461, 34.109745, -115.98707, 35.31552", @@ -213228,7 +213228,7 @@ { "id": "USGS_cont1996", "title": "1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.63461, 34.109745, -115.98707, 35.31552", @@ -214421,6 +214421,32 @@ "description": "The VIIRS Geolocation Onboard Calibrator (OBC)-IP file contains solar diffuser observations, the associated gain state and HAM side information, and all engineering and housekeeping data, including unscaled data from the Solar Diffuser Stability Monitor (SDSM)/VIIRS Earth View Radiometric Calibration Unit and the Solar Diffuser GEO angles.", "license": "proprietary" }, + { + "id": "VIIRSJ1_L2_IOP_2022.0", + "title": "NOAA-20 VIIRS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2017-11-29", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ1_L2_IOP_2022.0", + "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. 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There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", + "license": "proprietary" + }, + { + "id": "VIIRSJ2_L3m_PAR_NRT_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Photosynthetically Available Radiation (PAR) - Near Real-time (NRT) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023949-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023949-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_PAR_NRT_2022.0", + "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", + "license": "proprietary" + }, { "id": "VIIRSJ2_L3m_PAR_NRT_R2022.0", "title": "NOAA-21 VIIRS Global Mapped Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version R2022.0", @@ -215487,6 +216293,32 @@ "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "license": "proprietary" }, + { + "id": "VIIRSJ2_L3m_PIC_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023974-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023974-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_PIC_2022.0", + "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", + "license": "proprietary" + }, + { + "id": "VIIRSJ2_L3m_PIC_NRT_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) - Near Real-time (NRT) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023967-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023967-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_PIC_NRT_2022.0", + "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", + "license": "proprietary" + }, { "id": "VIIRSJ2_L3m_PIC_NRT_R2022.0", "title": "NOAA-21 VIIRS Global Mapped Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version R2022.0", @@ -215513,6 +216345,32 @@ "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "license": "proprietary" }, + { + "id": "VIIRSJ2_L3m_POC_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023999-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023999-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_POC_2022.0", + "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", + "license": "proprietary" + }, + { + "id": "VIIRSJ2_L3m_POC_NRT_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Particulate Organic Carbon (POC) - Near Real-time (NRT) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023985-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397023985-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_POC_NRT_2022.0", + "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", + "license": "proprietary" + }, { "id": "VIIRSJ2_L3m_POC_NRT_R2022.0", "title": "NOAA-21 VIIRS Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0", @@ -215539,6 +216397,32 @@ "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", "license": "proprietary" }, + { + "id": "VIIRSJ2_L3m_RRS_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397024028-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397024028-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_RRS_2022.0", + "description": "The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).", + "license": "proprietary" + }, + { + "id": "VIIRSJ2_L3m_RRS_NRT_2022.0", + "title": "NOAA-21 VIIRS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version 2022.0", + "catalog": "OB_CLOUD STAC Catalog", + "state_date": "2022-11-10", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3397024011-OB_CLOUD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3397024011-OB_CLOUD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OB_CLOUD/collections/VIIRSJ2_L3m_RRS_NRT_2022.0", + "description": "The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.", + "license": "proprietary" + }, { "id": "VIIRSJ2_L3m_RRS_NRT_R2022.0", "title": "NOAA-21 VIIRS Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version R2022.0", @@ -222159,7 +223043,7 @@ { "id": "WIND_3DP", "title": "3-D Plasma and Energetic Particle Investigation on WIND", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1994-11-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -222172,7 +223056,7 @@ { "id": "WIND_3DP", "title": "3-D Plasma and Energetic Particle Investigation on WIND", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-11-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -222198,7 +223082,7 @@ { "id": "WISPMAWSON04-05_1", "title": "A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-12-10", "end_date": "2005-04-25", "bbox": "62.18384, -67.68587, 63.40759, -67.47282", @@ -222211,7 +223095,7 @@ { "id": "WISPMAWSON04-05_1", "title": "A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2004-12-10", "end_date": "2005-04-25", "bbox": "62.18384, -67.68587, 63.40759, -67.47282", @@ -222497,7 +223381,7 @@ { "id": "WYGISC_HYDRO24K", "title": "1:24,000-scale Hydrography for ortions Wyoming, various sources", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1967-01-01", "end_date": "1971-12-31", "bbox": "-111, 41, -104, 45", @@ -222510,7 +223394,7 @@ { "id": "WYGISC_HYDRO24K", "title": "1:24,000-scale Hydrography for ortions Wyoming, various sources", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1967-01-01", "end_date": "1971-12-31", "bbox": "-111, 41, -104, 45", @@ -222523,7 +223407,7 @@ { "id": "WYGISC_LANDUSE", "title": "Agricultural Land Use of Wyoming", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "1982-12-31", "bbox": "-111.09, 40.95, -103.88, 45.107", @@ -222536,7 +223420,7 @@ { "id": "WYGISC_LANDUSE", "title": "Agricultural Land Use of Wyoming", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1980-01-01", "end_date": "1982-12-31", "bbox": "-111.09, 40.95, -103.88, 45.107", @@ -222731,7 +223615,7 @@ { "id": "Wildfire_Effects_Spruce_Field_1595_1", "title": "ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2017-07-26", "end_date": "2017-07-28", "bbox": "-152.42, 65.1, -151.95, 65.23", @@ -222744,7 +223628,7 @@ { "id": "Wildfire_Effects_Spruce_Field_1595_1", "title": "ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-07-26", "end_date": "2017-07-28", "bbox": "-152.42, 65.1, -151.95, 65.23", @@ -222770,7 +223654,7 @@ { "id": "Wildfires_2014_NWT_Canada_1307_1", "title": "ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-07-07", "end_date": "2015-07-15", "bbox": "-121.6, 60.33, -110.68, 64.25", @@ -222783,7 +223667,7 @@ { "id": "Wildfires_2014_NWT_Canada_1307_1", "title": "ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1997-07-07", "end_date": "2015-07-15", "bbox": "-121.6, 60.33, -110.68, 64.25", @@ -222796,7 +223680,7 @@ { "id": "Wildfires_Date_of_Burning_1559_1.1", "title": "ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-01", "end_date": "2019-12-31", "bbox": "-178.84, 41.75, -53.83, 70.16", @@ -222809,7 +223693,7 @@ { "id": "Wildfires_Date_of_Burning_1559_1.1", "title": "ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2001-01-01", "end_date": "2019-12-31", "bbox": "-178.84, 41.75, -53.83, 70.16", @@ -222848,7 +223732,7 @@ { "id": "Wildfires_NWT_Canada_2018_1703_1", "title": "ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2018-08-12", "end_date": "2018-08-22", "bbox": "-117.43, 60.45, -113.42, 62.57", @@ -222861,7 +223745,7 @@ { "id": "Wildfires_NWT_Canada_2018_1703_1", "title": "ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-08-12", "end_date": "2018-08-22", "bbox": "-117.43, 60.45, -113.42, 62.57", @@ -222874,7 +223758,7 @@ { "id": "Wildfires_NWT_Canada_2019_1900_1", "title": "ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2018-08-16", "end_date": "2019-09-05", "bbox": "-117.43, 60.92, -113.02, 62.57", @@ -222887,7 +223771,7 @@ { "id": "Wildfires_NWT_Canada_2019_1900_1", "title": "ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-08-16", "end_date": "2019-09-05", "bbox": "-117.43, 60.92, -113.02, 62.57", @@ -223030,7 +223914,7 @@ { "id": "XAERDT_L2_ABI_G16_1", "title": "ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "ALL STAC Catalog", + "catalog": "LAADS STAC Catalog", "state_date": "2019-01-01", "end_date": "2023-01-02", "bbox": "-180, -90, 180, 90", @@ -223043,7 +223927,7 @@ { "id": "XAERDT_L2_ABI_G16_1", "title": "ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "LAADS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-01", "end_date": "2023-01-02", "bbox": "-180, -90, 180, 90", @@ -223056,7 +223940,7 @@ { "id": "XAERDT_L2_ABI_G17_1", "title": "ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "ALL STAC Catalog", + "catalog": "LAADS STAC Catalog", "state_date": "2019-01-01", "end_date": "2023-01-02", "bbox": "-180, -90, 180, 90", @@ -223069,7 +223953,7 @@ { "id": "XAERDT_L2_ABI_G17_1", "title": "ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "LAADS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-01", "end_date": "2023-01-02", "bbox": "-180, -90, 180, 90", @@ -223108,7 +223992,7 @@ { "id": "XAERDT_L2_AHI_H09_1", "title": "AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "ALL STAC Catalog", + "catalog": "LAADS STAC Catalog", "state_date": "2022-12-13", "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", @@ -223121,7 +224005,7 @@ { "id": "XAERDT_L2_AHI_H09_1", "title": "AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "LAADS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2022-12-13", "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", @@ -223238,7 +224122,7 @@ { "id": "ZZZ302", "title": "Alabama Remote Sensing Archive Multispectral Imagery of Alabama from Landsat and Skylab", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1972-01-01", "end_date": "1984-01-01", "bbox": "-92, 24, -80, 35", @@ -223251,7 +224135,7 @@ { "id": "ZZZ302", "title": "Alabama Remote Sensing Archive Multispectral Imagery of Alabama from Landsat and Skylab", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1972-01-01", "end_date": "1984-01-01", "bbox": "-92, 24, -80, 35", @@ -223342,7 +224226,7 @@ { "id": "a-dataset-of-40000-trees-with-section-wise-measured-stem-diameter-and-length-and_1.0", "title": "A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2024-01-01", "end_date": "2024-01-01", "bbox": "7.56, 47.23, 7.56, 47.23", @@ -223355,7 +224239,7 @@ { "id": "a-dataset-of-40000-trees-with-section-wise-measured-stem-diameter-and-length-and_1.0", "title": "A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2024-01-01", "end_date": "2024-01-01", "bbox": "7.56, 47.23, 7.56, 47.23", @@ -223381,7 +224265,7 @@ { "id": "a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0", "title": "A numerical solver for heat and mass transport in snow based on FEniCS", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2022-01-01", "end_date": "2022-01-01", "bbox": "9.8472494, 46.812044, 9.8472494, 46.812044", @@ -223394,7 +224278,7 @@ { "id": "a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0", "title": "A numerical solver for heat and mass transport in snow based on FEniCS", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2022-01-01", "end_date": "2022-01-01", "bbox": "9.8472494, 46.812044, 9.8472494, 46.812044", @@ -223615,7 +224499,7 @@ { "id": "aamhcpex_1", "title": "AAMH CPEX", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2017-05-26", "end_date": "2017-07-16", "bbox": "154.716, 0.6408, -19.5629, 44.9689", @@ -223628,7 +224512,7 @@ { "id": "aamhcpex_1", "title": "AAMH CPEX", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-05-26", "end_date": "2017-07-16", "bbox": "154.716, 0.6408, -19.5629, 44.9689", @@ -223745,7 +224629,7 @@ { "id": "accumulation_lawdome_1960_1", "title": "Accumulation Measurements, Law Dome 1959-1960", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1959-01-01", "end_date": "1960-12-31", "bbox": "110, -67, 115, -65", @@ -223758,7 +224642,7 @@ { "id": "accumulation_lawdome_1960_1", "title": "Accumulation Measurements, Law Dome 1959-1960", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1959-01-01", "end_date": "1960-12-31", "bbox": "110, -67, 115, -65", @@ -223771,7 +224655,7 @@ { "id": "aces1am_1", "title": "ACES Aircraft and Mechanical Data", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -223784,7 +224668,7 @@ { "id": "aces1am_1", "title": "ACES Aircraft and Mechanical Data", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -223849,7 +224733,7 @@ { "id": "aces1log_1", "title": "ACES LOG DATA", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -223862,7 +224746,7 @@ { "id": "aces1log_1", "title": "ACES LOG DATA", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -223927,7 +224811,7 @@ { "id": "acoustic_charts_v6_1994_95_1", "title": "Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS)", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1995-02-06", "end_date": "1995-04-12", "bbox": "60, -69.393, 147.473, -42.882", @@ -223940,7 +224824,7 @@ { "id": "acoustic_charts_v6_1994_95_1", "title": "Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS)", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-02-06", "end_date": "1995-04-12", "bbox": "60, -69.393, 147.473, -42.882", @@ -223979,7 +224863,7 @@ { "id": "acoustic_doppler_current_profiler_data_-_2011", "title": "Acoustic Doppler Current Profiler Data - 2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-08-22", "end_date": "2011-09-13", "bbox": "-156, 70, -154, 72", @@ -223992,7 +224876,7 @@ { "id": "acoustic_doppler_current_profiler_data_-_2011", "title": "Acoustic Doppler Current Profiler Data - 2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2011-08-22", "end_date": "2011-09-13", "bbox": "-156, 70, -154, 72", @@ -224005,7 +224889,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2010", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2010", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2010-07-10", "end_date": "2010-08-16", "bbox": "-156, 70, -158, 71", @@ -224018,7 +224902,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2010", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2010", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-07-10", "end_date": "2010-08-16", "bbox": "-156, 70, -158, 71", @@ -224057,7 +224941,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2012", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2012", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156, 70, -157, 71", @@ -224070,7 +224954,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2012", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2012", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156, 70, -157, 71", @@ -224109,7 +224993,7 @@ { "id": "active_layer_arcss_grid_barrow_alaska_2011", "title": "Active Layer ARCSS grid Barrow, Alaska 2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2011-06-14", "end_date": "2011-07-25", "bbox": "-156.6, 71, -156.5, 71.5", @@ -224122,7 +225006,7 @@ { "id": "active_layer_arcss_grid_barrow_alaska_2011", "title": "Active Layer ARCSS grid Barrow, Alaska 2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-06-14", "end_date": "2011-07-25", "bbox": "-156.6, 71, -156.5, 71.5", @@ -224161,7 +225045,7 @@ { "id": "active_layer_nims_grid_atqasuk_alaska_2011", "title": "Active Layer NIMS grid Atqasuk, Alaska 2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-06-05", "end_date": "2011-08-12", "bbox": "-156, 70, -157, 71", @@ -224174,7 +225058,7 @@ { "id": "active_layer_nims_grid_atqasuk_alaska_2011", "title": "Active Layer NIMS grid Atqasuk, Alaska 2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2011-06-05", "end_date": "2011-08-12", "bbox": "-156, 70, -157, 71", @@ -224187,7 +225071,7 @@ { "id": "active_layer_nims_grid_atqasuk_alaska_2012", "title": "Active Layer NIMS grid Atqasuk, Alaska 2012", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156, 70, -157, 71", @@ -224200,7 +225084,7 @@ { "id": "active_layer_nims_grid_atqasuk_alaska_2012", "title": "Active Layer NIMS grid Atqasuk, Alaska 2012", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156, 70, -157, 71", @@ -224239,7 +225123,7 @@ { "id": "active_layer_nims_grid_barrow_alaska_2012", "title": "Active Layer NIMS grid Barrow, Alaska 2012", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156.6, 71, -156.5, 71.5", @@ -224252,7 +225136,7 @@ { "id": "active_layer_nims_grid_barrow_alaska_2012", "title": "Active Layer NIMS grid Barrow, Alaska 2012", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156.6, 71, -156.5, 71.5", @@ -224330,7 +225214,7 @@ { "id": "adpe-aat-census_1", "title": "Adelie penguin census from records from 1931 to 2007 AAT region", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1931-02-13", "end_date": "2006-12-08", "bbox": "38.2, -69.6, 89.5, -65.8", @@ -224343,7 +225227,7 @@ { "id": "adpe-aat-census_1", "title": "Adelie penguin census from records from 1931 to 2007 AAT region", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1931-02-13", "end_date": "2006-12-08", "bbox": "38.2, -69.6, 89.5, -65.8", @@ -224421,7 +225305,7 @@ { "id": "aerial_mosaics_macquarie_2017_2", "title": "Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2017-01-15", "end_date": "2017-02-15", "bbox": "158.874, -54.506, 158.954, -54.483", @@ -224434,7 +225318,7 @@ { "id": "aerial_mosaics_macquarie_2017_2", "title": "Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-01-15", "end_date": "2017-02-15", "bbox": "158.874, -54.506, 158.954, -54.483", @@ -224447,7 +225331,7 @@ { "id": "aerial_photo_sea_ice_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2003-09-10", "end_date": "", "bbox": "-58.2, -69.67, 118.85, -64.03", @@ -224460,7 +225344,7 @@ { "id": "aerial_photo_sea_ice_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-09-10", "end_date": "", "bbox": "-58.2, -69.67, 118.85, -64.03", @@ -224473,7 +225357,7 @@ { "id": "aerial_photo_sea_ice_ARISE_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-09-10", "end_date": "2003-10-31", "bbox": "109.1, -66.7, 118.85, -64.03", @@ -224486,7 +225370,7 @@ { "id": "aerial_photo_sea_ice_ARISE_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2003-09-10", "end_date": "2003-10-31", "bbox": "109.1, -66.7, 118.85, -64.03", @@ -224525,7 +225409,7 @@ { "id": "aerial_photo_sea_ice_SIPEX_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-08-29", "end_date": "2007-10-16", "bbox": "109.1, -66.7, 118.85, -64.03", @@ -224538,7 +225422,7 @@ { "id": "aerial_photo_sea_ice_SIPEX_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2007-08-29", "end_date": "2007-10-16", "bbox": "109.1, -66.7, 118.85, -64.03", @@ -224564,7 +225448,7 @@ { "id": "aerial_photographs_from_columbia_glacier_1976-2010", "title": "Aerial Photographs from Columbia Glacier, 1976-2010", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1976-07-24", "end_date": "2011-06-15", "bbox": "-146.895, 61.22, -146.895, 61.22", @@ -224577,7 +225461,7 @@ { "id": "aerial_photographs_from_columbia_glacier_1976-2010", "title": "Aerial Photographs from Columbia Glacier, 1976-2010", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1976-07-24", "end_date": "2011-06-15", "bbox": "-146.895, 61.22, -146.895, 61.22", @@ -224668,7 +225552,7 @@ { "id": "aerosol-data-weissfluhjoch_1.0", "title": "Aerosol Data Weissfluhjoch", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "9.806475, 46.832964, 9.806475, 46.832964", @@ -224681,7 +225565,7 @@ { "id": "aerosol-data-weissfluhjoch_1.0", "title": "Aerosol Data Weissfluhjoch", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "9.806475, 46.832964, 9.806475, 46.832964", @@ -224993,7 +225877,7 @@ { "id": "air_temperature_observations_in_the_arctic_1979-2004", "title": "Air Temperature Observations in the Arctic 1979-2004", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-01-01", "end_date": "2005-12-01", "bbox": "-180, 14.5, 180, 90", @@ -225006,7 +225890,7 @@ { "id": "air_temperature_observations_in_the_arctic_1979-2004", "title": "Air Temperature Observations in the Arctic 1979-2004", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1979-01-01", "end_date": "2005-12-01", "bbox": "-180, 14.5, 180, 90", @@ -225097,7 +225981,7 @@ { "id": "alan---nature-sustainability_1.0", "title": "Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2024-01-01", "end_date": "2024-01-01", "bbox": "5.95587, 45.81802, 10.49203, 47.80838", @@ -225110,7 +225994,7 @@ { "id": "alan---nature-sustainability_1.0", "title": "Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2024-01-01", "end_date": "2024-01-01", "bbox": "5.95587, 45.81802, 10.49203, 47.80838", @@ -225149,7 +226033,7 @@ { "id": "alaskan_air_ground_snow_and_soil_temperatures__1998-2005", "title": "Alaskan Air Ground Snow and Soil Temperatures 1998-2005", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-08-29", "end_date": "2007-11-30", "bbox": "-164.761, 64.919, -148.6, 70.439", @@ -225162,7 +226046,7 @@ { "id": "alaskan_air_ground_snow_and_soil_temperatures__1998-2005", "title": "Alaskan Air Ground Snow and Soil Temperatures 1998-2005", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1998-08-29", "end_date": "2007-11-30", "bbox": "-164.761, 64.919, -148.6, 70.439", @@ -225214,7 +226098,7 @@ { "id": "allADCP_GB", "title": "Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1995-04-25", "end_date": "1995-06-16", "bbox": "-68, 40.5, -67, 41.5", @@ -225227,7 +226111,7 @@ { "id": "allADCP_GB", "title": "Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-04-25", "end_date": "1995-06-16", "bbox": "-68, 40.5, -67, 41.5", @@ -225591,7 +226475,7 @@ { "id": "amsua15sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-08-03", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -225604,7 +226488,7 @@ { "id": "amsua15sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "1998-08-03", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -225643,7 +226527,7 @@ { "id": "amsua17sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-21", "end_date": "2003-12-13", "bbox": "-180, -89.575, 180, 89.629", @@ -225656,7 +226540,7 @@ { "id": "amsua17sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-21", "end_date": "2003-12-13", "bbox": "-180, -89.575, 180, 89.629", @@ -225955,7 +226839,7 @@ { "id": "ascatcpex_1", "title": "Advanced Scatterometer (ASCAT) CPEX", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-05-24", "end_date": "2017-07-16", "bbox": "160.241, 3.9062, -25.0958, 42.5176", @@ -225968,7 +226852,7 @@ { "id": "ascatcpex_1", "title": "Advanced Scatterometer (ASCAT) CPEX", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2017-05-24", "end_date": "2017-07-16", "bbox": "160.241, 3.9062, -25.0958, 42.5176", @@ -226046,7 +226930,7 @@ { "id": "aster_1", "title": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-10-08", "end_date": "", "bbox": "-180, -90, 180, -53", @@ -226059,7 +226943,7 @@ { "id": "aster_1", "title": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2000-10-08", "end_date": "", "bbox": "-180, -90, 180, -53", @@ -226202,7 +227086,7 @@ { "id": "atrs", "title": "Airborne Coherant Radar Sounding Data", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -70", @@ -226215,7 +227099,7 @@ { "id": "atrs", "title": "Airborne Coherant Radar Sounding Data", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -70", @@ -226683,7 +227567,7 @@ { "id": "avapsimpacts_1", "title": "Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2020-01-12", "end_date": "2023-02-28", "bbox": "-77.815, 33.54, -65.44, 44.17", @@ -226696,7 +227580,7 @@ { "id": "avapsimpacts_1", "title": "Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-12", "end_date": "2023-02-28", "bbox": "-77.815, 33.54, -65.44, 44.17", @@ -227840,7 +228724,7 @@ { "id": "brownbay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-02-01", "end_date": "2000-02-05", "bbox": "110.54, -66.281, 110.548, -66.279", @@ -227853,7 +228737,7 @@ { "id": "brownbay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1997-02-01", "end_date": "2000-02-05", "bbox": "110.54, -66.281, 110.548, -66.279", @@ -228763,7 +229647,7 @@ { "id": "capeden_sat_ikonos_1", "title": "A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-26", "end_date": "2001-01-31", "bbox": "142.5153, -67.0697, 143.03, -66.9478", @@ -228776,7 +229660,7 @@ { "id": "capeden_sat_ikonos_1", "title": "A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2001-01-26", "end_date": "2001-01-31", "bbox": "142.5153, -67.0697, 143.03, -66.9478", @@ -229465,7 +230349,7 @@ { "id": "climate_temps_1", "title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1901-01-01", "end_date": "2002-12-31", "bbox": "-180, -80, 180, -17", @@ -229478,7 +230362,7 @@ { "id": "climate_temps_1", "title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1901-01-01", "end_date": "2002-12-31", "bbox": "-180, -80, 180, -17", @@ -230115,7 +230999,7 @@ { "id": "darling_sst_00", "title": "2000 Seawater Temperatures at the Darling Marine Center", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-01-01", "end_date": "2000-12-31", "bbox": "-71.31, 42.85, -66.74, 47.67", @@ -230128,7 +231012,7 @@ { "id": "darling_sst_00", "title": "2000 Seawater Temperatures at the Darling Marine Center", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2000-01-01", "end_date": "2000-12-31", "bbox": "-71.31, 42.85, -66.74, 47.67", @@ -230167,7 +231051,7 @@ { "id": "darling_sst_82-93", "title": "1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1982-03-01", "end_date": "1993-12-31", "bbox": "-71.31, 42.85, -66.74, 47.67", @@ -230180,7 +231064,7 @@ { "id": "darling_sst_82-93", "title": "1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1982-03-01", "end_date": "1993-12-31", "bbox": "-71.31, 42.85, -66.74, 47.67", @@ -231103,7 +231987,7 @@ { "id": "doi:10.25921/sta3-3b95_Not Applicable", "title": "2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-09-08", "end_date": "2015-05-08", "bbox": "-84.4, 27.7, -83.4, 29.7", @@ -231116,7 +232000,7 @@ { "id": "doi:10.25921/sta3-3b95_Not Applicable", "title": "2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2014-09-08", "end_date": "2015-05-08", "bbox": "-84.4, 27.7, -83.4, 29.7", @@ -231194,7 +232078,7 @@ { "id": "doi:10.7289/V5862DPB_Not Applicable", "title": "Airborne Magnetic Trackline Database", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1958-12-06", "end_date": "2011-02-26", "bbox": "-180, -90, 180, 90", @@ -231207,7 +232091,7 @@ { "id": "doi:10.7289/V5862DPB_Not Applicable", "title": "Airborne Magnetic Trackline Database", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1958-12-06", "end_date": "2011-02-26", "bbox": "-180, -90, 180, 90", @@ -233131,7 +234015,7 @@ { "id": "f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d", "title": "3 year daily average solar exposure map Mali 3Km GRAS September 2008-2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-15, 8, 5, 28", @@ -233144,7 +234028,7 @@ { "id": "f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d", "title": "3 year daily average solar exposure map Mali 3Km GRAS September 2008-2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-15, 8, 5, 28", @@ -233846,7 +234730,7 @@ { "id": "fife_AF_dtrnd_nae_3_1", "title": "Aircraft Flux-Detrended: NRCC (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-06-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -233859,7 +234743,7 @@ { "id": "fife_AF_dtrnd_nae_3_1", "title": "Aircraft Flux-Detrended: NRCC (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-06-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -233976,7 +234860,7 @@ { "id": "fife_AF_filtr_wyo_7_1", "title": "Aircraft Flux-Filtered: U of Wy. (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-08-11", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -233989,7 +234873,7 @@ { "id": "fife_AF_filtr_wyo_7_1", "title": "Aircraft Flux-Filtered: U of Wy. (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-08-11", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -234028,7 +234912,7 @@ { "id": "fife_AF_raw_ncar_11_1", "title": "Aircraft Flux-Raw: Univ. Col. (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-05-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -234041,7 +234925,7 @@ { "id": "fife_AF_raw_ncar_11_1", "title": "Aircraft Flux-Raw: Univ. Col. (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-05-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -234405,7 +235289,7 @@ { "id": "fife_hydrology_strm_15m_1_1", "title": "15 Minute Stream Flow Data: USGS (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1984-12-25", "end_date": "1988-03-04", "bbox": "-96.6, 39.1, -96.6, 39.1", @@ -234418,7 +235302,7 @@ { "id": "fife_hydrology_strm_15m_1_1", "title": "15 Minute Stream Flow Data: USGS (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1984-12-25", "end_date": "1988-03-04", "bbox": "-96.6, 39.1, -96.6, 39.1", @@ -235276,7 +236160,7 @@ { "id": "finnarp_aerosols", "title": "Aerosol measurements at ABOA / FINNARP 2009", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -235289,7 +236173,7 @@ { "id": "finnarp_aerosols", "title": "Aerosol measurements at ABOA / FINNARP 2009", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -235432,7 +236316,7 @@ { "id": "foraging_trip_duration_BI_1", "title": "Adelie penguin foraging trip duration, Bechervaise Island, Mawson", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1991-10-01", "end_date": "2005-02-01", "bbox": "62.8055, -67.5916, 62.825, -67.5861", @@ -235445,7 +236329,7 @@ { "id": "foraging_trip_duration_BI_1", "title": "Adelie penguin foraging trip duration, Bechervaise Island, Mawson", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1991-10-01", "end_date": "2005-02-01", "bbox": "62.8055, -67.5916, 62.825, -67.5861", @@ -236719,7 +237603,7 @@ { "id": "geodata_0290", "title": "Administrative Boundaries - First Level (ESRI)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-01-01", "end_date": "1998-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -236732,7 +237616,7 @@ { "id": "geodata_0290", "title": "Administrative Boundaries - First Level (ESRI)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1998-01-01", "end_date": "1998-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -241438,7 +242322,7 @@ { "id": "gomc_156", "title": "Adopt-a-Tide Pool", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1990-01-01", "end_date": "", "bbox": "-70.923, 42.489, -70.763, 42.577", @@ -241451,7 +242335,7 @@ { "id": "gomc_156", "title": "Adopt-a-Tide Pool", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-01-01", "end_date": "", "bbox": "-70.923, 42.489, -70.763, 42.577", @@ -241646,7 +242530,7 @@ { "id": "gov.noaa.ncdc:C01599_beta6", "title": "Adaptive Ecosystem Climatology Beta 6 Satellite Climatology", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "2012-12-31", "bbox": "-135, 22.9276, -62.987, 53", @@ -241659,7 +242543,7 @@ { "id": "gov.noaa.ncdc:C01599_beta6", "title": "Adaptive Ecosystem Climatology Beta 6 Satellite Climatology", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1980-01-01", "end_date": "2012-12-31", "bbox": "-135, 22.9276, -62.987, 53", @@ -241893,7 +242777,7 @@ { "id": "gov.noaa.nodc:0000029_Not Applicable", "title": "1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-09-26", "end_date": "1995-05-26", "bbox": "-124.041667, 0.766667, -16.25, 46.263167", @@ -241906,7 +242790,7 @@ { "id": "gov.noaa.nodc:0000029_Not Applicable", "title": "1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1990-09-26", "end_date": "1995-05-26", "bbox": "-124.041667, 0.766667, -16.25, 46.263167", @@ -242530,7 +243414,7 @@ { "id": "gov.noaa.nodc:0000879_Not Applicable", "title": "Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2001-01-26", "end_date": "2001-05-18", "bbox": "-158.14, 19.27, -155.05, 21.37", @@ -242543,7 +243427,7 @@ { "id": "gov.noaa.nodc:0000879_Not Applicable", "title": "Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-26", "end_date": "2001-05-18", "bbox": "-158.14, 19.27, -155.05, 21.37", @@ -242569,7 +243453,7 @@ { "id": "gov.noaa.nodc:0000931_Not Applicable", "title": "Aerial surveys of ringed seals (Phoca hispida) on fast and pack ice in the central Beaufort Sea of Alaska, 1985-1987 and 1996-1999 (NCEI Accession 0000931)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1985-05-28", "end_date": "1999-06-04", "bbox": "-156.9983, 69.6517, -141.025, 71.865", @@ -242582,7 +243466,7 @@ { "id": "gov.noaa.nodc:0000931_Not Applicable", "title": "Aerial surveys of ringed seals (Phoca hispida) on fast and pack ice in the central Beaufort Sea of Alaska, 1985-1987 and 1996-1999 (NCEI Accession 0000931)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1985-05-28", "end_date": "1999-06-04", "bbox": "-156.9983, 69.6517, -141.025, 71.865", @@ -242907,7 +243791,7 @@ { "id": "gov.noaa.nodc:0002192_Not Applicable", "title": "A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-09-01", "end_date": "2002-08-25", "bbox": "-96.01, 23.49, -85.47, 29.38", @@ -242920,7 +243804,7 @@ { "id": "gov.noaa.nodc:0002192_Not Applicable", "title": "A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-09-01", "end_date": "2002-08-25", "bbox": "-96.01, 23.49, -85.47, 29.38", @@ -242933,7 +243817,7 @@ { "id": "gov.noaa.nodc:0002193_Not Applicable", "title": "A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-09-01", "end_date": "2002-08-01", "bbox": "-96, 23.47, -85.47, 29.33", @@ -242946,7 +243830,7 @@ { "id": "gov.noaa.nodc:0002193_Not Applicable", "title": "A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-09-01", "end_date": "2002-08-01", "bbox": "-96, 23.47, -85.47, 29.33", @@ -243375,7 +244259,7 @@ { "id": "gov.noaa.nodc:0058858_Not Applicable", "title": "Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2006-10-12", "end_date": "2008-04-15", "bbox": "-122.835, 47.769, -122.835, 47.769", @@ -243388,7 +244272,7 @@ { "id": "gov.noaa.nodc:0058858_Not Applicable", "title": "Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-10-12", "end_date": "2008-04-15", "bbox": "-122.835, 47.769, -122.835, 47.769", @@ -245364,7 +246248,7 @@ { "id": "gov.noaa.nodc:0125597_Not Applicable", "title": "Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-09-27", "end_date": "2016-02-25", "bbox": "-76.84, 26.491, -72.004, 26.516", @@ -245377,7 +246261,7 @@ { "id": "gov.noaa.nodc:0125597_Not Applicable", "title": "Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2004-09-27", "end_date": "2016-02-25", "bbox": "-76.84, 26.491, -72.004, 26.516", @@ -245715,7 +246599,7 @@ { "id": "gov.noaa.nodc:0148759_Not Applicable", "title": "AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-08-11", "end_date": "2016-02-20", "bbox": "-38.146, 66.329, -38.146, 66.329", @@ -245728,7 +246612,7 @@ { "id": "gov.noaa.nodc:0148759_Not Applicable", "title": "AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2009-08-11", "end_date": "2016-02-20", "bbox": "-38.146, 66.329, -38.146, 66.329", @@ -245819,7 +246703,7 @@ { "id": "gov.noaa.nodc:0156424_Not Applicable", "title": "Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1950-01-01", "end_date": "1996-12-31", "bbox": "-180, 58, 180, 90", @@ -245832,7 +246716,7 @@ { "id": "gov.noaa.nodc:0156424_Not Applicable", "title": "Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1950-01-01", "end_date": "1996-12-31", "bbox": "-180, 58, 180, 90", @@ -245845,7 +246729,7 @@ { "id": "gov.noaa.nodc:0156425_Not Applicable", "title": "Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1900-01-01", "end_date": "1998-12-31", "bbox": "-180, 45, 180, 90", @@ -245858,7 +246742,7 @@ { "id": "gov.noaa.nodc:0156425_Not Applicable", "title": "Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1900-01-01", "end_date": "1998-12-31", "bbox": "-180, 45, 180, 90", @@ -245884,7 +246768,7 @@ { "id": "gov.noaa.nodc:0156765_Not Applicable", "title": "Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-05-06", "end_date": "1996-08-30", "bbox": "-87.6, 29.6, -84.7, 30.6", @@ -245897,7 +246781,7 @@ { "id": "gov.noaa.nodc:0156765_Not Applicable", "title": "Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1994-05-06", "end_date": "1996-08-30", "bbox": "-87.6, 29.6, -84.7, 30.6", @@ -246014,7 +246898,7 @@ { "id": "gov.noaa.nodc:0159386_Not Applicable", "title": "Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2002-10-02", "end_date": "2002-10-04", "bbox": "-88.672, 22.203, -84.062, 26.433", @@ -246027,7 +246911,7 @@ { "id": "gov.noaa.nodc:0159386_Not Applicable", "title": "Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-10-02", "end_date": "2002-10-04", "bbox": "-88.672, 22.203, -84.062, 26.433", @@ -246040,7 +246924,7 @@ { "id": "gov.noaa.nodc:0159419_Not Applicable", "title": "ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2013-10-17", "end_date": "2013-10-20", "bbox": "-94.9828, 26.16133, -88, 29.69641", @@ -246053,7 +246937,7 @@ { "id": "gov.noaa.nodc:0159419_Not Applicable", "title": "ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2013-10-17", "end_date": "2013-10-20", "bbox": "-94.9828, 26.16133, -88, 29.69641", @@ -246118,7 +247002,7 @@ { "id": "gov.noaa.nodc:0162518_Not Applicable", "title": "ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2012-11-15", "end_date": "2012-11-17", "bbox": "-91.20748, 27.49168, -89, 29.0029", @@ -246131,7 +247015,7 @@ { "id": "gov.noaa.nodc:0162518_Not Applicable", "title": "ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-11-15", "end_date": "2012-11-17", "bbox": "-91.20748, 27.49168, -89, 29.0029", @@ -246196,7 +247080,7 @@ { "id": "gov.noaa.nodc:0163192_Not Applicable", "title": "A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-07-12", "end_date": "2005-07-27", "bbox": "-86.2279, 27.4432, -80.1996, 30.7692", @@ -246209,7 +247093,7 @@ { "id": "gov.noaa.nodc:0163192_Not Applicable", "title": "A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1998-07-12", "end_date": "2005-07-27", "bbox": "-86.2279, 27.4432, -80.1996, 30.7692", @@ -246755,7 +247639,7 @@ { "id": "gov.noaa.nodc:0172043_Not Applicable", "title": "ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2012-11-28", "end_date": "2012-12-19", "bbox": "-94.0863, 25.7961, -87.2228, 28.9733", @@ -246768,7 +247652,7 @@ { "id": "gov.noaa.nodc:0172043_Not Applicable", "title": "ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-11-28", "end_date": "2012-12-19", "bbox": "-94.0863, 25.7961, -87.2228, 28.9733", @@ -246781,7 +247665,7 @@ { "id": "gov.noaa.nodc:0172377_Not Applicable", "title": "Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2015-07-21", "end_date": "2016-08-05", "bbox": "-64.9199, 17.63764, -64.47889, 17.82709", @@ -246794,7 +247678,7 @@ { "id": "gov.noaa.nodc:0172377_Not Applicable", "title": "Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-07-21", "end_date": "2016-08-05", "bbox": "-64.9199, 17.63764, -64.47889, 17.82709", @@ -246872,7 +247756,7 @@ { "id": "gov.noaa.nodc:0175745_Not Applicable", "title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-07-07", "end_date": "2016-10-29", "bbox": "-51.5, -34.503, -44.5, -34.5", @@ -246885,7 +247769,7 @@ { "id": "gov.noaa.nodc:0175745_Not Applicable", "title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2011-07-07", "end_date": "2016-10-29", "bbox": "-51.5, -34.503, -44.5, -34.5", @@ -246898,7 +247782,7 @@ { "id": "gov.noaa.nodc:0175783_Not Applicable", "title": "Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1992-10-14", "end_date": "2016-12-28", "bbox": "27, -40, 30, -34", @@ -246911,7 +247795,7 @@ { "id": "gov.noaa.nodc:0175783_Not Applicable", "title": "Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1992-10-14", "end_date": "2016-12-28", "bbox": "27, -40, 30, -34", @@ -246924,7 +247808,7 @@ { "id": "gov.noaa.nodc:0175786_Not Applicable", "title": "Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1986-04-01", "end_date": "2017-06-27", "bbox": "-89.85889, 29.8917, -87.6955, 30.68067", @@ -246937,7 +247821,7 @@ { "id": "gov.noaa.nodc:0175786_Not Applicable", "title": "Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1986-04-01", "end_date": "2017-06-27", "bbox": "-89.85889, 29.8917, -87.6955, 30.68067", @@ -246989,7 +247873,7 @@ { "id": "gov.noaa.nodc:0185753_Not Applicable", "title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-09-01", "end_date": "2012-12-31", "bbox": "-84.5, 43.2, -79.8, 46.3", @@ -247002,7 +247886,7 @@ { "id": "gov.noaa.nodc:0185753_Not Applicable", "title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2006-09-01", "end_date": "2012-12-31", "bbox": "-84.5, 43.2, -79.8, 46.3", @@ -247015,7 +247899,7 @@ { "id": "gov.noaa.nodc:0186561_Not Applicable", "title": "2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-01-01", "end_date": "2003-12-31", "bbox": "-98, 25, -80, 31", @@ -247028,7 +247912,7 @@ { "id": "gov.noaa.nodc:0186561_Not Applicable", "title": "2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2003-01-01", "end_date": "2003-12-31", "bbox": "-98, 25, -80, 31", @@ -247314,7 +248198,7 @@ { "id": "gov.noaa.nodc:0209357_Not Applicable", "title": "A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-01-01", "end_date": "2020-03-01", "bbox": "-180, -90, 180, 90", @@ -247327,7 +248211,7 @@ { "id": "gov.noaa.nodc:0209357_Not Applicable", "title": "A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2000-01-01", "end_date": "2020-03-01", "bbox": "-180, -90, 180, 90", @@ -247340,7 +248224,7 @@ { "id": "gov.noaa.nodc:0210577_Not Applicable", "title": "Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2014-07-15", "end_date": "2018-11-11", "bbox": "-162, 11, -50, 43", @@ -247353,7 +248237,7 @@ { "id": "gov.noaa.nodc:0210577_Not Applicable", "title": "Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-07-15", "end_date": "2018-11-11", "bbox": "-162, 11, -50, 43", @@ -247418,7 +248302,7 @@ { "id": "gov.noaa.nodc:0221188_Not Applicable", "title": "3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2017-09-24", "end_date": "2017-09-24", "bbox": "-88.974, 28.932, -88.965, 28.944", @@ -247431,7 +248315,7 @@ { "id": "gov.noaa.nodc:0221188_Not Applicable", "title": "3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-09-24", "end_date": "2017-09-24", "bbox": "-88.974, 28.932, -88.965, 28.944", @@ -247522,7 +248406,7 @@ { "id": "gov.noaa.nodc:0231662_Not Applicable", "title": "ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2019-07-15", "end_date": "2019-07-15", "bbox": "-124.355093, 44.282964, -124.054485, 44.625023", @@ -247535,7 +248419,7 @@ { "id": "gov.noaa.nodc:0231662_Not Applicable", "title": "ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-07-15", "end_date": "2019-07-15", "bbox": "-124.355093, 44.282964, -124.054485, 44.625023", @@ -247691,7 +248575,7 @@ { "id": "gov.noaa.nodc:7000981_Not Applicable", "title": "A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1970-06-01", "end_date": "1970-07-01", "bbox": "-29.33, 50.01, -14.2, 55.56", @@ -247704,7 +248588,7 @@ { "id": "gov.noaa.nodc:7000981_Not Applicable", "title": "A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-06-01", "end_date": "1970-07-01", "bbox": "-29.33, 50.01, -14.2, 55.56", @@ -247821,7 +248705,7 @@ { "id": "gov.noaa.nodc:7200320_Not Applicable", "title": "AIR PRESSURE and Other Data from UNKNOWN PLATFORMS and Other Platforms from 1955-03-01 to 1970-08-13 (NCEI Accession 7200320)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1955-03-01", "end_date": "1970-08-13", "bbox": "-71.9, 29.4, 8.8, 65.6", @@ -247834,7 +248718,7 @@ { "id": "gov.noaa.nodc:7200320_Not Applicable", "title": "AIR PRESSURE and Other Data from UNKNOWN PLATFORMS and Other Platforms from 1955-03-01 to 1970-08-13 (NCEI Accession 7200320)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1955-03-01", "end_date": "1970-08-13", "bbox": "-71.9, 29.4, 8.8, 65.6", @@ -248159,7 +249043,7 @@ { "id": "gov.noaa.nodc:7601613_Not Applicable", "title": "AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1972-01-01", "end_date": "1974-06-30", "bbox": "-77, 37, -76, 39", @@ -248172,7 +249056,7 @@ { "id": "gov.noaa.nodc:7601613_Not Applicable", "title": "AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1972-01-01", "end_date": "1974-06-30", "bbox": "-77, 37, -76, 39", @@ -248276,7 +249160,7 @@ { "id": "gov.noaa.nodc:7700179_Not Applicable", "title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1919-09-29", "end_date": "1976-04-26", "bbox": "-60, 44, 48, 80.5", @@ -248289,7 +249173,7 @@ { "id": "gov.noaa.nodc:7700179_Not Applicable", "title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1919-09-29", "end_date": "1976-04-26", "bbox": "-60, 44, 48, 80.5", @@ -251279,7 +252163,7 @@ { "id": "gov.noaa.nodc:9300196_Not Applicable", "title": "Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1991-06-11", "end_date": "1993-03-22", "bbox": "-88, 17, -85, 22", @@ -251292,7 +252176,7 @@ { "id": "gov.noaa.nodc:9300196_Not Applicable", "title": "Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1991-06-11", "end_date": "1993-03-22", "bbox": "-88, 17, -85, 22", @@ -251864,7 +252748,7 @@ { "id": "gov.noaa.nodc:9600025_Not Applicable", "title": "AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1992-11-09", "end_date": "1993-02-24", "bbox": "158, -2, 158, -2", @@ -251877,7 +252761,7 @@ { "id": "gov.noaa.nodc:9600025_Not Applicable", "title": "AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1992-11-09", "end_date": "1993-02-24", "bbox": "158, -2, 158, -2", @@ -252384,7 +253268,7 @@ { "id": "gov.noaa.nodc:9900022_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-08-01", "end_date": "1998-12-31", "bbox": "-124.1, 44.6, -124, 44.8", @@ -252397,7 +253281,7 @@ { "id": "gov.noaa.nodc:9900022_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1998-08-01", "end_date": "1998-12-31", "bbox": "-124.1, 44.6, -124, 44.8", @@ -252462,7 +253346,7 @@ { "id": "gov.noaa.nodc:9900119_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-05-01", "end_date": "1999-06-30", "bbox": "-124, 44.6, -124, 44.6", @@ -252475,7 +253359,7 @@ { "id": "gov.noaa.nodc:9900119_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-05-01", "end_date": "1999-06-30", "bbox": "-124, 44.6, -124, 44.6", @@ -259638,7 +260522,7 @@ { "id": "joughin_0631973", "title": "Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "2009-12-31", "bbox": "-124.8, -80.8, -86.7, -73.9", @@ -259651,7 +260535,7 @@ { "id": "joughin_0631973", "title": "Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1980-01-01", "end_date": "2009-12-31", "bbox": "-124.8, -80.8, -86.7, -73.9", @@ -260821,7 +261705,7 @@ { "id": "latent-reserves-in-the-swiss-nfi_1.0", "title": "'Latent reserves' within the Swiss NFI", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "5.95587, 45.81802, 10.49203, 47.80838", @@ -260834,7 +261718,7 @@ { "id": "latent-reserves-in-the-swiss-nfi_1.0", "title": "'Latent reserves' within the Swiss NFI", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "5.95587, 45.81802, 10.49203, 47.80838", @@ -260886,7 +261770,7 @@ { "id": "law_dome_700yr_na_1", "title": "700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1301-01-01", "end_date": "1995-12-31", "bbox": "112.806946, -66.76972, 112.806946, -66.76972", @@ -260899,7 +261783,7 @@ { "id": "law_dome_700yr_na_1", "title": "700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1301-01-01", "end_date": "1995-12-31", "bbox": "112.806946, -66.76972, 112.806946, -66.76972", @@ -262654,7 +263538,7 @@ { "id": "macquarie_taspaws_grid_1", "title": "A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1974-01-01", "end_date": "2001-06-02", "bbox": "158.7322, -54.8011, 158.9781, -54.4714", @@ -262667,7 +263551,7 @@ { "id": "macquarie_taspaws_grid_1", "title": "A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1974-01-01", "end_date": "2001-06-02", "bbox": "158.7322, -54.8011, 158.9781, -54.4714", @@ -263200,7 +264084,7 @@ { "id": "mendocino_mathison_peak_nff_sr", "title": "Airborne laser swath mapping (ALSM) data of the San Andreas fault", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-02-05", "end_date": "2003-02-11", "bbox": "-123.81387, 39.31092, -123.720085, 39.333496", @@ -263213,7 +264097,7 @@ { "id": "mendocino_mathison_peak_nff_sr", "title": "Airborne laser swath mapping (ALSM) data of the San Andreas fault", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2003-02-05", "end_date": "2003-02-11", "bbox": "-123.81387, 39.31092, -123.720085, 39.333496", @@ -264968,7 +265852,7 @@ { "id": "nsf0232042", "title": "Aeromagnetic and gravity data of the Central Transantarctic Mountains", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2003-12-27", "end_date": "2004-01-25", "bbox": "139.27539, -83.95234, 170.21844, -82.35733", @@ -264981,7 +265865,7 @@ { "id": "nsf0232042", "title": "Aeromagnetic and gravity data of the Central Transantarctic Mountains", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-12-27", "end_date": "2004-01-25", "bbox": "139.27539, -83.95234, 170.21844, -82.35733", @@ -265124,7 +266008,7 @@ { "id": "nwrc_amphibianslowermiss", "title": "A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-09-05", "end_date": "1999-12-05", "bbox": "-91.95, 31.15, -91.25, 32.4333", @@ -265137,7 +266021,7 @@ { "id": "nwrc_amphibianslowermiss", "title": "A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1999-09-05", "end_date": "1999-12-05", "bbox": "-91.95, 31.15, -91.25, 32.4333", @@ -265163,7 +266047,7 @@ { "id": "obrienbay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1997-03-31", "end_date": "1997-03-31", "bbox": "110.516, -66.297, 110.54, -66.293", @@ -265176,7 +266060,7 @@ { "id": "obrienbay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-03-31", "end_date": "1997-03-31", "bbox": "110.516, -66.297, 110.54, -66.293", @@ -265397,7 +266281,7 @@ { "id": "oxygen-isotopes-plateau-1984_1", "title": "7 year oxygen isotope results from samples taken on Antarctic Plateau traverse, 1984", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1978-01-01", "end_date": "1984-12-31", "bbox": "100, -75, 130, -65", @@ -265410,7 +266294,7 @@ { "id": "oxygen-isotopes-plateau-1984_1", "title": "7 year oxygen isotope results from samples taken on Antarctic Plateau traverse, 1984", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1978-01-01", "end_date": "1984-12-31", "bbox": "100, -75, 130, -65", @@ -265566,7 +266450,7 @@ { "id": "pfynwaldgasexchange_1.0", "title": "2013-2020 gas exchange at Pfynwald", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2021-01-01", "end_date": "2021-01-01", "bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564", @@ -265579,7 +266463,7 @@ { "id": "pfynwaldgasexchange_1.0", "title": "2013-2020 gas exchange at Pfynwald", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-01-01", "end_date": "2021-01-01", "bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564", @@ -268569,7 +269453,7 @@ { "id": "scarmarbin_1647", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -268582,7 +269466,7 @@ { "id": "scarmarbin_1647", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -268647,7 +269531,7 @@ { "id": "scarmarbin_1651", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1979-01-01", "end_date": "1986-01-01", "bbox": "-180, -90, 180, 90", @@ -268660,7 +269544,7 @@ { "id": "scarmarbin_1651", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-01-01", "end_date": "1986-01-01", "bbox": "-180, -90, 180, 90", @@ -268725,7 +269609,7 @@ { "id": "scarmarbin_1806", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997).", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -268738,7 +269622,7 @@ { "id": "scarmarbin_1806", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997).", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -268803,7 +269687,7 @@ { "id": "scarmarbin_987", "title": "A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection)", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -268816,7 +269700,7 @@ { "id": "scarmarbin_987", "title": "A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection)", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -269336,7 +270220,7 @@ { "id": "shirley_dem_1", "title": "A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2005-01-01", "end_date": "2007-05-01", "bbox": "110.473, -66.287, 110.509, -66.277", @@ -269349,7 +270233,7 @@ { "id": "shirley_dem_1", "title": "A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-01-01", "end_date": "2007-05-01", "bbox": "110.473, -66.287, 110.509, -66.277", @@ -269518,7 +270402,7 @@ { "id": "slow-snow-compression_1.0", "title": "A grain-size driven transition in the deformation mechanism in slow snow compression", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2023-01-01", "end_date": "2023-01-01", "bbox": "9.8417222, 46.8095077, 9.8417222, 46.8095077", @@ -269531,7 +270415,7 @@ { "id": "slow-snow-compression_1.0", "title": "A grain-size driven transition in the deformation mechanism in slow snow compression", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2023-01-01", "end_date": "2023-01-01", "bbox": "9.8417222, 46.8095077, 9.8417222, 46.8095077", @@ -270207,7 +271091,7 @@ { "id": "sowers_0739491", "title": "2008 South Pole Firn Air Methane Isotopes", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2008-12-01", "end_date": "2009-01-31", "bbox": "-180, -90, 180, 90", @@ -270220,7 +271104,7 @@ { "id": "sowers_0739491", "title": "2008 South Pole Firn Air Methane Isotopes", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2008-12-01", "end_date": "2009-01-31", "bbox": "-180, -90, 180, 90", @@ -275576,7 +276460,7 @@ { "id": "usgs_npwrc_graywolves_Version 30APR2001", "title": "Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-168, 43.5, -75, 55", @@ -275589,7 +276473,7 @@ { "id": "usgs_npwrc_graywolves_Version 30APR2001", "title": "Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-168, 43.5, -75, 55", @@ -275888,7 +276772,7 @@ { "id": "vanderford_data_1983_85_1", "title": "Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1983-01-01", "end_date": "1985-12-31", "bbox": "108, -67.5, 113, -66", @@ -275901,7 +276785,7 @@ { "id": "vanderford_data_1983_85_1", "title": "Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1983-01-01", "end_date": "1985-12-31", "bbox": "108, -67.5, 113, -66", @@ -276694,7 +277578,7 @@ { "id": "winston_bathy_1", "title": "A bathymetric survey of Winston Lagoon", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1987-01-09", "end_date": "1987-01-14", "bbox": "73.23557, -53.20274, 73.83911, -52.95006", @@ -276707,7 +277591,7 @@ { "id": "winston_bathy_1", "title": "A bathymetric survey of Winston Lagoon", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-01-09", "end_date": "1987-01-14", "bbox": "73.23557, -53.20274, 73.83911, -52.95006", @@ -276837,7 +277721,7 @@ { "id": "wygisc_wolphoyo", "title": "Aerial Photos for Crazy Woman and Clear Creek Watersheds", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-107, 44, -106.36, 44.75", @@ -276850,7 +277734,7 @@ { "id": "wygisc_wolphoyo", "title": "Aerial Photos for Crazy Woman and Clear Creek Watersheds", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-107, 44, -106.36, 44.75", diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index 0838c0fbc2..7d6ff98231 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -3130,23 +3130,23 @@ ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564427300-NSIDC_ECS.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL07QL_006 ATLAS/ICESat-2 L3A Sea Ice Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL07QL_006 ATLAS/ICESat-2 L3A Sea Ice Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-11-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL08QL_006 ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL08QL_006 ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-11-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL09QL_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL09QL_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006 NSIDC_ECS STAC Catalog 2024-11-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL10QL_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed. ATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary +ATL10QL_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006 NSIDC_ECS STAC Catalog 2024-11-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed. ATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary -ATL13QL_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary +ATL13QL_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006 NSIDC_ECS STAC Catalog 2024-11-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_ECS STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684163-NSIDC_ECS.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary @@ -5637,7 +5637,7 @@ DeltaX_L1B_UAVSAR_WaterLevels_1979_1.1 Delta-X: UAVSAR L1B Interferometric Produ DeltaX_L1_AVIRIS_Radiance_1987_1 Delta-X: AVIRIS-NG L1B Spectral Radiance Products, MRD, Louisiana, 2021 ORNL_CLOUD STAC Catalog 2021-03-27 2021-09-25 -91.59, 29.06, -89.68, 29.85 https://cmr.earthdata.nasa.gov/search/concepts/C2256471725-ORNL_CLOUD.umm_json This dataset provides Level 1B (L1B) radiance products from NASA's Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) instrument acquired over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA during two deployments; spring and fall of 2021. All flights were flown on a Dynamic Aviation King Air B200. There are a combined 200 total flight lines for the spring and fall 2021 deployments; spring 2021 had 75 flight lines, fall 2021 had 175 flight lines. AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Level 1B data are orthorectified calibrated radiance values in units of spectral radiance in which raw digital numbers (DNs) are translated to units of radiant intensity measured at the sensor. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution, dependent on aircraft altitude. Additional flight line files include band information of observational geometry and illumination parameters, as well as geographic pixel locations and elevation. These L1B data are provided in ENVI file format. AVIRIS-NG Cal/Val, Level 2 and Level 3 products for the Pre-Delta-X and Delta-X missions are provided in related datasets. proprietary DeltaX_L1_UAVSAR_SLC_Stack_1984_1.1 Delta-X: UAVSAR L1 Single Look Complex (SLC) Stack Products, MRD, Louisiana, 2021 ORNL_CLOUD STAC Catalog 2021-03-27 2021-09-13 -91.59, 29.01, -90.13, 29.78 https://cmr.earthdata.nasa.gov/search/concepts/C2256889308-ORNL_CLOUD.umm_json This dataset contains UAVSAR Level 1 (L1) Single Look Complex (SLC) stack products for Delta-X flight lines acquired during 2021-03-27 to 2021-04-18 (spring) and 2021-09-03 to 2021-09-13 (fall). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. The study area includes the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Repeat pass interferometric synthetic aperture (InSAR) data are a standard UAVSAR product delivered by the UAVSAR processing team. These repeat pass SLC stack co-registered time series data were used as the underlying data for higher level data products. These higher level products provide a time series of water level changes and address a goal of the Delta-X campaign to measure water-level changes throughout wetlands. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. These L1 data contain slant range single look complex (SLC), latitude/longitude/height, look vector, doppler, and metadata files. The data are provided in SLC stack format (*.slc) with associated annotation (*.ann), latitude-longitude-height (*.llh), look vector (*.lkv), and Doppler centroid-slant range (*.dop) files. The single look complex (SLC) stacks are in the HH, HV, VH, and VV polarizations. The same area was sampled at approximately 30-minute intervals. The SLCs are not corrected for residual baseline (BU). proprietary DeltaX_L1b_AirSWOT_1996_1.1 Delta-X: AirSWOT Level 1B Interferogram Products in Radar Coordinates, 2021 ORNL_CLOUD STAC Catalog 2021-03-26 2021-09-12 -91.54, 29.07, -90.58, 29.8 https://cmr.earthdata.nasa.gov/search/concepts/C2428617287-ORNL_CLOUD.umm_json This dataset contains AirSWOT interferogram products collected during the 2021 Delta-X Campaign over the Atchafalaya and Terrebonne Basins of the Mississippi River Delta, Louisiana, USA from 2021-03-26 to 2021-04-18 (Spring) and 2021-08-21 to 2021-09-12 (Fall). AirSWOT uses near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. AirSWOT elevation data is useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions. The AirSWOT Level 1B (L1B) data products represent interferogram data in the radar coordinate system, not in georeferenced map coordinates. This is an earlier stage of data processing which is used to generate the later Level 2 and Level 3 data products which will contain georeferenced water heights and water height profiles for river channels in each basin. The data are provided in binary and text file formats. proprietary -DeltaX_L2A_AVIRIS-NG_BRDF_V2_2139_2 Delta-X: AVIRIS-NG L2B BRDF-Adjusted Surface Reflectance, MRD, LA, 2021, V2 ORNL_CLOUD STAC Catalog 2021-03-27 2021-09-25 -91.59, 29.05, -89.67, 29.85 https://cmr.earthdata.nasa.gov/search/concepts/C2707162636-ORNL_CLOUD.umm_json This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) from 2021-03-27 to 2021-04-06 and 2021-08-18 to 2021-09-25. Reflectance data are provided as file sets for each flight line. In addition, ten files of mosaicked flight lines, by time period and over four locations (labeled Terre, Atcha, TerreEast, and Bara), are included. Files are presented as compressed (*.zip) files, containing binary ENVI image and header files. Only land pixels were corrected and mask files for the mosaic file coverage showing presence/absence of water are also included. For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise. proprietary +DeltaX_L2A_AVIRIS-NG_BRDF_V3_2355_3 Delta-X: AVIRIS-NG BRDF-Adjusted Surface Reflectance, MRD, LA, 2021, V3 ORNL_CLOUD STAC Catalog 2021-03-27 2021-09-25 -91.59, 29.05, -89.07, 30.23 https://cmr.earthdata.nasa.gov/search/concepts/C3397061771-ORNL_CLOUD.umm_json This data provides AVIRIS-NG Bidirectional Reflectance Distribution Function (BRDF) and sunglint-corrected surface spectral reflectance images over the Atchafalaya and Terrebonne basins of the Mississippi River Delta (MRD) of coastal Louisiana, USA. Flights were acquired during the Spring and Fall 2021 deployments of the Delta-X campaign. The imagery was acquired by the Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) from 2021-03-27 to 2021-04-06 and 2021-08-18 to 2021-09-25. Reflectance data are provided for each flight line. In addition, ten files of mosaicked flight lines, by time period and over four locations (labeled Terre, Atcha, TerreEast, and Bara), are included. Data are provided as binary ENVI image and header files. Only land pixels were corrected; mask files for the mosaic file coverage showing presence/absence of water and clouds are also included. For the Delta-X mission, these data serve to better understand rates of soil erosion, accretion, and creation in the delta system, with the goal of building better models of how river deltas will behave under relative sea level rise. proprietary DeltaX_L2_AVIRIS_Reflectance_1988_1 Delta-X: AVIRIS-NG L2 Surface Reflectance, MRD Louisiana, 2021 ORNL_CLOUD STAC Catalog 2021-03-27 2021-09-25 -91.64, 29.02, -89.59, 29.85 https://cmr.earthdata.nasa.gov/search/concepts/C2430019879-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) atmospherically corrected surface reflectance data acquired from NASA's Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over regions of interest in the Atchafalaya and Terrebonne basins on the southern coast of Louisiana, United States. Data were collected as part of the Delta-X Spring and Fall 2021 deployments that occurred from 2021-03-27 to 2021-04-06 and from 2021-08-18 to 2021-08-25. Additionally, L2 data from flights flown specifically to capture the Significant Event of Hurricane Ida are provided. This includes 56 files from flights conducted following Hurricane Ida from 2021-09-23 to 2021-09-25. Hurricane Ida made landfall over this region on 2021-08-29. AVIRIS-NG is a pushbroom spectral mapping system with a high signal-to-noise ratio (SNR) designed for high performance imaging spectroscopy. AVIRIS-NG measures the wavelength range from 380 nm to 2510 nm with 5-nm sampling resolution. For this dataset, spatial resolution varies from 3.8-5.4 meters. For this campaign, the AVIRIS-NG instrument was deployed on the Dynamic Aviation King Air B200 platform. This dataset represents one part of a multisensor airborne sampling campaign conducted by different aircraft teams for the Delta-X Campaign. Data are provided in ENVI file format. proprietary DeltaX_L2_AirSWOT_WaterElev_V3_2350_3 Delta-X: AirSWOT L2 Geocoded Water Surface Elevation, MRD, LA, 2021, V3 ORNL_CLOUD STAC Catalog 2021-03-26 2021-09-12 -91.6, 28.98, -90.21, 29.86 https://cmr.earthdata.nasa.gov/search/concepts/C3235809207-ORNL_CLOUD.umm_json This dataset contains Level 2 (L2) AirSWOT geocoded products, including estimated water surface elevation. The AirSWOT instrument is a Ka-band interferometer and for this study is flown on the King Air B200 platform. Data were collected during the DeltaX airborne campaign over the Atchafalaya and Terrebonne basins of the Mississippi River Delta, Louisiana, USA. Flights occurred during the Delta-X Spring 2021 deployment from 2021-03-26 to 2021-04-18 and the Delta-X Fall 2021 deployment from 2021-08-21 to 2021-09-12. AirSWOT is capable of producing high resolution (3.6 m) digital elevation models over land and water bodies using near-nadir wide-swath Ka-band radar interferometry to measure water-surface elevation and produce continuous gridded elevation data. The instrument includes six antennas that form multiple baseline pairs for along-track and across-track interferometry. AirSWOT elevation data are useful for calibrating elevation and slopes along the main channels, as well as tying observations to open ocean tidal conditions and is an airborne calibration and validation instrument for the Surface Water and Ocean Topography (SWOT) satellite. This Version 3 dataset provides updated data files due to an updated Calumet survey that changed the water level by 0.138 m. This resulted in all the AirSWOT water levels changing by that same amount. For these L2 products, only the estimated water surface elevation in respect to the WGS84 ellipsoid surface, and estimated height above the NAVD88 (GEOID12B) vertical datum files changed. Note that data acquired on September 1 and September 5, 2021 do not meet the expected MAE in-situ comparison and should be used with caution. This dataset contains cloud optimized GeoTIFF rasters in UTM map coordinates for each flight line. In addition, a text file provides basic metadata, including flight line ID, start and end UTC times of data acquisition, processor version number, and the date and time of different processing stages. proprietary DeltaX_L2_UAVSAR_WaterLevels_2057_1.1 Delta-X: UAVSAR L2 Interferometric Products, MRD, Louisiana, 2021 ORNL_CLOUD STAC Catalog 2021-03-27 2021-09-13 -91.59, 29.01, -90.13, 29.78 https://cmr.earthdata.nasa.gov/search/concepts/C2428388674-ORNL_CLOUD.umm_json This dataset contains georeferenced UAVSAR Level 2 (L2) interferometric products for Delta-X flight lines acquired during the spring (2021-03-27 to 2021-04-18) and fall (2021-09-03 to 2021-09-13) deployments. This dataset provides water-level change observations throughout wetlands of the Atchafalaya and Terrebonne Basins, in Southern Louisiana, USA, within the Mississippi River Delta (MRD), and it may be used to generate time series analysis. The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. Water surface elevations were measured on multiple flights at 30-minute intervals. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. The data include interferogram phase, interferogram amplitude, unwrapped interferogram phase, and coherence products. A series of quality assurance masks of troposphere-induced phase delay regions were generated for all SAR acquisition dates using a weather feature matching algorithm. Geometry files for each flight line per field campaign with latitude, longitude, height and incidence angle information are also included. The data are provided in ENVI format. proprietary @@ -8955,8 +8955,8 @@ KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air t KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity ALL STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary KOPRI-KPDC-00000621_1 Soil and Fresh water samples of the Antarctic Jang Bogo Station from Terra Nova Bay collected in 2016 AMD_KOPRI STAC Catalog 2016-01-07 2016-02-21 164.192056, -74.633361, 164.23725, -74.612056 https://cmr.earthdata.nasa.gov/search/concepts/C2244300323-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and water samples of the Antarctic Jang Bogo Station from Terra Nova Bay in Antarctica Investigation to the terrestrial biodiversity in Terra Nova Bay for the monitoring by environment change proprietary KOPRI-KPDC-00000622_1 Sampling activity for identification between biotic (ciliate) and abiotic data from Barton Peninsular in Antarctica during the summer season in 2015/2016. AMD_KOPRI STAC Catalog 2015-12-04 2015-12-18 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300508-AMD_KOPRI.umm_json Identification of ciliate biota and environmental data of habitats from Antarctica (Barton Peninsular) Identification of the relationship between biotic sample and abiotic data proprietary -KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 ALL STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary +KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 ALL STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary KOPRI-KPDC-00000624_1 Lichen samples from South Shetland Islands collected in 2016 AMD_KOPRI STAC Catalog 2016-02-01 2016-02-21 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300624-AMD_KOPRI.umm_json Lichen samples from Barton Peninsular collected in 2016 Ecophysiological study of lichen proprietary KOPRI-KPDC-00000625_2 Climate Measurement Around the King Sejong Station, Antarctica in 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305947-AMD_KOPRI.umm_json Meteorological observation was carried out at the King Sejong Station in 2016. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, horizontal global solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report. To understand weather phenomema and to monitor at Antarctic Peninsula proprietary KOPRI-KPDC-00000626_1 Soil samples of the Antarctic King Sejong Station from Barton Peninsular collected in 2016 AMD_KOPRI STAC Catalog 2016-02-19 2016-02-19 -58.788436, -62.224964, -58.786192, -62.22415 https://cmr.earthdata.nasa.gov/search/concepts/C2244300652-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change proprietary @@ -9039,8 +9039,8 @@ KOPRI-KPDC-00000703_1 Soil physical and chemical properties in Council, Alaska i KOPRI-KPDC-00000704_1 Soil physical and chemical properties in Cambridge Bay, Canada in 2012 AMD_KOPRI STAC Catalog 2012-06-28 2012-07-14 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244296757-AMD_KOPRI.umm_json Physical and chemical properties of soil which were collected in 2012 (before climate manipulation) were analyzed. Soils from 0-5 and 5-10 cm depths were sampled. To monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000705_1 Soil physical and chemical properties in Cambridge Bay, Canada in 2013 AMD_KOPRI STAC Catalog 2013-07-31 2013-08-09 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244296821-AMD_KOPRI.umm_json Physical and chemical properties of soil which were collected in 2013 after one year of climate manipulation were analyzed. Soils from 0-5 and 5-10 cm depths were sampled. To monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000706_1 Soil physical and chemical properties in Cambridge Bay, Canada in 2015 AMD_KOPRI STAC Catalog 2015-07-30 2015-08-07 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244296843-AMD_KOPRI.umm_json Physical and chemical properties of soil which were collected in 2015 after three years of climate manipulation were analyzed. Soils from organic and mineral layers were sampled. To monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation proprietary -KOPRI-KPDC-00000707_3 3D floorplan for CAD of Jang Bogo Station AMD_KOPRI STAC Catalog 2011-01-01 2011-01-31 164.228817, -74.624017, 164.228817, -74.624017 https://cmr.earthdata.nasa.gov/search/concepts/C2244296823-AMD_KOPRI.umm_json 3D floorplan for CAD of Jang Bogo Station To use for Numerical Weather Prediction Model proprietary KOPRI-KPDC-00000707_3 3D floorplan for CAD of Jang Bogo Station ALL STAC Catalog 2011-01-01 2011-01-31 164.228817, -74.624017, 164.228817, -74.624017 https://cmr.earthdata.nasa.gov/search/concepts/C2244296823-AMD_KOPRI.umm_json 3D floorplan for CAD of Jang Bogo Station To use for Numerical Weather Prediction Model proprietary +KOPRI-KPDC-00000707_3 3D floorplan for CAD of Jang Bogo Station AMD_KOPRI STAC Catalog 2011-01-01 2011-01-31 164.228817, -74.624017, 164.228817, -74.624017 https://cmr.earthdata.nasa.gov/search/concepts/C2244296823-AMD_KOPRI.umm_json 3D floorplan for CAD of Jang Bogo Station To use for Numerical Weather Prediction Model proprietary KOPRI-KPDC-00000708_1 Multiprotein-bridging factor 1c-like gene sequence from an Antarctic moss Polytrichastrum alpinum AMD_KOPRI STAC Catalog 2017-03-03 2017-03-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244297364-AMD_KOPRI.umm_json PaMBF1c (Multiprotein-bridging factor 1c-like) gene considered as an abiotic stimulus related genes from an Antarctic moss Polytrichastrum alpinum Investigation of molecular adaptation mechanism of the Antarcic moss to Antarctic environment proprietary KOPRI-KPDC-00000709_1 AMIGOS data in the Drygalski Ice Tongue, 2012 AMD_KOPRI STAC Catalog 2012-01-31 2012-12-31 164.294346, -75.412399, 165.17164, -75.348164 https://cmr.earthdata.nasa.gov/search/concepts/C2244295101-AMD_KOPRI.umm_json GPS, camera, and weather (air temperature, humidity, pressure, wind speed, wind direction) measurements from the AMIGOS systems in the Drygalski Ice Tongue Monitoring the movement and environmental change of Drygalski Ice Tongue proprietary KOPRI-KPDC-00000710_1 Hydro-Carbon Hydrate Accumulations in the Okhotsk Sea III AMD_KOPRI STAC Catalog 2006-05-24 2006-06-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295224-AMD_KOPRI.umm_json We had very remarkable results from the CHAOS-1 (2003) and CHAOS-2 (2005) project; lots of gas flares in the water column, many gas venting structures on the seafloor, gas hydrate samples including massive gas hydrate chunk (about 45 cm thick) near the seafloor, and gas hydrate-related structures in deep sub-bottom depth. These results encourage us to continue and expend the CHAOS project. Since the previous expedition focused on the relatively small area where gas hydrate-related phenomena has been known to be active, the basic aim of the CHAOS-III expedition is to improve understanding on gas hydrate-related phenomena in the Sea of Okhotsk in terms of multidisciplinary areas including geology, chemistry, oceanology and biology. 1. Detection of new gas hydrate-related structures including gas flares and gas venting structures. 2. Definition of the boundaries of the gas hydrate province 3. Mapping of the seafloor expressions related with gas hydrates and gas seepages using side-scan sonar. 4. Recognition of size, shape, and morphology of gas seepages on the seafloor. 5. High-resolution seismic investigation to examine inner structures and the gas hydrate stability condition in gas hydrate-baring sediments in detail. 6. Detection of gas flares in the water column emitted from gas seepages. 7. Study on hydrated water and dissociated gas sample 8. Chemistry of gas, gas hydrate, hydrate-forming fluids and carbonates including isotopic analysis. 9. Determination of methane concentration in the water column. 10. Underway survey to understand distribution of methane and dioxide in surface water and its controlling factor. 11. Detailed investigation of marine sedimentological environment in the gas hydrate area 12. Mechanism of formation-dissociation for gas-hydrates. 13. Interrelation of methane fluxes and mercury 14. Organic geochemical information related to the origin and composition of sedimentary organic matter. 15. Identification of biomarkers of microorganisms associated methane cycle. 16. Understanding of the composition of microbial community in gas hydrate environment proprietary @@ -9056,10 +9056,10 @@ KOPRI-KPDC-00000719_1 Seawater for dissolved organic carbon AMD_KOPRI STAC Catal KOPRI-KPDC-00000720_1 Biogeochemical data of seawater and sediment AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295503-AMD_KOPRI.umm_json Biogeochemical data of seawater and sediment proprietary KOPRI-KPDC-00000721_1 Lichen samples from South Shetland Islands collected in 2014 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -64.083333, -64.766667, -64.083333, -64.766667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295558-AMD_KOPRI.umm_json Lichen samples from Barton Peninsular collected in 2014. Locality, habitat information for 1286 lichen samples Investigation to diversity, morphology, phylogeography and ecophysiology in lichen proprietary KOPRI-KPDC-00000722_1 Lichen samples from Punta Arenas in Chile collected in 2014 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -71.416667, -53.6, -71.416667, -53.6 https://cmr.earthdata.nasa.gov/search/concepts/C2244295591-AMD_KOPRI.umm_json Lichen samples from Chile collected in 2014. Locality, habitat information for 165 lichen samples Investigation to diversity, morphology and phylogeography in lichen proprietary -KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary -KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary +KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary +KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary KOPRI-KPDC-00000725_1 Water isotope composition in a GV7 3-m snow pit (2013-2014) AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295745-AMD_KOPRI.umm_json A 3 m snow pit was collected at GV7 (Antarctica) in the 2013-2014 summer season. Its water isotope composition (dD, d18O) was determined using cavity ringdown spectroscopy (PICARRO). To detect annual (seasonal) layering of snowpack. proprietary KOPRI-KPDC-00000726_1 NEEM project_ice core AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295831-AMD_KOPRI.umm_json We obtained ice cores after participating the North Greenland Eemian Ice Drilling program. We reconstruct the high-resolution ice record of a shift of mineral dust sources in response to climate transition between the Last Glacial Maximum(~25,000 yr BP) and Holocene(8,000 yr BP) by analyzing trace elements including rare earth elements from a Greenland NEEM ice core. proprietary KOPRI-KPDC-00000727_1 ARA05C BC AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296153-AMD_KOPRI.umm_json ARA05C BC proprietary @@ -9096,8 +9096,8 @@ KOPRI-KPDC-00000756_1 Gravity cores from Antarctic Weddell Sea(JV10-GC01) AMD_KO KOPRI-KPDC-00000757_1 Physical and chemical properties of soil cores from Council, Alaska in 2016 AMD_KOPRI STAC Catalog 2017-06-01 2017-09-20 -163.7, 64.85, -163.7, 64.85 https://cmr.earthdata.nasa.gov/search/concepts/C2244299727-AMD_KOPRI.umm_json Several soil physical and chemical properties (moisture content, bulk density, C and N content, etc.) were analyzed from soil samples acquired in tussock and inter-tussock areas in August. 2016. To use for the basic information in the laboratory incubation study and to understand the site characteristics proprietary KOPRI-KPDC-00000758_1 Crystal structure and functional characterization of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea strain 34H AMD_KOPRI STAC Catalog 2017-06-21 2017-06-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300669-AMD_KOPRI.umm_json Isoaspartyl dipeptidase (IadA) is an enzyme that catalyzes the hydrolysis of an isoaspartyl dipeptide-like moiety, which can be inappropriately formed in proteins, between the β-carboxyl group side chain of Asp and the amino group of the following amino acid. Here, we have determined the structures of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea, both ligand-free and that complexed with β-isoaspartyl lysine, at 1.85-Å and 2.33-Å resolution, respectively. In both structures, CpsIadA formed an octamer with two Zn ions in the active site. A structural comparison with Escherichia coli isoaspartyl dipeptidase (EcoIadA) revealed a major difference in the structure of the active site. For metal ion coordination, CpsIadA has a Glu166 residue in the active site, whereas EcoIadA has a post-translationally carbamylated-lysine 162 residue. Site-directed mutagenesis studies confirmed that the Glu166 residue is critical for CpsIadA enzymatic activity. This residue substitution from lysine to glutamate induces the protrusion of the β12-α8 loop into the active site to compensate for the loss of length of the side chain. In addition, the α3-β9 loop of CpsIadA adopts a different conformation compared to EcoIadA, which induces a change in the structure of the substrate-binding pocket. Despite CpsIadA having a different active-site residue composition and substrate-binding pocket, there is only a slight difference in CpsIadA substrate specificity compared with EcoIadA. Comparative sequence analysis classified IadA-containing bacteria and archaea into two groups based on the active-site residue composition, with Type I IadAs having a glutamate residue and Type II IadAs having a carbamylated-lysine residue. CpsIadA has maximal activity at pH 8±8.5 and 45ÊC, and was completely inactivated at 60ÊC. Despite being isolated from a psychrophilic bacteria, CpsIadA is thermostable probably owing to its octameric structure. This is the first conclusive description of the structure and properties of a Type I IadA. To determine the structures of an isoaspartyl dipeptidase IadA from a psychrophilic bacterium Colwellia psychrerythraea strain 34H (CpsIadA) in both the ligand-free form and that complexed with β-isoaspartyl lysine proprietary KOPRI-KPDC-00000759_1 X-ray diffraction data of EaEST AMD_KOPRI STAC Catalog 2016-04-03 2016-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300649-AMD_KOPRI.umm_json A novel microbial esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7, was identified and characterized. To our knowledge, this is the first report describing structural analysis and biochemical characterization of an esterase isolated from the genus Exiguobacterium. Crystal structure of EaEST, determined at a resolution of 1.9 Å, showed that the enzyme has a canonical α/β hydrolase fold with an α-helical cap domain and a catalytic triad consisting of Ser96, Asp220, and His248. Interestingly, the active site of the structure of EaEST is occupied by a peracetate molecule, which is the product of perhydrolysis of acetate. This result suggests that EaEST may have perhydrolase activity. The activity assay showed that EaEST has significant perhydrolase and esterase activity with respect to short-chain p-nitrophenyl esters (≤C8), naphthyl derivatives, phenyl acetate, and glyceryl tributyrate. However, the S96A single mutant had low esterase and perhydrolase activity. Moreover, the L30A mutant showed low levels of protein expression and solubility as well as preference for different substrates. On conducting an enantioselectivity analysis using R- and S-methyl-3-hydroxy-2-methylpropionate, a preference for R-enantiomers was observed. Surprisingly, immobilized EaEST was found to not only retain 200% of its initial activity after incubation for 1 h at 80°C, but also retained more than 60% of its initial activity after 20 cycles of reutilization. This research will serve as basis for future engineering of this esterase for biotechnological and industrial applications. Our goal was to identify a novel cold-active esterase from a polar microorganism. We identified and characterized a novel esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7. Further structural and functional analysis indicated that EaEST had dual activity of a perhydrolase and an esterase. It is known that perhydrolysis is a side activity of esterases and it may be useful in industrial and organic synthesis. Moreover, the peracetate-bound EaEST structure reported in our study provides the first snapshot of the peracetate binding mode, and a comparison of the structure of EaEST with that of PfEST (PDB code 3HI4) reveals a comprehensive structural basis for the conformational changes of this enzyme induced by binding of different substrates. proprietary -KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 AMD_KOPRI STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 ALL STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary +KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 AMD_KOPRI STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary KOPRI-KPDC-00000761_1 Comparison of diversity of ciliate between Barton peninsula in Antarctica and Korea using NGS technique. AMD_KOPRI STAC Catalog 2017-05-04 2017-06-18 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300615-AMD_KOPRI.umm_json Identification of ciliate diversity from Korea and Antarctica (Barton Peninsular) Comparison of both data to know the specific ciliate in Antarctica proprietary KOPRI-KPDC-00000762_1 Greenland NEEM 2009S1 shallow ice core trace elements concentrations AMD_KOPRI STAC Catalog 2017-09-27 2017-09-27 -51.06, 77.45, -51.06, 77.45 https://cmr.earthdata.nasa.gov/search/concepts/C2244300703-AMD_KOPRI.umm_json The first high resolution records of atmospherc trace metals for 1711~1969 were recovered from Greenland NEEM shallow ice core together with ions records. These records reveal increases in various atmospheric metals since the Industrial Revolution. Also, the comparion between these records and those from other Greenland ice cores represents regional differences in anthropogenic contributions. Researches for changes in atmospheric trace element over Greenland after the Industrial Revolution and contributions from natural/anthropogenic sources proprietary KOPRI-KPDC-00000763_1 CPS2 AMD_KOPRI STAC Catalog 2013-02-20 2013-02-27 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300790-AMD_KOPRI.umm_json CPS2 is termed as cell-protection substances 2 capable of protection of the cells and lowering freezing points below melting points. Antarctic freshwater green microalga, Chloromonas sp. was reported to produce and secrete CPS2. CPS2 genes will be utilized to protect the skin and tissue cells by applying any valuable products. proprietary @@ -9108,14 +9108,14 @@ KOPRI-KPDC-00000767_1 2016-2017 Barton Peninsular micro-climate data_HOBO soil t KOPRI-KPDC-00000767_1 2016-2017 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2016-01-14 2017-01-27 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244300860-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 proprietary KOPRI-KPDC-00000768_1 Rn gas data measured at KSG during 2013.2-2016.11 AMD_KOPRI STAC Catalog 2013-02-01 2016-11-24 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300905-AMD_KOPRI.umm_json Monitoring of Rn gas at KSG, Antarctica Investigation of air mass path moving to the KSG, Antarctica proprietary KOPRI-KPDC-00000769_1 Simulated Atmospheric Wind at 850 hPa by Boundary Conditions during Last Glacial Maximum AMD_KOPRI STAC Catalog 2017-09-28 2017-09-28 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244301166-AMD_KOPRI.umm_json Atmospheric wind climatology at 850 hPa from the preindustrial simulation, Last Glacial Maximum simulation, LGM-SST simulation, LGM-SEAICE simulation, and LGM-topography simulation. To examine the responses of SH westerly winds to LGM boundary conditions using the state-of-the-art numerical model. To evaluate which boundary conditions are more important in the position and strength of SH westerly winds. proprietary -KOPRI-KPDC-00000770_1 Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298407-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter Monitoring of Aerosol Number Concentration (>10nm) from King Sejong Station. proprietary KOPRI-KPDC-00000770_1 Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298407-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter Monitoring of Aerosol Number Concentration (>10nm) from King Sejong Station. proprietary +KOPRI-KPDC-00000770_1 Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298407-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter Monitoring of Aerosol Number Concentration (>10nm) from King Sejong Station. proprietary KOPRI-KPDC-00000771_1 Italian Seismic Line 2017 AMD_KOPRI STAC Catalog 2017-02-02 2017-03-01 170.15625, -76.980149, -165.498047, -72.127936 https://cmr.earthdata.nasa.gov/search/concepts/C2244295712-AMD_KOPRI.umm_json Italian Seismic Line 2017, single channel seismic data, were collected during the 2016-2017 austral summer with the RV OGS Explora in the Ross Sea continental margin, Antarctica The major purpose of this survey is to investigate stratigraphy and sedimentary structure of the Ross Sea continental margin, Antarctica proprietary KOPRI-KPDC-00000772_1 List of marine benthic invertebrate animal species around King Sejong Station (2017) AMD_KOPRI STAC Catalog 2017-09-29 2017-09-29 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295664-AMD_KOPRI.umm_json Survey of marine benthic invertebrate biota by diving around King Sejong Station Diversity of marine benthic invertebrates proprietary KOPRI-KPDC-00000773_2 Comparison of diversity of ciliate between Jang Bogo Station in Antarctica and Korea using NGS technique (Site261_2014) AMD_KOPRI STAC Catalog 2021-08-02 2021-08-02 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244305169-AMD_KOPRI.umm_json Identification of ciliate diversity from Korea and Antarctica (Jang Bogo Station) Comparison of both data to know the specific ciliate in Antarctica proprietary KOPRI-KPDC-00000774_1 ANA07C Multi-Channel Seismic Survey Lines AMD_KOPRI STAC Catalog 2017-02-04 2017-02-05 166, -75, 170, -74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244295683-AMD_KOPRI.umm_json Multi-Channel seismic data were collected during the 2016-2017 ANA07C cruise in the Ross Sea, Antarctic Ocean The major purpose of this survey is to investigate stratography and the structure of sediments across the Terror Rift, Antarctica. proprietary -KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary +KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00000776_1 Meterological data at BearPeninsula in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-04-11 -115.56512, -74.1877, -115.56512, -74.1877 https://cmr.earthdata.nasa.gov/search/concepts/C2244300521-AMD_KOPRI.umm_json Meterological observation at BearPeninsula DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Antarctic region. Primary climate factors including solar radiation wind speed and direction, air temperature, pressure and relative humidity has been monitored using automatic weather monitoring system at Bear Peninsula. One hourly averaged data are stored at a data logger and an Argos Satellite transmitter is used to transmit daily data. The objectives of this monitoring are to record the past and current climate change through continuous operation of AWS, and to understand characteristics of meteorological phenomena at Bear Peninsula. Monitoring on meteorology at Bear Peninsula. proprietary KOPRI-KPDC-00000777_2 Fossils from North Greenland (2016) AMD_KOPRI STAC Catalog 2016-07-25 2016-08-12 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244305474-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 600 kg of fossils were collected during 2016 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary KOPRI-KPDC-00000778_1 GV7_S2_dust data AMD_KOPRI STAC Catalog 2017-10-10 2017-10-10 158.85, -70.683333, 158.85, -70.683333 https://cmr.earthdata.nasa.gov/search/concepts/C2244300372-AMD_KOPRI.umm_json GV7_S2_dust data MS4_GV7 S22 dust data proprietary @@ -9132,8 +9132,8 @@ KOPRI-KPDC-00000788_1 Doppler wind lidar data at DASAN Station in 2017 AMD_KOPRI KOPRI-KPDC-00000789_2 Ionic species in shallow ice core from GV7 site excavated in 2013-2014 AMD_KOPRI STAC Catalog 2013-12-01 2014-01-10 158.866667, -70.683333, 158.866667, -70.683333 https://cmr.earthdata.nasa.gov/search/concepts/C2244305848-AMD_KOPRI.umm_json Analysis of ionic species in the section of ~15-78m depth of shallow ice core from GV7 site in Antarctica Reconstruction of ionic species to indicate paleo atmospheric environment/climate change of Northern Victoria Land, Antarctica proprietary KOPRI-KPDC-00000790_3 Ionic species in the firn core sampled at Styx glacier in 2014-15 AMD_KOPRI STAC Catalog 2014-12-10 2015-01-02 163.766667, -73.9, 163.766667, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C2244307106-AMD_KOPRI.umm_json Analysis of ionic species in the upper section of firn core from Styx glacier in Antarctica Determination of ionic species in the upper section of firn core from Styx glacier in Antarctica proprietary KOPRI-KPDC-00000791_1 Lichen samples from King George Island collected in 2016 and 2017 AMD_KOPRI STAC Catalog 2016-12-03 2017-02-01 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300628-AMD_KOPRI.umm_json Lichen samples from King George Island collected in 2016 and 2017 Ecophysiological study of lichen proprietary -KOPRI-KPDC-00000792_3 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2016 AMD_KOPRI STAC Catalog 2016-01-10 2017-02-02 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244301575-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2016 Long term monitoring proprietary KOPRI-KPDC-00000792_3 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2016 ALL STAC Catalog 2016-01-10 2017-02-02 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244301575-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2016 Long term monitoring proprietary +KOPRI-KPDC-00000792_3 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2016 AMD_KOPRI STAC Catalog 2016-01-10 2017-02-02 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244301575-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2016 Long term monitoring proprietary KOPRI-KPDC-00000793_2 Mesospheric temperature, Dasan Station, Arctic, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-10-02 11.932, 78.9233, 11.932, 78.9233 https://cmr.earthdata.nasa.gov/search/concepts/C2244306430-AMD_KOPRI.umm_json Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Dasan Station, Arctic Study of the long-term trend of mesospheric temperature in the northern high latitude proprietary KOPRI-KPDC-00000794_3 Neutral wind and temperature from FPI, Dasan Station, Arctic, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-03-22 11.9333, 78.9167, 11.9333, 78.9167 https://cmr.earthdata.nasa.gov/search/concepts/C2244306038-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Dasan Station, Arctic Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary KOPRI-KPDC-00000795_2 Ionospheric scintillation, Dasan Station, Arctic, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-10-08 11.932, 78.9233, 11.932, 78.9233 https://cmr.earthdata.nasa.gov/search/concepts/C2244307129-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan Station, Arctic Study of the ionospheric irregularity in the northern high latitude proprietary @@ -9164,8 +9164,8 @@ KOPRI-KPDC-00000818_2 Neutron count, Jang Bogo Station, Antarctica, 2016 AMD_KOP KOPRI-KPDC-00000819_2 Ionospheric scintillation, Jang Bogo Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-04 2016-06-29 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306751-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Jang Bogo Station, Antarctica Study of the ionospheric irregularity in the southern high latitude proprietary KOPRI-KPDC-00000820_1 Temporal variation of marine phytoplankton in the surface water of the Antarctic Jang Bogo Station in Terra Nova Bay, September 2016-August 2017 AMD_KOPRI STAC Catalog 2016-09-01 2017-08-31 164.2, -74.616667, 164.2, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300705-AMD_KOPRI.umm_json As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica. Investigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water. proprietary KOPRI-KPDC-00000821_2 Electron density and plasma drift, Jang Bogo Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-10-01 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306337-AMD_KOPRI.umm_json Electron density profile, plasma drift velocity, and ionospheric tilt information measured from VIPIR (ionosonde) at Jang Bogo Station, Antarctica Study of the ionospheric characteristics in the southern high latitude proprietary -KOPRI-KPDC-00000822_2 All-Sky airglow image, King Sejong Station, Antarctica, 2016 ALL STAC Catalog 2016-01-01 2016-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306160-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude proprietary KOPRI-KPDC-00000822_2 All-Sky airglow image, King Sejong Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306160-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude proprietary +KOPRI-KPDC-00000822_2 All-Sky airglow image, King Sejong Station, Antarctica, 2016 ALL STAC Catalog 2016-01-01 2016-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306160-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude proprietary KOPRI-KPDC-00000823_4 Neutral wind and temperature from Meteor Radar, King Sejong Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-10-01 -58.7885, -62.2245, -58.7885, -62.2245 https://cmr.earthdata.nasa.gov/search/concepts/C2244306729-AMD_KOPRI.umm_json Neutral wind (80 – 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica Study of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude proprietary KOPRI-KPDC-00000824_2 Mesospheric temperature and airglow intensity, King Sejong Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-10-01 -58.7885, -62.2245, -58.7885, -62.2245 https://cmr.earthdata.nasa.gov/search/concepts/C2244306100-AMD_KOPRI.umm_json Mesospheric temperature and airglow intensity measured from Spectral Airglow Temperature Imager (SATI) at King Sejong Station Study of atmospheric wave activities and temperature variations in mesosphere and lower thermosphere (MLT) at southern high latitude proprietary KOPRI-KPDC-00000825_2 Ionospheric scintillation, King Sejong Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-10-01 -58.7885, -62.2245, -58.7885, -62.2245 https://cmr.earthdata.nasa.gov/search/concepts/C2244306210-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station, Antarctica Study of the ionospheric irregularity in the southern high latitude proprietary @@ -9221,8 +9221,8 @@ KOPRI-KPDC-00000875_1 Eddy covariance data at DASAN Station in 2016 AMD_KOPRI ST KOPRI-KPDC-00000876_1 Eddy covariance data at DASAN Station in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-09-19 11.865833, 78.921944, 11.865833, 78.921944 https://cmr.earthdata.nasa.gov/search/concepts/C2244295705-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2017 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux at DASAN Station proprietary KOPRI-KPDC-00000877_1 CCN(Cloud Condensation Nuclei) data at Zeppelin station in January-November, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-11-30 11.888889, 78.906667, 11.888889, 78.906667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295663-AMD_KOPRI.umm_json The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station. Monitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station proprietary KOPRI-KPDC-00000878_1 LED NDVI measured at Council site of Alaska in 2016 AMD_KOPRI STAC Catalog 2017-12-05 2017-12-05 -163.711, 64.844, -163.711, 64.844 https://cmr.earthdata.nasa.gov/search/concepts/C2244301187-AMD_KOPRI.umm_json A vegetation index NDVI was measured during growing season at the Council site, 70-miles northeast from the Nome, Alaska. The sensor was developed by Seoul National University (Prof. Young-Ryul Ryu) and provided for in-situ installation. The sensor is composed of one pair of upward/downward looking LEDs to obtain reflectivity in each bandwidth. We can calculate NDVI (normalized difference vegetation index) using this sensor to monitor vegetation activity. To monitor high-temporal variation of vegetaion activity at permafrost region, west Alaska. proprietary -KOPRI-KPDC-00000879_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 AMD_KOPRI STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300975-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000879_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 ALL STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300975-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary +KOPRI-KPDC-00000879_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 AMD_KOPRI STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300975-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000880_1 Soil moisture and soil temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 AMD_KOPRI STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300926-AMD_KOPRI.umm_json Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000881_1 CO2 auto-chamber data of Council site in 2017 AMD_KOPRI STAC Catalog 2016-09-22 2017-09-13 -163.705333, 64.843333, -163.705333, 64.843333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301146-AMD_KOPRI.umm_json CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2017 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval. To monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site. proprietary KOPRI-KPDC-00000882_1 Upper air observation data at Jang Bogo Station in 2016 AMD_KOPRI STAC Catalog 2016-02-01 2016-12-21 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244300540-AMD_KOPRI.umm_json Regular upper air observation is made once a day at 00 UTC from February to November by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every two-second. The minimum observation height is over 20 km. Monitoring of changes in meteorological variables with altitude over Jang Bogo station proprietary @@ -9291,8 +9291,8 @@ KOPRI-KPDC-00000944_1 Moderate Resolution Imaging Spectroradiometer in Arctic (M KOPRI-KPDC-00000945_1 Moderate Resolution Imaging Spectroradiometer in Antarctic (MODIS) / Aqua, 2015 AMD_KOPRI STAC Catalog 2015-01-01 2015-12-31 180, -90, -180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2244297229-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary -KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary +KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary KOPRI-KPDC-00000948_1 Moderate Resolution Imaging Spectroradiometer (MODIS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-30 2016-02-03 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297939-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data around the Jang Bogo Station in Antarctic. To derive products including vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans around the Jang Bogo Station. proprietary KOPRI-KPDC-00000949_1 Medium Resolution Spectral Imager (MERSI) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-10-31 2015-11-09 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244298276-AMD_KOPRI.umm_json MERSI is a scanner carried aboard the third FengYun (FY-3) series of meteorological satellites launched by China and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud, vegetation, snow and ice, ocean color around the Jang Bogo Station. proprietary KOPRI-KPDC-00000952_1 Moderate Resolution Imaging Spectroradiometer in the Arctic (MODIS) / Aqua, 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244298621-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary @@ -9342,8 +9342,8 @@ KOPRI-KPDC-00000995_1 Sea Ice from SW of James Ross Island AMD_KOPRI STAC Catalo KOPRI-KPDC-00000996_1 Sea Ice from W of James Ross Island AMD_KOPRI STAC Catalog 2018-04-20 -58.543322, -64.147707, -58.543322, -64.147707 https://cmr.earthdata.nasa.gov/search/concepts/C2244299635-AMD_KOPRI.umm_json 2018 W of James Ross Island Sea Ice, Antarctic Climate change observation proprietary KOPRI-KPDC-00000997_1 Identification of growth rate of Antarctic terrestrial ciliates based on temperature around King Sejong Station (2017/18) AMD_KOPRI STAC Catalog 2017-12-06 2018-01-24 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300273-AMD_KOPRI.umm_json Identification of growth rate of ciliates from Barton Peninsular, South Shetland Islands in Antarctica To show the growth rate of ciliates based on temperature in Antarctica proprietary KOPRI-KPDC-00000998_2 ANA08C Marine Magnetic Data AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301255-AMD_KOPRI.umm_json Marine magnetic data were collected during the ANA08C Expedition in the 2017-2018 austral summer in the Ross Sea, Antarctica proprietary -KOPRI-KPDC-00000999_2 2018 Multibeam bathymetry data in the Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301265-AMD_KOPRI.umm_json Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary KOPRI-KPDC-00000999_2 2018 Multibeam bathymetry data in the Ross Sea, Antarctica ALL STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301265-AMD_KOPRI.umm_json Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary +KOPRI-KPDC-00000999_2 2018 Multibeam bathymetry data in the Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301265-AMD_KOPRI.umm_json Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary KOPRI-KPDC-00001000_2 Sub-bottom profile data in the Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301275-AMD_KOPRI.umm_json Sub-bottom profile (SBP) data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary KOPRI-KPDC-00001001_1 De novo transcriptome assembly of the moss Sanionia uncinata in response to relative water content reduction in the Antarctic natural habitat AMD_KOPRI STAC Catalog 2016-03-21 -58.771667, -62.220278, -58.771667, -62.220278 https://cmr.earthdata.nasa.gov/search/concepts/C2244300607-AMD_KOPRI.umm_json Despite the importance, the molecular responses of S. uncinata related to the decrease in water availability in the long-term future have not yet been identified. To explain physiological and molecular change induced by dehydration, we performed de novo transcriptome assembly. Using the short-read assembly program, 32,100 unigenes were assembled with an N50 of 1,296 bp. proprietary KOPRI-KPDC-00001002_1 EGRIP SP TE AMD_KOPRI STAC Catalog 2018-06-01 2018-06-30 -35.9915, 75.6268, -35.9915, 75.6268 https://cmr.earthdata.nasa.gov/search/concepts/C2244300642-AMD_KOPRI.umm_json Greenland EastGRIP 2017 snow pit trace metals Investigation of seasonal changes in atmospheric trace metals over northeastern Greenland proprietary @@ -9449,8 +9449,8 @@ KOPRI-KPDC-00001100_3 Ionospheric scintillation, King Sejong Station, Antarctica KOPRI-KPDC-00001101_5 Neutral wind and temperature from FPI, King Sejong Station, Antarctica, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244307207-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at KSS station, Antarctica Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary KOPRI-KPDC-00001102_3 All-Sky airglow image, King Sejong Station, Antarctica, 2017 ALL STAC Catalog 2017-01-01 2017-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244307078-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude proprietary KOPRI-KPDC-00001102_3 All-Sky airglow image, King Sejong Station, Antarctica, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244307078-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude proprietary -KOPRI-KPDC-00001103_3 All-Sky airglow image, King Sejong Station, Antarctica, 2018 ALL STAC Catalog 2018-01-01 2018-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306042-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001103_3 All-Sky airglow image, King Sejong Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306042-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary +KOPRI-KPDC-00001103_3 All-Sky airglow image, King Sejong Station, Antarctica, 2018 ALL STAC Catalog 2018-01-01 2018-10-01 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306042-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001104_3 Electron density and plasma drift, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-02 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306739-AMD_KOPRI.umm_json Electron density profile, plasma drift velocity, and ionospheric tilt information measured from VIPIR (ionosonde) at Jang Bogo Station, Antarctica Study of the ionospheric characteristics in the southern high latitude proprietary KOPRI-KPDC-00001105_4 Neutral wind and temperature from FPI, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-03-06 2018-10-01 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306027-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Jang Bogo Station (JBS), Antarctica Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary KOPRI-KPDC-00001106_3 Neutron count, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-09-30 164.14, -74.6202, 164.2273, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244306728-AMD_KOPRI.umm_json Cosmic ray origin neutron count measured from neutron monitor at Jang Bogo Station, Antarctica Study of the variation of neutron count in the southern high latitude proprietary @@ -9460,8 +9460,8 @@ KOPRI-KPDC-00001108_4 All-sky aurora (proton) image at Jang Bogo Station, Antarc KOPRI-KPDC-00001109_4 Geomagnetic field, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-09-30 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306279-AMD_KOPRI.umm_json Variation of geomagnetic field measured from search-coil magnetometer (SCM) at Jang Bogo Station, antarctica Study of the activity of ultra low frequency (ULF) wave in the southern high latitude proprietary KOPRI-KPDC-00001110_4 Neutral wind and temperature from FPI, Dasan Station, Arctic, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-03-22 11.836, 78.938, 11.836, 78.938 https://cmr.earthdata.nasa.gov/search/concepts/C2244307214-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Febry-Perot interferometer (FPI) at Dasan station, Arctic Study of the atmosphere wave activities in the upper atmosphere in the southern/northern high-latitude proprietary KOPRI-KPDC-00001111_4 Ionospheric scintillation, Dasan Station, Arctic, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-08 11.932, 78.9233, 11.932, 78.9233 https://cmr.earthdata.nasa.gov/search/concepts/C2244306245-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan Station, Arctica Study of the ionospheric irregularity in the northern high latitude proprietary -KOPRI-KPDC-00001112_4 All-sky aurora (proton) image, Longyearbyen, Norway, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-02-28 16.040746, 78.147909, 16.040746, 78.147909 https://cmr.earthdata.nasa.gov/search/concepts/C2244306694-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway Study of the aurora (proton) characteristics in the northern high latitude proprietary KOPRI-KPDC-00001112_4 All-sky aurora (proton) image, Longyearbyen, Norway, 2018 ALL STAC Catalog 2018-01-01 2018-02-28 16.040746, 78.147909, 16.040746, 78.147909 https://cmr.earthdata.nasa.gov/search/concepts/C2244306694-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway Study of the aurora (proton) characteristics in the northern high latitude proprietary +KOPRI-KPDC-00001112_4 All-sky aurora (proton) image, Longyearbyen, Norway, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-02-28 16.040746, 78.147909, 16.040746, 78.147909 https://cmr.earthdata.nasa.gov/search/concepts/C2244306694-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway Study of the aurora (proton) characteristics in the northern high latitude proprietary KOPRI-KPDC-00001113_3 Mesospheric temperature, Kiruna, Sweden, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 21.03, 67.872, 21.03, 67.872 https://cmr.earthdata.nasa.gov/search/concepts/C2244306621-AMD_KOPRI.umm_json Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Sweden Study of the long-term trend of mesospheric temperature in the northern high latitude proprietary KOPRI-KPDC-00001114_4 Neutral wind and temperature from FPI, Kiruna, Sweden, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 21.03, 67.872, 21.03, 67.872 https://cmr.earthdata.nasa.gov/search/concepts/C2244307306-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Kiruna, Sweden Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary KOPRI-KPDC-00001115_2 Ionospheric total electron content monitoring system over Kiruna, Sweden at 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244305498-AMD_KOPRI.umm_json Total electron content in the ionosphere over Kiruna, Sweden Study of the statistical characteristics of ionosphere in northern high latitude proprietary @@ -9473,14 +9473,14 @@ KOPRI-KPDC-00001120_1 CTD data in the Kongfjorden, Svalbard in May, 2017 AMD_KOP KOPRI-KPDC-00001121_1 CTD data in the Kongfjorden, Svalbard in October, 2017 AMD_KOPRI STAC Catalog 2017-10-18 2017-10-20 11.65, 78.907, 12.385, 78.985 https://cmr.earthdata.nasa.gov/search/concepts/C2244300720-AMD_KOPRI.umm_json In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the October, 2017. To investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard. proprietary KOPRI-KPDC-00001122_1 CTD data in the Kongfjorden, Svalbard in April, 2018 AMD_KOPRI STAC Catalog 2018-04-12 2018-04-14 11.65, 78.907, 12.385, 78.985 https://cmr.earthdata.nasa.gov/search/concepts/C2244300782-AMD_KOPRI.umm_json In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the April, 2018. To investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard. proprietary KOPRI-KPDC-00001123_1 CTD data in the Kongfjorden, Svalbard in June, 2018 AMD_KOPRI STAC Catalog 2018-06-07 2018-06-09 11.65, 78.907, 12.385, 78.985 https://cmr.earthdata.nasa.gov/search/concepts/C2244300794-AMD_KOPRI.umm_json In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the June, 2018. To investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard. proprietary -KOPRI-KPDC-00001124_4 All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018 ALL STAC Catalog 2018-03-01 2018-10-31 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244307161-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica Study of the aurora characteristics in the southern high latitude proprietary KOPRI-KPDC-00001124_4 All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-03-01 2018-10-31 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244307161-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica Study of the aurora characteristics in the southern high latitude proprietary +KOPRI-KPDC-00001124_4 All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018 ALL STAC Catalog 2018-03-01 2018-10-31 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244307161-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica Study of the aurora characteristics in the southern high latitude proprietary KOPRI-KPDC-00001125_4 NanoSMPS particle number concentration in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301545-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary KOPRI-KPDC-00001126_5 NanoSMPS particle number concentration in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301557-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary KOPRI-KPDC-00001127_3 NanoSMPS particle number concentration in 2016 AMD_KOPRI STAC Catalog 2016-10-01 2016-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301534-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary KOPRI-KPDC-00001128_1 Soil moisture and soil temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300912-AMD_KOPRI.umm_json Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06 ~ 2018. 06) were collected. To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary -KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 ALL STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary +KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 ALL STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00001130_1 Atmospheric DMS mixing ratio measured from Storhofdi, Iceland in 2017-2018. AMD_KOPRI STAC Catalog 2017-04-04 2018-08-18 -20.29, 63.4, -20.29, 63.4 https://cmr.earthdata.nasa.gov/search/concepts/C2244300807-AMD_KOPRI.umm_json Custum-made DMS analyzer was installed at the Storhofdi observatory, Iceland, and monitored the atmospheric DMS mixing ratio in 2017-208. Analyzing in-situ DMs mixing ratio Storhofdi, Iceland. proprietary KOPRI-KPDC-00001131_1 NDVI data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2018-07-04 2018-09-05 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300832-AMD_KOPRI.umm_json NDVI(Normalized Difference Vegetation Index) from climate manipulation (increasing snow cover) plot for 2 months (2018.7.4 ~ 9.5) were collected proprietary KOPRI-KPDC-00001132_1 Eddy covariance data of Canada permafrost site in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -105.058917, 69.13025, -105.058917, 69.13025 https://cmr.earthdata.nasa.gov/search/concepts/C2244301100-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2017 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary @@ -9527,8 +9527,8 @@ KOPRI-KPDC-00001173_5 Surface temperature, Humidity, Pressure at the GPS station KOPRI-KPDC-00001174_3 Ice Sheet monitoring system(AMIGOS)data at Drygalski Ice tongue, Nansen Ice Sheet, and Campbell Glacier in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 163.427, -75.351, 164.345, -75.072 https://cmr.earthdata.nasa.gov/search/concepts/C2244298390-AMD_KOPRI.umm_json Remotely operating weather station and digital camera Investigation of the behaviour of ice sheet proprietary KOPRI-KPDC-00001175_4 Ice sheet monitoring GPS data around the Jang Bogo Station in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 155.34, -75.555, 164.509, -74.268 https://cmr.earthdata.nasa.gov/search/concepts/C2244301615-AMD_KOPRI.umm_json Remotely operating GPS system Investigation of the behaviour of ice sheet proprietary KOPRI-KPDC-00001176_4 Small phytoplankton contribution to the total primary production during three cruises (ANA02C, ANA04B, ANA06B) in the Amundsen Sea, Antarctica AMD_KOPRI STAC Catalog 2012-02-01 2016-02-29 -127.9108, -75.058905, -101.759, -69.999937 https://cmr.earthdata.nasa.gov/search/concepts/C2244302354-AMD_KOPRI.umm_json To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were conducted by incubating phytoplankton in the incubators on the deck for 3-4 hours after adding stable isotopes (13C, 15NO3, and 15NH4) as tracers into each bottle. Productivity experiments were completed during three cruises. The samples for productivity were collected by CTD rosette water samplers at 6 different light depths (100, 50, 30, 12, 5 and 1%). To understand the spatial distribution of phytoplankton productivity and to assess effect of climate change on ocean ecosystem, productivity experiments were executed in the Amundsen Sea, Antarctica. proprietary -KOPRI-KPDC-00001177_3 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018 AMD_KOPRI STAC Catalog 2018-11-18 2019-01-14 154.838627, -75.536572, 155.93514, -75.246428 https://cmr.earthdata.nasa.gov/search/concepts/C2244300863-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation proprietary KOPRI-KPDC-00001177_3 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018 ALL STAC Catalog 2018-11-18 2019-01-14 154.838627, -75.536572, 155.93514, -75.246428 https://cmr.earthdata.nasa.gov/search/concepts/C2244300863-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation proprietary +KOPRI-KPDC-00001177_3 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018 AMD_KOPRI STAC Catalog 2018-11-18 2019-01-14 154.838627, -75.536572, 155.93514, -75.246428 https://cmr.earthdata.nasa.gov/search/concepts/C2244300863-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation proprietary KOPRI-KPDC-00001178_2 Carbon and nitrogen uptake rates of pico-phytoplankton during two survey periods (2017 and 2018) in the Kongsfjorden, Svalbard AMD_KOPRI STAC Catalog 2017-05-04 2018-04-14 11.65, 78.918, 12.373, 78.955 https://cmr.earthdata.nasa.gov/search/concepts/C2244302365-AMD_KOPRI.umm_json To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were conducted by incubating phytoplankton in the incubators for 4-5 hours after adding stable isotopes (13C, 15NO3, and 15NH4) as tracers into each bottle. The purposes of this study were to estimate the carbon and nitrogen uptake rates of pico-phytoplanktontwo survey periods (2017 and 2018) in Kongsfjorden, Svalbard. proprietary KOPRI-KPDC-00001179_2 Carbon and nitrogen uptake rates of phytoplankton during April 2018 in Kongsfjorden, Svalbard. AMD_KOPRI STAC Catalog 2018-04-12 2018-04-14 11.65, 78.918, 12.373, 78.955 https://cmr.earthdata.nasa.gov/search/concepts/C2244302326-AMD_KOPRI.umm_json The purposes of this study were to investigate spatial variation in total carbon and nitrogen uptake rates of phytoplankton during April in Kongsfjorden, Svalbard. proprietary KOPRI-KPDC-00001180_2 Carbon and nitrogen uptake rates of phytoplankton during May, 2017 in the Kongsfjorden, Svalbard AMD_KOPRI STAC Catalog 2017-05-04 2017-05-08 11.65, 78.918, 12.373, 78.955 https://cmr.earthdata.nasa.gov/search/concepts/C2244302375-AMD_KOPRI.umm_json The purposes of this study were to investigate spatial variation in total carbon and nitrogen uptake rates of phytoplankton during the spring period in Kongsfjorden, Svalbard. proprietary @@ -9567,10 +9567,10 @@ KOPRI-KPDC-00001216_3 Cloud Condensation Nuclei concentration at King Sejong Sta KOPRI-KPDC-00001217_3 Cloud Condensation Nuclei concentration at King Sejong Station collected in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244302084-AMD_KOPRI.umm_json Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN Monitoring of Aerosol CCN from King Sejong Station. proprietary KOPRI-KPDC-00001218_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018 ALL STAC Catalog 2018-01-01 2018-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301229-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001218_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301229-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary -KOPRI-KPDC-00001219_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017 ALL STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301244-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001219_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301244-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary -KOPRI-KPDC-00001220_2 Aerosol Size Distribution from King Sejong Station collected in 2019. AMD_KOPRI STAC Catalog 2019-01-01 2019-06-30 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305477-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary +KOPRI-KPDC-00001219_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017 ALL STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301244-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001220_2 Aerosol Size Distribution from King Sejong Station collected in 2019. ALL STAC Catalog 2019-01-01 2019-06-30 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305477-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary +KOPRI-KPDC-00001220_2 Aerosol Size Distribution from King Sejong Station collected in 2019. AMD_KOPRI STAC Catalog 2019-01-01 2019-06-30 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305477-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00001221_3 KPDC MAXDOAS For Halogen gases at KSJ 2018-2019 AMD_KOPRI STAC Catalog 2018-12-09 2019-06-12 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244306023-AMD_KOPRI.umm_json Spectrum intensity for gaseous halogen compounds measured at King Sejong Station in 2018-2019 (from 9 Dec 2018 to 12 June 2019) by using Multi-Axis Differential Optic Absorption Spectroscopy (Max-DOAS) Monitoring of atmospheric halogen compounds at King Sejong Station. proprietary KOPRI-KPDC-00001222_2 Meltwater sampling from Barton Peninsula (MW2) AMD_KOPRI STAC Catalog 2018-12-08 2018-12-08 -58.7392, -62.24065, -58.7392, -62.24065 https://cmr.earthdata.nasa.gov/search/concepts/C2244301257-AMD_KOPRI.umm_json Meltwater samples were obtained in Barton Peninsula to investigate ice chemical reactions in polar region. proprietary KOPRI-KPDC-00001223_2 Meltwater sampling from Barton Peninsula (MW1) AMD_KOPRI STAC Catalog 2018-12-08 2018-12-08 -58.74405, -62.2399, -58.74405, -62.2399 https://cmr.earthdata.nasa.gov/search/concepts/C2244301268-AMD_KOPRI.umm_json Meltwater was sampled in Barton Peninsula to investigate ice chemical reactions in polar region. proprietary @@ -9621,8 +9621,8 @@ KOPRI-KPDC-00001271_2 Ionospheric total electron content monitoring system over KOPRI-KPDC-00001272_2 Neutron Monitor installed at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301215-AMD_KOPRI.umm_json The Neutron Monitor observes the neutron flux incoming from space to earth's atmosphere over JBS, Antarctica. To study the variation of neutron flux with the strength of solar activity and the relationship between neutron flux and atmospheric constituents. proprietary KOPRI-KPDC-00001273_2 Neutral wind data from FPI installed at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301235-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at JBS station, Antarctica Study of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude proprietary KOPRI-KPDC-00001274_2 Plasma density and drift velocity in ionoephre over Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244305912-AMD_KOPRI.umm_json Ionospheric plasma density and drift velocity measured from VIPIR at JBS station, Antarctica Comprehensive study of ionosphere on plasma-neutral interaction over the southern high-latitude proprietary -KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 ALL STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary +KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 ALL STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001276_3 Neutral wind and temperature, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306024-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, and 250km measured from Fabry-Perot Interferometer (FPI) at King Sejong Station Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary KOPRI-KPDC-00001277_3 Ionospheric scintillation, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306035-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station Study of the ionospheric irregularity in the southern high latitude proprietary KOPRI-KPDC-00001278_4 Neutral wind and temperature (MR), King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 -58.78462, -62.2238, -58.78462, -62.2238 https://cmr.earthdata.nasa.gov/search/concepts/C2244306123-AMD_KOPRI.umm_json Neutral wind (80 – 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica Study of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude proprietary @@ -9760,8 +9760,8 @@ KOPRI-KPDC-00001408_4 ARA10C Sub-bottom profiler Survey Data AMD_KOPRI STAC Cata KOPRI-KPDC-00001409_5 ARA10C Multi-Channel Seismic Survey Data AMD_KOPRI STAC Catalog 2019-09-02 2019-09-10 167.9057, 73.34671, -168.43342, 76.38129 https://cmr.earthdata.nasa.gov/search/concepts/C2244306328-AMD_KOPRI.umm_json Multi-Channel seismic data were collected during the 2019 ARA10C cruise in the Chukchi Sea, Arctic Ocean Investigation of submarine resource environment and seabed methane release in the Chukchi rise proprietary KOPRI-KPDC-00001410_1 Soil moisture and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2019 AMD_KOPRI STAC Catalog 2018-06-18 2019-06-30 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244302366-AMD_KOPRI.umm_json To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation, micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2018.06.18.~2019.06.30) were collected. proprietary KOPRI-KPDC-00001411_1 CO2/Soil temperature profile of Alaska permafrost site in 2019 AMD_KOPRI STAC Catalog 2018-09-13 2019-09-26 -163.711, 64.844, -163.711, 64.844 https://cmr.earthdata.nasa.gov/search/concepts/C2244302376-AMD_KOPRI.umm_json CO2/Soil temperature profile had been measured during summertime in 2019 at Council, Alaska. To monitor and understand CO2 emission and soil temperature change over permafrost region proprietary -KOPRI-KPDC-00001412_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2019 ALL STAC Catalog 2018-06-18 2019-06-30 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244302385-AMD_KOPRI.umm_json To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation, micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2018.06.18~2019.06.30) were collected proprietary KOPRI-KPDC-00001412_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2019 AMD_KOPRI STAC Catalog 2018-06-18 2019-06-30 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244302385-AMD_KOPRI.umm_json To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation, micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2018.06.18~2019.06.30) were collected proprietary +KOPRI-KPDC-00001412_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2019 ALL STAC Catalog 2018-06-18 2019-06-30 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244302385-AMD_KOPRI.umm_json To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation, micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2018.06.18~2019.06.30) were collected proprietary KOPRI-KPDC-00001413_2 NDVI data collected from climate manipulation plots in Cambridge Bay, Canada in 2019 AMD_KOPRI STAC Catalog 2019-06-28 2019-11-02 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244306044-AMD_KOPRI.umm_json NDVI(Normalized Difference Vegetation Index) from climate manipulation (increasing snow cover) plot for 2 months (2018.7.4 ~ 9.5) were collected proprietary KOPRI-KPDC-00001414_1 Eddy covariance data of Canada permafrost site in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -105.058917, 69.13025, -105.058917, 69.13025 https://cmr.earthdata.nasa.gov/search/concepts/C2244302398-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2018 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary KOPRI-KPDC-00001415_1 Eddy covariance data of Greenland permafrost site in 2018 AMD_KOPRI STAC Catalog 2018-08-01 2018-12-06 -16.64025, 81.581167, -16.64025, 81.581167 https://cmr.earthdata.nasa.gov/search/concepts/C2244302422-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2018 at Nord, Greenland. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR5000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary @@ -9771,8 +9771,8 @@ KOPRI-KPDC-00001418_1 Eddy covariance data at DASAN Station in 2019 AMD_KOPRI ST KOPRI-KPDC-00001420_2 Marine heat flow in Chukchi Plateau and East Siberian shelf areas on Arctic ocean 2019 AMD_KOPRI STAC Catalog 2019-09-01 2019-09-17 165.5, 72.9, -162.5, 77.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244307184-AMD_KOPRI.umm_json Heat flow measurements in Chukchi Plateau and East Siberian shelf areas on Arctic ocean Investigation to the thermal structure in Chukchi Plateau and East Siberian shelf areas on Arctic ocean proprietary KOPRI-KPDC-00001421_1 Hydrocasting observation of conductivity, temperature, and depth (CTD) AMD_KOPRI STAC Catalog 2019-08-30 2019-09-20 165.640667, 73.456833, -169.736, 77.132 https://cmr.earthdata.nasa.gov/search/concepts/C2244304657-AMD_KOPRI.umm_json Warming the Arctic surface ocean due to influx of warm Pacific water not only leads to the declining of the sea ice extent but also triggers melting gas hydrate stored in the Arctic Sea floor of the continental shelf areas. Methane (CH4) is the most abundant hydrocarbon in the atmosphere, where it plays a much more effective role as the greenhouse gas than carbon dioxide (CO2). To understand the behavior of gas hydrate in the sediment and to estimate the CH4 fluxes from the sediment through the water column to the atmosphere, we obtained data on water temperature, salinity, density and fluorescence in the water column. proprietary KOPRI-KPDC-00001422_2 Surface observation of CH4 in the atmosphere and ocean AMD_KOPRI STAC Catalog 2019-08-30 2019-09-20 165.640667, 64.49025, -156.825778, 77.132 https://cmr.earthdata.nasa.gov/search/concepts/C2244305666-AMD_KOPRI.umm_json Warming the Arctic surface ocean due to influx of warm Pacific water not only leads to the declining of the sea ice extent but also triggers melting gas hydrate stored in the Arctic Sea floor of the continental shelf areas. Methane (CH4) is the most abundant hydrocarbon in the atmosphere, where it plays a much more effective role as the greenhouse gas than carbon dioxide (CO2). We study to estimate the CH4 fluxes on the interface of air and seawater. The CH4 in the ambient air and the surface water were quantitatively measured along the ship track. proprietary -KOPRI-KPDC-00001423_2 2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores) AMD_KOPRI STAC Catalog 2019-08-29 2019-09-20 167.676767, 73.69587, 179.98125, 77.132017 https://cmr.earthdata.nasa.gov/search/concepts/C2244305039-AMD_KOPRI.umm_json Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc. proprietary KOPRI-KPDC-00001423_2 2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores) ALL STAC Catalog 2019-08-29 2019-09-20 167.676767, 73.69587, 179.98125, 77.132017 https://cmr.earthdata.nasa.gov/search/concepts/C2244305039-AMD_KOPRI.umm_json Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc. proprietary +KOPRI-KPDC-00001423_2 2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores) AMD_KOPRI STAC Catalog 2019-08-29 2019-09-20 167.676767, 73.69587, 179.98125, 77.132017 https://cmr.earthdata.nasa.gov/search/concepts/C2244305039-AMD_KOPRI.umm_json Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc. proprietary KOPRI-KPDC-00001424_1 Manganese nodule samples in the East siberian shelf (2019 ARA10C cruise) AMD_KOPRI STAC Catalog 2019-08-29 2019-11-20 176.338742, 74.921332, 179.055023, 75.799365 https://cmr.earthdata.nasa.gov/search/concepts/C2244305407-AMD_KOPRI.umm_json We collected the manganese nodule by dredge to study the distribution of manganese nodule in the East siberian sea, Arctic Ocean. proprietary KOPRI-KPDC-00001425_1 Ship-borne radiosonde observation data over the Arctic Ocean in the 2016 Araon summer expedition(ARA07B,ARA07C) AMD_KOPRI STAC Catalog 2016-08-06 2016-09-08 179.619, 66.819, 179.024, 78.547 https://cmr.earthdata.nasa.gov/search/concepts/C2244301446-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 6 August 2016 to 8 September 2016 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary KOPRI-KPDC-00001426_1 Ship-borne radiosonde observation data over the Arctic Ocean in the 2017 Araon summer expedition(ARA08B,ARA08C) AMD_KOPRI STAC Catalog 2017-08-07 2017-09-13 179.183, 65.174, 179.086, 77.991 https://cmr.earthdata.nasa.gov/search/concepts/C2244301491-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 7 August 2017 to 13 September 2017 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary @@ -9855,10 +9855,10 @@ KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 ALL STAC KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 AMD_KOPRI STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001506_6 Ionospheric scintillation, Kiruna Sweden, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-20 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244307220-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden Study of the ionospheric irregularity in the northern high latitude proprietary KOPRI-KPDC-00001507_6 Ionospheric scintillation, Dasan Station, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 11.9342, 78.9272, 11.9342, 78.9272 https://cmr.earthdata.nasa.gov/search/concepts/C2244306380-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic Study of the ionospheric irregularity in the northern high latitude proprietary -KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 ALL STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary -KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity ALL STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary +KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 ALL STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary +KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity ALL STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary KOPRI-KPDC-00001510_2 Snow cover map of the Barton Peninsula, King George Island, Antarctica AMD_KOPRI STAC Catalog 1986-01-28 2020-01-19 -58.747839, -62.229025, -58.747839, -62.229025 https://cmr.earthdata.nasa.gov/search/concepts/C2244306359-AMD_KOPRI.umm_json Snow cover on the Barton Peninsula, Antarctica extracted from time-series Landsat satellite data proprietary KOPRI-KPDC-00001511_3 Bacterial NGS data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244306368-AMD_KOPRI.umm_json These data were obtained to examine bacterial community structure and reveal the correlation between soil physicochemical factors and soil bacterial composition in glacial foreland of the Antarctic. proprietary KOPRI-KPDC-00001512_2 2019/20 season Korean Route Traverse based GPS GIS data ALL STAC Catalog 2019-11-07 2020-01-18 149.040453, -77.04815, 164.228789, -74.62405 https://cmr.earthdata.nasa.gov/search/concepts/C2244306379-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches proprietary @@ -9969,8 +9969,8 @@ KOPRI-KPDC-00001628_3 Weather forecasts over the Arctic region AMD_KOPRI STAC Ca KOPRI-KPDC-00001629_1 Foraging trips of Chinstrap penguin and Gentoo penguin breeding at Narebski Point from 2006 to 2019 AMD_KOPRI STAC Catalog 2006-12-17 2020-01-02 -58.766667, -62.233333, -58.766667, -62.233333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301271-AMD_KOPRI.umm_json This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Chinstrap penguin and Gentoo penguin at Narebski Point from December 2006 to January 2020. In sheet1 and sheet2, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin. proprietary KOPRI-KPDC-00001630_1 Foraging trips of Adélie penguin breeding at Inexpressible Island on December 2018 AMD_KOPRI STAC Catalog 2018-12-15 2018-12-17 163.65, -74.9, 163.65, -74.9 https://cmr.earthdata.nasa.gov/search/concepts/C2244301300-AMD_KOPRI.umm_json This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Adélie penguin at Inexpressible Island on December 2018. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin. proprietary KOPRI-KPDC-00001631_2 Foraging trips of Adélie penguin breeding at Adélie Cove on December 2018 AMD_KOPRI STAC Catalog 2018-12-31 2019-01-02 164, -74.75, 164, -74.75 https://cmr.earthdata.nasa.gov/search/concepts/C2244306008-AMD_KOPRI.umm_json This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Adélie penguin at Adélie Cove from December 2018 to January 2019. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin. proprietary -KOPRI-KPDC-00001632_1 A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011 ALL STAC Catalog 2010-12-20 2011-01-20 -145, -74.6, -112, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244301322-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations. proprietary KOPRI-KPDC-00001632_1 A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011 AMD_KOPRI STAC Catalog 2010-12-20 2011-01-20 -145, -74.6, -112, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244301322-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations. proprietary +KOPRI-KPDC-00001632_1 A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011 ALL STAC Catalog 2010-12-20 2011-01-20 -145, -74.6, -112, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244301322-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations. proprietary KOPRI-KPDC-00001633_1 Observed CTD data and dissolved noble gases along the Dotson Trough, Amundsen Sea, Antarctica in 2011 AMD_KOPRI STAC Catalog 2010-12-26 2011-01-02 -117.6895, -74.2067, -112.4962, -72.4145 https://cmr.earthdata.nasa.gov/search/concepts/C2244301379-AMD_KOPRI.umm_json This dataset is dissolved noble gases obtained during ANA01C cruise. The dataset also contain potential temperature, salinity and dissolved oxygen obtained by CTD rosette system. The dataset constituted 5 station along the Dotson Trough, Amundsen Sea. proprietary KOPRI-KPDC-00001634_2 Lowered Acoustic Doppler Current Profiler (LADCP) data - August 2016, western Arctic Ocean (4 CTD stations) AMD_KOPRI STAC Catalog 2016-08-08 2016-08-27 -175.895, 76.575, -164.155, 77.864 https://cmr.earthdata.nasa.gov/search/concepts/C2244306113-AMD_KOPRI.umm_json The data are the Lowered Acoustic Doppler Current Profiler (LADCP) data obtained from R/V Icebreaker ARAON in August 2016. The dataset contains LADCP data from surface to 100 m depth (5-m interval) at 4 CTD stations (Sts. 23, 24, 29, and 30) aiming at measuring instantaneous current profiles. proprietary KOPRI-KPDC-00001635_2 Meteorological data at the Jang Bogo Station, Antarctica in 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 164.228333, -74.623333, 164.228333, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306204-AMD_KOPRI.umm_json Meteorological observation was carried out at the Jang Bogo Station in 2020. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report. To understand weather phenomena and to monitor climate variation at Jang Bogo Station, Antarctica proprietary @@ -10008,8 +10008,8 @@ KOPRI-KPDC-00001666_2 Wind data on ARAON DaDis for Antarctic cruise, 2020/2021 A KOPRI-KPDC-00001667_2 Upper O3 observation data at Jang Bogo Station in 2019 AMD_KOPRI STAC Catalog 2019-01-17 2019-11-28 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306388-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary KOPRI-KPDC-00001668_2 Upper O3 observation data at Jang Bogo Station in 2020 AMD_KOPRI STAC Catalog 2020-01-16 2020-12-17 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306563-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary KOPRI-KPDC-00001669_2 Upper O3 observation data at Jang Bogo Station in 2021 AMD_KOPRI STAC Catalog 2021-01-02 2021-06-10 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306666-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary -KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary +KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001673_2 Multibeam data, Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR), 2020/21 season AMD_KOPRI STAC Catalog 2020-11-28 2020-11-29 -179.79775, -66.58295, -176.64499, -64.11792 https://cmr.earthdata.nasa.gov/search/concepts/C2244306908-AMD_KOPRI.umm_json During 2020/2021 summer season, due to sea ice, we obtained high resolution bathymetric data and marine magnetic data for only one short spreading-segment in “large-scaled spreading and fracture zones (or leaky transform faults)” located between the Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR). It is expected that it will be able to contribute to the investigations for the tectonic evolution of the Antarctica related to the Australian-Pacific-Antarctic plates and the evolution of the Zealandia-Antarctic mantle, through the bathymetric and magnetic data that will be accumulated in the future. proprietary @@ -10112,8 +10112,8 @@ KOPRI-KPDC-00001773_2 Genes involved in adaptation to marine environment in Ceta KOPRI-KPDC-00001774_1 Weddell Seal hair sample (196296) AMD_KOPRI STAC Catalog 2020-12-21 2020-12-21 164.225806, -74.624389, 164.225806, -74.624389 https://cmr.earthdata.nasa.gov/search/concepts/C2244302383-AMD_KOPRI.umm_json Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic. proprietary KOPRI-KPDC-00001776_4 CTD data (2011-2019), XCTD data (2017-2019) AMD_KOPRI STAC Catalog 2011-08-01 2019-08-31 177, 72.5, -150, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2244306846-AMD_KOPRI.umm_json To investigate variations of water masses in the Chukchi Borderland proprietary KOPRI-KPDC-00001777_2 Soil physicochemical data from two long-term chronosequences (Ardley Island and King George Island) in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.93333, -62.233333, -58.766667, -62.216666 https://cmr.earthdata.nasa.gov/search/concepts/C2244306274-AMD_KOPRI.umm_json Physicochemical data (pH, EC, TC, SOC, TIC, TN and soil texture) of glacier foreland soil samples obtained from Ardley and King George Island at 2019 proprietary -KOPRI-KPDC-00001778_2 2020/21 season Korean Route Traverse based GPS GIS data ALL STAC Catalog 2020-12-01 2020-12-31 164.2362, -74.6281, 164.2362, -74.6281 https://cmr.earthdata.nasa.gov/search/concepts/C2244306293-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche proprietary KOPRI-KPDC-00001778_2 2020/21 season Korean Route Traverse based GPS GIS data AMD_KOPRI STAC Catalog 2020-12-01 2020-12-31 164.2362, -74.6281, 164.2362, -74.6281 https://cmr.earthdata.nasa.gov/search/concepts/C2244306293-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche proprietary +KOPRI-KPDC-00001778_2 2020/21 season Korean Route Traverse based GPS GIS data ALL STAC Catalog 2020-12-01 2020-12-31 164.2362, -74.6281, 164.2362, -74.6281 https://cmr.earthdata.nasa.gov/search/concepts/C2244306293-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche proprietary KOPRI-KPDC-00001779_3 LoopSeq amplicon sequencing data of microbial 16S-18S-ITS long reads from King George Island in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.93333, -62.216666, -58.93333, -62.216666 https://cmr.earthdata.nasa.gov/search/concepts/C2244306309-AMD_KOPRI.umm_json Loopseq long sequencing read data amplified 16S-18S, 18S-ITS region through synthetic long-read (SLR) sequencing technology to identify microbial species in glacial forelands of the Antarctic. proprietary KOPRI-KPDC-00001780_7 Multibeam data (around Orca seamount in Bransfield strait) / 2020&21 season ANA11B AMD_KOPRI STAC Catalog 2021-01-23 2021-01-28 -58.8341, -62.55474, -57.84559, -62.38568 https://cmr.earthdata.nasa.gov/search/concepts/C2244306329-AMD_KOPRI.umm_json Since last year, the frequency of earthquakes has increased in the vicinity of Orca seamount in the Bransfield Strait. Accordingly, in order to confirm the change of the submarine topography due to the earthquake, a side line was set in the epicenter where earthquakes mainly occur and the area covering the Orca seamount, and multi-beam survey was conducted. The survey area shows a distribution of water depth of -300 to -2000m. The observation results that have been post-processed will be used as basic data to analyze geological and geophysical characteristics of the region in the future. proprietary KOPRI-KPDC-00001781_5 KPSN Seismic Data at Victoria Land, Antarctic 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 159.085, -75.605, 165.7361, -74.137 https://cmr.earthdata.nasa.gov/search/concepts/C2244306339-AMD_KOPRI.umm_json To monitor the activites of Mt. Melbourne and glacial movements proprietary @@ -10137,8 +10137,8 @@ KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colb KOPRI-KPDC-00001798_2 Fast Ice Map in the Terra Nova Bay AMD_KOPRI STAC Catalog 2017-05-14 2018-01-09 164.259926, -74.65589, 164.259926, -74.65589 https://cmr.earthdata.nasa.gov/search/concepts/C2244306604-AMD_KOPRI.umm_json Extraction of fast ice area using satellite data proprietary KOPRI-KPDC-00001800_2 Species list and coverage of benthic animals in Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2017-11-01 2019-11-30 168.024447, -77.84013, 168.024447, -77.84013 https://cmr.earthdata.nasa.gov/search/concepts/C2244306640-AMD_KOPRI.umm_json Species list and coverage of benthic animals in Ross Sea, Antarctica proprietary KOPRI-KPDC-00001801_2 Ecological index of benthic animals in Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2017-11-01 2019-11-30 168.024447, -77.84013, 168.024447, -77.84013 https://cmr.earthdata.nasa.gov/search/concepts/C2244306667-AMD_KOPRI.umm_json Biodiversity analysis of benthic animals in Ross Sea, Antarctica proprietary -KOPRI-KPDC-00001804_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020 AMD_KOPRI STAC Catalog 2020-01-10 2021-03-11 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306700-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020 Long term monitoring proprietary KOPRI-KPDC-00001804_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020 ALL STAC Catalog 2020-01-10 2021-03-11 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306700-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020 Long term monitoring proprietary +KOPRI-KPDC-00001804_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020 AMD_KOPRI STAC Catalog 2020-01-10 2021-03-11 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306700-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020 Long term monitoring proprietary KOPRI-KPDC-00001809_2 mRNA sequencing data of lab culture Sanionia uncinata AMD_KOPRI STAC Catalog 2021-02-01 2021-03-31 126.646833, 37.368742, 126.646833, 37.368742 https://cmr.earthdata.nasa.gov/search/concepts/C2244306716-AMD_KOPRI.umm_json To investigate climate factors which regulate life cycle of antartic moss Sanionia uncinata Lab culter Sanionia uncinata were treated with the condition that mimic the climate condition of King George Isaland proprietary KOPRI-KPDC-00001810_2 Global surface air temperature for the 2000 - 2019 winter from the CAM6 hindcast simulation AMD_KOPRI STAC Catalog 2000-10-01 2020-02-28 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306727-AMD_KOPRI.umm_json Developing a seasonal prediction system with atmosphere global climate model CAM6, monthly surface air temperature data was generated from the 2000-2019 wintertime hindcast simulation. proprietary KOPRI-KPDC-00001811_3 Neutral wind and temperature (MR), King Sejong Station, 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-09-28 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307232-AMD_KOPRI.umm_json Neutral wind (80 – 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica Study of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude proprietary @@ -10147,8 +10147,8 @@ KOPRI-KPDC-00001813_2 Neutral wind and temperature, King Sejong Station, 2021 AM KOPRI-KPDC-00001814_2 Ionospheric scintillation, King Sejong Station, 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-09-28 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244306818-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station Study of the ionospheric irregularity in the southern high latitude proprietary KOPRI-KPDC-00001815_2 Ionospheric scintillation, Kiruna Sweden, 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-09-29 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244306982-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden Study of the ionospheric irregularity in the northern high latitude proprietary KOPRI-KPDC-00001816_3 Geomagnetic field, King Sejong Station, 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-09-29 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244306742-AMD_KOPRI.umm_json Variation of geomagnetic field measured from search-coil magnetometer at King Sejong Station. Study of the activity of ultra low frequency (ULF) wave in the southern high latitude. proprietary -KOPRI-KPDC-00001817_2 All-sky airglow image, King Sejong Station, 2021 AMD_KOPRI STAC Catalog 2021-02-01 2021-08-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244306764-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001817_2 All-sky airglow image, King Sejong Station, 2021 ALL STAC Catalog 2021-02-01 2021-08-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244306764-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary +KOPRI-KPDC-00001817_2 All-sky airglow image, King Sejong Station, 2021 AMD_KOPRI STAC Catalog 2021-02-01 2021-08-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244306764-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001818_2 Neutral wind and temperature, Kiruna Sweden, 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-04-20 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244306754-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary KOPRI-KPDC-00001819_2 Metagenomic Analysis of Bacterial Communities AMD_KOPRI STAC Catalog 2021-03-30 2021-10-01 -58.767778, -62.219722, -58.767778, -62.219722 https://cmr.earthdata.nasa.gov/search/concepts/C2244306737-AMD_KOPRI.umm_json Metagenomic Analysis of Bacterial Communities in Colobanthus quitensis in KGI, Antarctic Peninsula proprietary KOPRI-KPDC-00001820_2 Metagenomic Analysis of Fungal Communities AMD_KOPRI STAC Catalog 2021-03-30 2021-10-01 -58.767778, -62.219722, -58.767778, -62.219722 https://cmr.earthdata.nasa.gov/search/concepts/C2244306749-AMD_KOPRI.umm_json Metagenomic Analysis of Fungal Communities in Colobanthus quitensis in KGI, Antarctic Peninsula proprietary @@ -10180,8 +10180,8 @@ KOPRI-KPDC-00001846_2 Major ionic species measured at ice core from Tourmaline P KOPRI-KPDC-00001847_2 Trace elements in GV7 snow pit AMD_KOPRI STAC Catalog 2013-12-22 2013-12-24 158.863583, -70.688083, 158.863583, -70.688083 https://cmr.earthdata.nasa.gov/search/concepts/C2244305965-AMD_KOPRI.umm_json Trace elements in GV7 snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present proprietary KOPRI-KPDC-00001848_2 Trace elements in Hercules Neve snow pit AMD_KOPRI STAC Catalog 2015-12-16 2015-12-16 165.410756, -73.052936, 165.410756, -73.052936 https://cmr.earthdata.nasa.gov/search/concepts/C2244305998-AMD_KOPRI.umm_json Trace elements in Hercules Neve snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present proprietary KOPRI-KPDC-00001850_3 Continuous monitoring of pCO2 and its relevant parameters in the coast of the Jang Bogo Station, Antarctica, in 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-06-30 164.2, -74.616667, 164.2, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244307445-AMD_KOPRI.umm_json In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, ocean pCO2 and its relevant physical, chemical, biological parameters start monitoring in 2020. These include atmospheric CO2 concentration, ocean pCO2, seawater temperature, salinity, dissolved oxygen, pH, chlorophyll-a, CDOM, and, turbidity. proprietary -KOPRI-KPDC-00001851_2 All-sky aurora (electron) image, Jang Bogo Station, 2021 ALL STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306019-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude. proprietary KOPRI-KPDC-00001851_2 All-sky aurora (electron) image, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306019-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude. proprietary +KOPRI-KPDC-00001851_2 All-sky aurora (electron) image, Jang Bogo Station, 2021 ALL STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306019-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude. proprietary KOPRI-KPDC-00001852_2 Neutral wind and temperature, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306033-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) Jang Bogo Station, Antarctica Study of the atmospheric wave activities in the upper atmosphere in the southern high-latitude. proprietary KOPRI-KPDC-00001853_2 Electron density, plasma drift, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2020-11-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306043-AMD_KOPRI.umm_json Electron density profile, plasma drift velocity, and ionospheric tile information measured from VIPIR (ionosonde) at Jang Bogo Station. Study of the ionospheric characteristics in the southern high latitude. proprietary KOPRI-KPDC-00001854_2 Neutron count, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2020-11-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306067-AMD_KOPRI.umm_json Cosmic ray origin neutron count from space measured from neutron monitor at Jang Bogo Station, Antarctica. Study of the variation of neutron count in the southern high latitude. proprietary @@ -10226,8 +10226,8 @@ KOPRI-KPDC-00001899_1 Tidal data at King Sejong Station AMD_KOPRI STAC Catalog 2 KOPRI-KPDC-00001900_1 Geochemical, isotope, and grain size data of GC05-DP02 AMD_KOPRI STAC Catalog 2005-12-01 2005-12-31 -62.639765, -61.04516, -62.639765, -61.04516 https://cmr.earthdata.nasa.gov/search/concepts/C2244306353-AMD_KOPRI.umm_json Magnetic susceptibility, total organic carbon, total nitrogen, C/N ratio, biogenic opal, CaCO3, nitrogen isotope of acid treated samples, grain size analysis data of GC05-DP02 covering the last 600 kyrs. proprietary KOPRI-KPDC-00001902_1 Time series of volume backscattering strength in the Arctic Ocean AMD_KOPRI STAC Catalog 2018-09-01 2019-05-31 -177.069017, 75.777983, -177.069017, 75.777983 https://cmr.earthdata.nasa.gov/search/concepts/C2244306041-AMD_KOPRI.umm_json Data were collected and processed to monitor the vertical dynamics of zooplankton and micro nekton in the Arctic Ocean. proprietary KOPRI-KPDC-00001904_1 Lipid biomarkers (HBIs, sterols) from surface sediments in the Western Arctic AMD_KOPRI STAC Catalog 2020-05-01 2020-12-31 174.001, 73.228, 174, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2244306081-AMD_KOPRI.umm_json To establish a reconstruction technique for past sea ice changes based on pure domestic technology. Acquisition of lipid biomarkers (HBIs, sterols) from surface sediments in the Western Arctic. proprietary -KOPRI-KPDC-00001905_1 2015 ARA06C-01JPC: Lipid biomarkers (HBIs, sterols) from core sediments ALL STAC Catalog 2021-01-01 2021-12-31 -166.428882, 73.620361, -166.428882, 73.620361 https://cmr.earthdata.nasa.gov/search/concepts/C2244306092-AMD_KOPRI.umm_json To identify past sea ice changes based on lipid biomarkers of a sediment core (ARA06C-01JPC) covering the Holocene in the Western Arctic. proprietary KOPRI-KPDC-00001905_1 2015 ARA06C-01JPC: Lipid biomarkers (HBIs, sterols) from core sediments AMD_KOPRI STAC Catalog 2021-01-01 2021-12-31 -166.428882, 73.620361, -166.428882, 73.620361 https://cmr.earthdata.nasa.gov/search/concepts/C2244306092-AMD_KOPRI.umm_json To identify past sea ice changes based on lipid biomarkers of a sediment core (ARA06C-01JPC) covering the Holocene in the Western Arctic. proprietary +KOPRI-KPDC-00001905_1 2015 ARA06C-01JPC: Lipid biomarkers (HBIs, sterols) from core sediments ALL STAC Catalog 2021-01-01 2021-12-31 -166.428882, 73.620361, -166.428882, 73.620361 https://cmr.earthdata.nasa.gov/search/concepts/C2244306092-AMD_KOPRI.umm_json To identify past sea ice changes based on lipid biomarkers of a sediment core (ARA06C-01JPC) covering the Holocene in the Western Arctic. proprietary KOPRI-KPDC-00001906_1 Temporal variation of phytoplankton in the surface water of Marian Cove, King George Island, Antarctica, September 2021 - December 2021 AMD_KOPRI STAC Catalog 2021-09-01 2021-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244306105-AMD_KOPRI.umm_json As a research on the ecology of phytoplankton in the coastal waters of the King Sejong Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors were investigated in the Marian Cove, Maxwell Bay of King George Island in Antarctica. Investigation to marine phytoplankton biomass in the coastal waters around the King Sejong Station in Antarctica for the monitoring by environment change of surface sea water conducted. proprietary KOPRI-KPDC-00001907_1 Temporal variation of marine phytoplankton in the surface water of the Antarctic Jang Bogo Station in Terra Nova Bay, July 2021 - December 2021 AMD_KOPRI STAC Catalog 2021-07-01 2021-12-31 164.2, -74.616667, 164.2, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244306127-AMD_KOPRI.umm_json As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica. Investigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted. proprietary KOPRI-KPDC-00001908_1 Stable isotope ratios of carbon and nitrogen from penguin and its diets in the Ross Sea region, 2018-2019 AMD_KOPRI STAC Catalog 2018-10-31 2019-04-16 170.619, -74.5462, 175.8171, -72.0951 https://cmr.earthdata.nasa.gov/search/concepts/C2244306177-AMD_KOPRI.umm_json Antarctic krill and ice krill samples were collected in the western Ross Sea during the ARAON cruise in 2018-2019 (ANA09B). Chick carcasses of Adelie and Emperor penguins were collected at Cape Hallett, Inexpressible Island, Cape Washington, and Coulman Island. Pretreated samples were used for carbon stable isotope analysis, and untreated samples were used for nitrogen stable isotope. The stable isotope ratios of carbon and nitrogen were determined using an isotope ratio mass spectrometer coupled with and elemental analyzer (EA-IRMS). proprietary @@ -10282,12 +10282,12 @@ Kuparuk_Veg_Maps_1378_1 Maps of Vegetation Types and Physiographic Features, Kup Kuroshio_Area_0 Measurements in the Kuroshio current OB_DAAC STAC Catalog 1997-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360413-OB_DAAC.umm_json Measurements in the Kuroshio, western boundary current in the North Pacific Ocean, from 1997. proprietary Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary -L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary +L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary -L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ALL STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ESA STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary +L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ALL STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary L2SW_Open_3.0 SMOS NRT L2 Swath Wind Speed ESA STAC Catalog 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689620-ESA.umm_json SMOS retrieved surface wind speed gridded maps (with a spatial sampling of 1/4 x 1/4 degrees) are available in NetCDF format. Each product contains parts of ascending and descending orbits and it is generated by Ifremer, starting from the SMOS L1B data products, in Near Real Time i.e. within 4 to 6 hours from sensing time. Before using this dataset, please check the read-me-first note available in the Resources section below. proprietary L3SW_Open_4.0 SMOS L3 Daily Wind Speed ESA STAC Catalog 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689536-ESA.umm_json SMOS L3WS products are daily composite maps of the collected SMOS L2 swath wind products for a specific day, provided with the same grid than the Level 2 wind data (SMOS L2WS NRT) but separated into ascending and descending passes. This product is available the day after sensing from Ifremer, in NetCDF format. Before using this dataset, please check the read-me-first note available in the Resources section below. proprietary L3S_LEO_AM-STAR-v2.80_2.80 GHRSST NOAA/STAR ACSPO v2.80 0.02 degree L3S Dataset from mid-Morning LEO Satellites (GDS v2) POCLOUD STAC Catalog 2006-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2050135480-POCLOUD.umm_json NOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C . The Metop-FG satellite program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The US National Oceanic and Atmospheric Administration (NOAA) under the joint NOAA/EUMETSAT Initial Joint Polar System Agreement, has contributed three Advanced Very High Resolution Radiometer (AVHRR) sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format. The L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites (MetOp-A, -B and -C; all hosted in PO.DAAC) and covers from Dec 2006-present. The L3S_LEO_AM SST dataset is reported in two files per 24-hour interval, daytime and nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency, and then replaced by the Re-ANalysis (RAN) files about 2 months later, with identical file names. The dataset is validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), and monitored in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). The L3S SST imagery and local coverage are continuously evaluated, and checked for consistency with other Level 2, 3 and 4 datasets in the ACSPO Regional Monitor for SST (ARMS) system. NOAA plans to include data from other mid-morning platforms and sensors, such as MetOp-SG METImage and Terra MODIS, into L3S_LEO_AM. More information about the dataset can be found under the Documentation and Citation tabs. proprietary @@ -10423,15 +10423,15 @@ LC35_Landsat7_Fire_Masks_1071_1 LBA-ECO LC-35 Landsat ETM+ Derived Active Fire M LC39_DECAF_Model_1190_1 LBA-ECO LC-39 Modeled Carbon Flux from Deforestation, Mato Grosso, Brazil: 2000-2006 ORNL_CLOUD STAC Catalog 2000-10-01 2006-09-30 -63.85, -20, -50.76, -10 https://cmr.earthdata.nasa.gov/search/concepts/C2781588541-ORNL_CLOUD.umm_json This data set contains modeled estimates of carbon flux, biomass, and annual burning emissions across the Brazilian state of Mato Grosso from 2000-2006. The model, DEforestation CArbon Flux (DECAF), was used to provide annual carbon fluxes from large deforestation events (>25 ha) based on post-deforestation land use, and the frequency and duration of active fires during the deforestation process. Carbon fluxes associated with the conversion of Cerrado to mechanized crop production, fires in Cerrado, and managed pasture cover types were also estimated. Model data outputs provided include: * Estimated aboveground live biomass from DECAF in 2000 and 2004.* Annual biomass burning emissions estimates for 2001-2005 from low, middle, and high emissions scenarios with DECAF. There are 15 GeoTIFF files for annual emissions which represent the carbon emissions per pixel in grams of carbon per m2 (g C m-2). Model data inputs provided include: * Annual burn trajectories for 2001 - 2005, including deforestation, Cerrado land cover conversion, and fires in pasture and Cerrado ecosystems unrelated to agricultural expansion. These data were assembled from three sources: MODIS 500-m burned area maps, annual deforestation based on data from the INPE PRODES program, and the conversion of Cerrado savannah/woodland to cropland estimated from land cover information from MODIS phenology metrics.* Annual land cover data 2001-2004 for the portion of Mato Grosso covered by MODIS phenology metrics, tile h12v10, updated based on annual land cover changes in Amazon forest and Cerrado cover types.* Monthly Normalized Difference Vegetation Index (NDVI) for MODIS tile h12v10 from 10/2000 - 09/2006, based on cloud and gap-filled 16-day NDVI data from MODIS Collection 4 16-day NDVI composites MOD13 product (Huete et al., 2002).There are six compressed (*.gz) files with this data set. proprietary LC39_MODIS_Fire_SA_1186_1 LBA-ECO LC-39 MODIS Active Fire and Frequency Data for South America: 2000-2007 ORNL_CLOUD STAC Catalog 2000-03-01 2007-12-31 -81.29, -34.86, -53.31, 11.75 https://cmr.earthdata.nasa.gov/search/concepts/C2781578636-ORNL_CLOUD.umm_json This data set provides active fire locations and estimates of annual fire frequencies for South America from 2000-2007. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra (2000-2007) and Aqua (2003-2007) satellite platforms were analyzed to determine spatial and temporal patterns in satellite fire detections. The analysis considered a high-confidence subset of all MODIS fire detections to reduce the influence of false fire detections over small forest clearings in Amazonia (Schroeder et al., 2008). The number of unique days on which the active fire detections were recorded within a 1 km radius was estimated from the subset of active fire detections and the ArcGIS neighborhood variety algorithm. There are 14 data files with this data set: 7 GeoTIFF (.tif) files of fire frequency at MODIS 250 m resolution, where each grid cell value represents the number of days in that year on which active fires were detected, and 7 shape files of active fire locations for the years 2001-2007. proprietary LD2012-d18O-Native-age_1 "Annual Mean Water Isotope (d18O) Record for the ""DSS"" Law Dome Ice Core" AU_AADC STAC Catalog 0174-01-01 2007-12-31 112.81, -66.77, 112.81, -66.77 https://cmr.earthdata.nasa.gov/search/concepts/C1214313595-AU_AADC.umm_json "The LD2012-d18O-Native-age record is the annual mean water isotope (d18O) record for the ""DSS"" (Dome Summit South) Law Dome ice core with extensions (e.g. As described in van Ommen et al., Nature Geoscience, 2010) from overlapping ice cores which are dated by comparing multiple chemical species as well as water isotopes. LD2012-d18O-Native-age record spans 2007 A.D. to 174 A.D. The d18O measurements were completed using Isotope Ratio Mass Spectrometers. This work was done as part of AAS 757 and AAS 4061." proprietary -LDEO_INDICES_INDIA All-India Monsoon Rainfall Index at LDEO/IRI Climate Data Library ALL STAC Catalog 1813-06-01 1998-09-30 70, -10, 90, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214614350-SCIOPS.umm_json An all-India summer monsoon rainfall series for the instrumental period of 1844-1991 has been constructed using a progressively increasing station density to 1870, and one that is fixed thereafter at a uniformly distributed 36 stations. The statistical scheme accounts for the increasing variance contributed to the all-India series by the increasing number of stations during the period 1844-1870. An interesting outcome of this study is that a reliable estimate of summer monsoon rainfall over India can be obtained using only 36 observations. proprietary LDEO_INDICES_INDIA All-India Monsoon Rainfall Index at LDEO/IRI Climate Data Library SCIOPS STAC Catalog 1813-06-01 1998-09-30 70, -10, 90, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214614350-SCIOPS.umm_json An all-India summer monsoon rainfall series for the instrumental period of 1844-1991 has been constructed using a progressively increasing station density to 1870, and one that is fixed thereafter at a uniformly distributed 36 stations. The statistical scheme accounts for the increasing variance contributed to the all-India series by the increasing number of stations during the period 1844-1870. An interesting outcome of this study is that a reliable estimate of summer monsoon rainfall over India can be obtained using only 36 observations. proprietary +LDEO_INDICES_INDIA All-India Monsoon Rainfall Index at LDEO/IRI Climate Data Library ALL STAC Catalog 1813-06-01 1998-09-30 70, -10, 90, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214614350-SCIOPS.umm_json An all-India summer monsoon rainfall series for the instrumental period of 1844-1991 has been constructed using a progressively increasing station density to 1870, and one that is fixed thereafter at a uniformly distributed 36 stations. The statistical scheme accounts for the increasing variance contributed to the all-India series by the increasing number of stations during the period 1844-1870. An interesting outcome of this study is that a reliable estimate of summer monsoon rainfall over India can be obtained using only 36 observations. proprietary LEOLSTCMG30_001 Low Earth Orbit Land Surface Temperature Monthly Global Gridded V001 LPCLOUD STAC Catalog 2002-08-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763264753-LPCLOUD.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 1 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to present. The MEaSUREs LEOLST product is generated by regridding the monthly LST CMG products from MODIS (MYD21C3.061) and VIIRS (VNP21C3.002). The product will be available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format. proprietary LEOLSTCMG30_002 Low Earth Orbit Land Surface Temperature Monthly Global Gridded V002 LPDAAC_ECS STAC Catalog 2002-08-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2773138594-LPDAAC_ECS.umm_json The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 2 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to present.The MEaSUREs LEOLST product is generated by regridding the monthly CMG products from Aqua MODIS (MYD21C3) and VIIRS (VNP21C3 and VJ121). The product is available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format. proprietary LEO_0 Long-term Ecosystem Observatory (LEO) oceanographic and meteorological data collection system OB_DAAC STAC Catalog 2001-07-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360429-OB_DAAC.umm_json Measurements from the LEO station off the Atlantic Coast of New Jersey in 2001. proprietary LEVEL_1C__3_5.0 Proba-V 1Km, 333m, and 100m products ESA STAC Catalog 2013-11-28 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1965336924-ESA.umm_json The Proba-V VEGETATION Raw products (Level 1C/P) and synthesis products (Level 3, S1 = daily, S5 = 5 days, S10 = decade) ensure coverage of all significant landmasses worldwide with, in the case of a 10-day synthesis product, a minimum effect of cloud cover, resulting from selection of cloud-free acquisitions during the 10-day period. It ensures a daily coverage between Lat. 35°N and 75°N, and between 35°S and 56°S, and a full coverage every two days at equator. The VEGETATION instrument is pre-programmed with an indefinite repeated sequence of acquisitions. This nominal acquisition scenario allows a continuous series of identical products to be generated, aiming to map land cover and vegetation growth across the entire planet every two days.Products overview • Projection: Plate carrée projection • Spectral bands: All 4 + NDVI • Format: HDF5 & GeoTiFF The Proba-V VEGETATION Level 3 synthesis products are divided into so called granules, each measuring 10 degrees x 10 degrees, each granule being delivered as a single file. Level 3 products are: - Syntesys S1, with resolution 100m (TOA, TOC and TOC NDVI reflectance), 333m (TOA and TOC reflectance) and 1km (TOA and TOC reflectance) - Syntesys S5, with resolution 100m (TOA, TOC and TOC NDVI reflectance) - Syntesys S10, with resolution 333m (TOC and TOC NDVI reflectance) and 1km (TOC and TOC NDVI reflectance) proprietary LF_Bibliography_1 Bibliography of papers relevant to longline fishing. AU_AADC STAC Catalog 1972-01-01 -180, -80, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1214313596-AU_AADC.umm_json The bibliography covers a wealth of published, 'grey', and unpublished literature addressing the effects of longline fishing on seabird mortality. The scope is global, but with a special emphasis on the Southern Ocean. Information on longline methodology is included and attention is given to materials that cover the various mitigation methods in use, tested or proposed. Further, information on the relevant aspects of the ecology of affected seabird species is covered, especially that dealing with mortality levels, at-sea distributions and population and conservation biology. Data sources covered include the scientific literature, popular publications, newspaper articles, videos, brochures, maps and posters, as well as government, NGO and IGO reports. proprietary -LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 ALL STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary +LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary LGB_Del_traverse_1 Delta Oxygen-18 isotope data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313576-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Several shallow depth ice cores (15-60 m) were drilled at selected sites along 2014 km of the main traverse track from LGB00 (68.6543 S, 61.1201 E) near Mawson Station to LGB72 (69.9209 S,76.4933 E) near Davis Station, and at selected sites along a western traverse line from LGB00 toward Enderby Land. Surface cores (2 m) were collected at 30 km intervals along the entire route from LGB00-LGB72. Ice cores have been kept in cool storage at a local cold room storage facility. Isotope data from the cores have been saved in various spreadsheet files (mainly MS Excel). Initial summary data can be obtained from CRC Research Note No.09 'Surface mass balance and snow surface properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary LGB_Gra_traverse_1 Earth gravity field for ice thickness data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313598-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. LaCoste and Romberg gravity meters were used to record measurements of the Earth's gravity field approximately every 2 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. Gravity readings were also obtained at 5 km intervals along a 516 km upper western offset track (50 km parallel upslope from main route) from LGBUW485 (68.6458 S, 60.0272 E) to LGBUW000 (72.6508 S, 55.9275 E). Raw data were stored as meter readings in field notebooks, transferred manually to spreadsheet files (MS Excel). Processed data were stored in spreadsheet files (MS Excel). The data available at the url below are stored in various formats. Summary data (2 km spatial resolution) can be obtained from CRC Research Note No.27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1990-95'. Documents providing archive details of the logbooks are available for download from the provided URL. This work was completed as part of ASAC projects 3 and 2216. Logbook(s): - Gravity Meter Log 89/90 - LGBT Gravity #2 1992-93 - Glaciology Gravity Readings LGBT 1990-91 proprietary LGB_Ht_traverse_1 Ice sheet surface elevation data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313577-AU_AADC.umm_json The ANARE Lambert Glacier Basin (LGB) series of oversnow traverses were conducted during the period 1989-95. Field operations were carried out along the proximity of the 2500 m elevation contour around the interior basin between Mawson and Davis stations. The main traverse route covered some 2014 km of track from LGB00 at 68.6543 S, 61.1201 E, and LGB72 at 69.9209 S, 76.4933 E. An offset route (50 km upslope) parallels the main traverse track around the western half of the basin. Raw data were stored in binary files containing pressure, temperature, navigational position and a variety of other parameters at an approximately 10 m spacing associated with each 2 km long section of track. Processed data were stored as 2 km averaged ice sheet surface elevation spreadsheet files (MS Excel). The data available at the url below are stored in various formats. Summary data (2 km spatial average) can be obtained from CRC Research Note No. 27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1989-95'. This work was completed as part of ASAC projects 3 and 2216. proprietary @@ -10522,8 +10522,8 @@ LSC_biomarkers Evaluation of Selected Histologic and Immunologic Biomarkers in F LSC_immunereprohistologic Immune, Reproductive and Histologic Biomarker Evaluation in Fish Collected for the Columbia and Rio Grande River Basin BEST Program, 1997 CEOS_EXTRA STAC Catalog 1997-08-01 2001-03-01 -115, 30, -105, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553382-CEOS_EXTRA.umm_json "This study is part of a larger project entitled ""Contaminants and Biomarkers in Fish in the Columbia River and Rio Grande Basins, 1997"" ( Mid-Continent Ecological Science Center) This project is part of the Biomonitoring of Environmental Status and Trends (BEST) program. The BEST program incorporates both analytical chemistry arid a suite of biological responses to describe and track contaminant exposure and effects. Our part of this program is to measure and evaluate selected histologic, immunological and reproductive biomarkers. Our objectives are: to document the presence of selected histologic lesions which have been validated or widely accepted as indicators of contaminant exposure; to determine if there is evidence of immunosuppression using immune system biomarkers; evaluate gonad histology utilizing new potential biomarkers; determine if changes in gonad histology correlate with circulating vitellogenin levels; determine if these findings correlate with contaminant presence or concentration. Information was obtained from http://www.lsc.usgs.gov" proprietary LSM_807_1 Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies ORNL_CLOUD STAC Catalog 1996-01-15 1996-01-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2956539244-ORNL_CLOUD.umm_json The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate. Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide, the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files. proprietary LS_TM_ARC Landsat TM Image Data Archived in China Remote Sensing Satellite Ground Station CEOS_EXTRA STAC Catalog 1986-06-01 90, 20, 140, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2226631645-CEOS_EXTRA.umm_json Landsat 5 was launched on March 1, 1984, carrying a seven-band TM sensor, and still operates properly at present. The satellite takes a sun-synchronized orbit with 705km altitude and 98.22 deg. inclination. A TM scene covers 185km by 170km earth surface approximately, with 30m ground resolution for band 1,2,3,4,5,7 and 120m for band6. For a particular place, the revisit cycle of the satellite is 16 days. Chaina Remote Sensing Satellite Ground Station(CRSGS) was inaugurated and become operational in Dec. 1986. Up to now it is the most important source of remote sensing satellite data in China for earth resouce exploration and environment monitoring. CRSGS has provided a large amount of satellite remote sensing products to more than 400 users, domestic and abroad. Applications of TM images have resulted in great economic and social benefits in a wide range of areas of national economy: resource survey and utilization, environment monitoring, geographic cartography, minerarl exploration, disaster detecting and assessing, etc. TM data received by CRSGS since 1986 have been archived. Through a Catalogue Archive and Browse System(CABS), users can retrieve useful information about data of interests. A image(or a group of images) could be searched according to date, location(latitude-longitude or path-row), and quality, etc. Text catalogue is available for all TM data in the archival. In addition to text contents, sub-sampled browse images are available for data acquired after Apr.,1994. The major products of CRSGS are TM data on CCTs, floppy disks and imagery on films or papaer prints. Products fall into two categories with respect to processing methods. 1. Standard processing includes systematic correction, precision correction, and geocoding, etc. 2.Special product(user dependent) includes multi-scene mosaicking, image classification, user defined annotation or administrative boundary adding, special juts enhancement, etc. proprietary -LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 ALL STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 SCIOPS STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary +LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 ALL STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary LTER_0 Long Term Ecological Research Network (LTER) OB_DAAC STAC Catalog 1981-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360464-OB_DAAC.umm_json Measurements from the Long Term Ecological Research Network (LTER) between 1981 and 1999. proprietary LUH2_GCB2019_1851_1 LUH2-GCB2019: Land-Use Harmonization 2 Update for the Global Carbon Budget, 850-2019 ORNL_CLOUD STAC Catalog 0850-01-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2756847743-ORNL_CLOUD.umm_json This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. proprietary LULC_Nigeria_Ethiopia_SAfrica_2367_1 Annual Land Use and Urban Land Cover: Ethiopia, Nigeria, and South Africa, 2016-2020 ORNL_CLOUD STAC Catalog 2016-01-01 2020-12-31 2.57, -35.34, 49.69, 16.21 https://cmr.earthdata.nasa.gov/search/concepts/C3235688636-ORNL_CLOUD.umm_json This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format. proprietary @@ -10562,20 +10562,20 @@ Landsat_8 Landsat 8 USGS_LTA STAC Catalog 2013-02-11 -180, -82.71, 180, 82.74 h Landsat_MSS_ESA_Archive_9.0 Landsat MSS ESA Archive ESA STAC Catalog 1975-04-21 1993-12-31 -22, -24, 44, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1965336926-ESA.umm_json This dataset contains all the Landsat 1 to Landsat 5 Multi Spectral Scanner (MSS) high-quality ortho-rectified L1T dataset acquired by ESA over the Fucino, Kiruna (active from April to September only) and Maspalomas (on campaign basis) visibility masks. The acquired Landsat MSS scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 200m). The altitude changed from 917 Km to 705 km and therefore two World Reference Systems (WRS) were. A full image is composed of 3460 pixels x 2880 lines with a pixel size of 60m. Level 1 Geometrically and terrain corrected GTC products (L1T) are available: it is the most accurate level of processing as it incorporates Ground Control Points (GCPs) and a Digital Elevation Model (DEM) to provide systematic geometric and topographic accuracy, with geodetic accuracy dependent on the number, spatial distribution and accuracy of the GCPs over the scene extent, and the resolution of the DEM used. proprietary Landsat_RBV_8.0 Landsat RBV ESA STAC Catalog 1978-11-01 2018-08-01 20, -90, 50, 75 https://cmr.earthdata.nasa.gov/search/concepts/C3325393983-ESA.umm_json This dataset contains Landsat 3 Return Beam Vidicon (RBV) products, acquired by ESA by the Fucino ground station over its visibility mask. The data (673 scenes) are the result of the digitalization of the original 70 millimetre (mm) black and white film rolls. The RBV instrument was mounted on board the Landsat 1 to 3 satellites between 1972 and 1983, with 80 meter resolution. Three independent co-aligned television cameras, one for each spectral band (band 1: blue-green, band 2: yellow-red, band 3: NIR), constituted this instrument. The RBV system was redesigned for Landsat 3 to use two cameras operating in one broad spectral band (green to near-infrared; 0.505–0.750 µm), mounted side-by-side, with panchromatic spectral response and higher spatial resolution than on Landsat-1 and Landsat-2. Each of the cameras produced a swath of about 90 km (for a total swath of 180 km), with a spatial resolution of 40 m. proprietary Large_River_DOC_Export_0 Export of dissolved organic carbon (DOC) by large rivers OB_DAAC STAC Catalog 2015-05-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360426-OB_DAAC.umm_json Measurements taken as a part of a project to quanitfy and assess the export of dissolved organic carbon by large rivers. proprietary -Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ALL STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ORNL_CLOUD STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary -Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ALL STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary +Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ALL STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ORNL_CLOUD STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary -Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area ALL STAC Catalog 1993-01-01 2010-12-31 -122.4, -43.2, 176.13, 58.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784384781-ORNL_CLOUD.umm_json This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file. proprietary +Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ALL STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area ORNL_CLOUD STAC Catalog 1993-01-01 2010-12-31 -122.4, -43.2, 176.13, 58.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784384781-ORNL_CLOUD.umm_json This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file. proprietary +Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area ALL STAC Catalog 1993-01-01 2010-12-31 -122.4, -43.2, 176.13, 58.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784384781-ORNL_CLOUD.umm_json This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file. proprietary Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ESA STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ALL STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary LiDAR_Forest_Inventory_Brazil_1644_1 LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018 ORNL_CLOUD STAC Catalog 2008-01-01 2018-12-31 -68.3, -26.7, -39.06, -1.58 https://cmr.earthdata.nasa.gov/search/concepts/C2398128915-ORNL_CLOUD.umm_json This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time. proprietary LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ORNL_CLOUD STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ALL STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary LiDAR_Veg_Ht_Idaho_1532_1 LiDAR Data, DEM, and Maximum Vegetation Height Product from Southern Idaho, 2014 ORNL_CLOUD STAC Catalog 2014-08-23 2014-08-31 -116.89, 42.28, -114.68, 43.33 https://cmr.earthdata.nasa.gov/search/concepts/C2767326506-ORNL_CLOUD.umm_json This dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles. High resolution digital elevation models and maps of maximum vegetation height derived from the LiDAR data are provided for each site. proprietary -Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments ALL STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments AU_AADC STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary +Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments ALL STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary Light_Tipping_Points_1 Light-driven tipping points in polar ecosystems - Casey Station, Antarctica AU_AADC STAC Catalog 1998-01-01 2008-12-31 110.4, -66.3, 110.6, -66.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214313622-AU_AADC.umm_json "Some ecosystems can undergo abrupt transformation in response to relatively small environmental change. Identifying imminent ""tipping points"" is crucial for biodiversity conservation, particularly in the face of climate change. Here we describe a tipping point mechanism likely to induce widespread regime shifts in polar ecosystems. Seasonal snow and ice cover periodically block sunlight reaching polar ecosystems, but the effect of this on annual light depends critically on the timing of cover within the annual solar cycle. At high latitudes sunlight is strongly seasonal, and ice-free days around the summer solstice receive orders of magnitude more light than those in winter. Early melt that brings the date of ice-loss closer to midsummer will cause an exponential increase in the amount of sunlight reaching some areas per year. This is likely to drive ecological tipping points in which primary producers (plants and algae) flourish and out-compete dark-adapted communities. We demonstrate this principle on Antarctic shallow seabed ecosystems, which our data suggest are sensitive to small changes in the timing of sea-ice loss. Algae respond to light thresholds that are easily exceeded by a slight reduction in sea-ice duration. Earlier sea-ice loss is likely to cause extensive regime-shifts in which endemic shallow-water invertebrate communities are replaced by algae, reducing coastal biodiversity and fundamentally changing ecosystem functioning. Modeling shows that recent changes in ice and snow cover have already transformed annual light budgets in large areas of the Arctic and Antarctic, and both aquatic and terrestrial ecosystems are likely to experience further significant change in light. The interaction between ice loss and solar irradiance renders polar ecosystems acutely vulnerable to abrupt ecosystem change, as light-driven tipping points are readily breached by relatively slight shifts in the timing of snow and ice loss. This archive contains data and statistical code for the article: Graeme F. Clark, Jonathan S. Stark, Emma L. Johnston, John W. Runcie, Paul M. Goldsworthy, Ben Raymond and Martin J. Riddle (2013) Light-driven tipping points in polar ecosystems. Global Change Biology Data and code are organised into folders according to figures in the article. See the article for a full description of methods. Statistical code was written in R v. 2.15.0. In data files, rows are samples and columns are variables. Details for numerical variables in each data file are listed below. Figures 7 and 8 were made in MATLAB and code is not provided. Figure 1: rad_data.csv Solar irradiance data derived from: Suri M, Hofierja J (2004) A new GIS-based solar radiation model and its application to photovoltaic assessments. Transactions in GIS 8: 175-190. Figure 2: Fig. 2c.1.csv Light: Measured light at the seabed per day (mol photons m-2 d-1). Figure 2: Fig. 2c.2.csv Light: Measured light at the seabed per day (mol photons m-2 d-1). Light.mod.p: Light at the seabed per day (mol photons m-2 d-1) predicted from modeled seasonal variation. Figure 2: Fig. 2d.csv Light: Measured light at the seabed per day (mol photons m-2 d-1). Figure 3: Fig. 3a.csv Irradiance: Mean irradiance (micro mol photons m-2 s-1). P/R: Productivity/respiration ratios (micro mol photons O2-1 gFW-1 h-1). Figure 3: Fig. 3b.csv Light: Mean irradiance (micro mol photons m-2 s-1) in experimental treatments. Growth: Thallus growth (mm) of Palmaria decipiens under experimental treatments. Figure 3: Fig. 3c.csv Des, Him, Irr, Pal: Ice-free days required for minimum annual light budget Figure 3: Fig. 3c.bars.csv Prop: relative cover (sums to 1 per site) of algae and invertebrates, excluding Inversiula nutrix and Spirorbis nordenskjoldi. Figure 4: Fig. 4.csv Time: months after deployment Length: length of thalli (mm) Figure 5: Fig. 5c and d.csv Axis 1 and Axis 1: Values from first two axes of principal coordinate analysis IceCover: proportion of days that each site is free of sea-ice per year. Beta: Beta-diversity. Calculated as Jaccard similarity between the most ice-covered site (OB1) and each other site. Figure 5: Fig. 5e and f.csv IceCover: proportion of days that each site is free of sea-ice per year. Value: number of species per boulder (for Metric=Diversity), or percent cover per boulder (for Metric=Cover). Figure 6: Fig. 6a.csv Sites.lost: number of sites removed from dataset due to sea-ice loss. Ice: maximum ice-free days within the region (d yr-1). S: Total species richness across each subset of sites. Effort: relative sampling effort (number of sites sampled)." proprietary Line_P_0 Line P oceanic transect OB_DAAC STAC Catalog 2009-08-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360432-OB_DAAC.umm_json Line P is an oceanic transect of 26 periodically sampled stations running from southern Vancouver Island to Ocean Station Papa, situated at 50N and 145W. proprietary Long_Fjord_Depth_Measurements_2007_1 Bathymetry/depth measurements made at Long Fjord, Vestfold Hills by drilling through sea ice AU_AADC STAC Catalog 2007-05-24 2007-05-24 78.06903, -68.54131, 78.18976, -68.51341 https://cmr.earthdata.nasa.gov/search/concepts/C1214311180-AU_AADC.umm_json Water depth measurements were taken in Long Fjord during early winter in 2007. The measurements were collected by Graham Cook, station leader at Davis Station in the Australian Antarctic Territory. The measurements were made by dropping a weighted line off the back of a quad bike, after drilling a hole through the sea ice. Measurements were made approximately every 100 metres. The download file contains a csv spreadsheet which lists each waypoint, plus the corresponding water depth and any comments. The text file contains the waypoint information collected by the Garmin GPS unit. Data in the text file are comma separated and are interpreted as follows: WP,D,001 (waypoint) , -68.51341000, 78.06903000,(Latitude and Longitude) 05/25/2007, 10:25:35, (Date and time Downloaded to Computer) 24-MAY-07 11:40:42 (Date and time of reading). Time is in local time. Vegetation was found on the weight that we used when we first started at the seaward end of the Fjord and then again in shallow water between Brookes Hut and a small island 800 or 900 metres out from Brookes. The weight is quite smooth and does not pick up a lot. The reference given below provides some further information about previously collected bathymetry data in Long Fjord. Furthermore, also see the metadata records: 'Bathymetric data of Long and Tryne Fjords at Vestfold Hills, Antarctica, collected in December 1999 [VH_bathy_99]' 'Interpolated bathymetry of Long and Tryne Fjords, Vestfold Hills, Antarctica [long_tryne_bathy]' The fields in this dataset are: Waypoint Latitude Longitude Water Depth Date Time proprietary @@ -11015,8 +11015,8 @@ MI1B2T_003 MISR Level 1B2 Terrain Data V003 LARC STAC Catalog 1999-12-19 -180, MI1B2T_004 MISR Level 1B2 Terrain Data V004 LARC STAC Catalog 1999-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2794373806-LARC.umm_json MI1B2T_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Terrain Data Version 4 product. It contains Terrain-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. proprietary MI1B2_ELLIPSOID_NRT_001 MISR Near Real Time (NRT) Level 1B2 Ellipsoid Data V001 LARC STAC Catalog 2021-08-08 2022-10-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-LARC.umm_json This file contains Ellipsoid-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files. proprietary MI1B2_TERRAIN_NRT_001 MISR Near Real Time (NRT) Level 1B2 Terrain Data V001 LARC STAC Catalog 2021-10-11 2022-10-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-LARC.umm_json This file contains Terrain-projected TOA Radiance,resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files. proprietary -MI2010_11_Alien-plant-survey_JDS_1 Alien plant survey Macquarie Island 2010_11 AU_AADC STAC Catalog 2010-10-13 2011-01-31 158.8, -54.7, 158.9, -54.6 https://cmr.earthdata.nasa.gov/search/concepts/C1214313682-AU_AADC.umm_json "The data are location and abundance data of alien plants found during a systematic survey of Macquarie Island. It relates to three species Poa annua, Cerastium fontanum and Stellaria media. It is essentially a repeat of the Copson 1977 survey. This work has been completed as part of ASAC (AAS) project 2904, ""Aliens in Antarctica"" (ASAC_2904)." proprietary MI2010_11_Alien-plant-survey_JDS_1 Alien plant survey Macquarie Island 2010_11 ALL STAC Catalog 2010-10-13 2011-01-31 158.8, -54.7, 158.9, -54.6 https://cmr.earthdata.nasa.gov/search/concepts/C1214313682-AU_AADC.umm_json "The data are location and abundance data of alien plants found during a systematic survey of Macquarie Island. It relates to three species Poa annua, Cerastium fontanum and Stellaria media. It is essentially a repeat of the Copson 1977 survey. This work has been completed as part of ASAC (AAS) project 2904, ""Aliens in Antarctica"" (ASAC_2904)." proprietary +MI2010_11_Alien-plant-survey_JDS_1 Alien plant survey Macquarie Island 2010_11 AU_AADC STAC Catalog 2010-10-13 2011-01-31 158.8, -54.7, 158.9, -54.6 https://cmr.earthdata.nasa.gov/search/concepts/C1214313682-AU_AADC.umm_json "The data are location and abundance data of alien plants found during a systematic survey of Macquarie Island. It relates to three species Poa annua, Cerastium fontanum and Stellaria media. It is essentially a repeat of the Copson 1977 survey. This work has been completed as part of ASAC (AAS) project 2904, ""Aliens in Antarctica"" (ASAC_2904)." proprietary MI2AS_AEROSOL_NRT_001 MISR Near Real Time (NRT) Level 2 Aerosol parameters V001 LARC STAC Catalog 2021-08-08 2022-10-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2079023474-LARC.umm_json This is the Level 2 Aerosol Product. It contains Aerosol optical depth and particle type, with associated atmospheric data. It is used for MISR Near Real Time processing, and is derived from session-based Level 1 input files. proprietary MI2TC_CMV_BFR_NRT_001 MISR Near Real Time (NRT) Level 2 Cloud Motion Vector parameters in BUFR format V001 LARC STAC Catalog 2022-03-05 2022-10-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-LARC.umm_json This is the MISR Level 2 Cloud Motion Vector Product containing height-resolved cloud motion vectors with associated data in BUFR format. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files. proprietary MI2TC_CMV_HDF_NRT_001 MISR Near Real Time (NRT) Level 2 Cloud Motion Vector parameters V001 LARC STAC Catalog 2021-10-11 2022-10-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000101-LARC.umm_json This is the MISR Level 2 Cloud Motion Vector Product containing height-resolved cloud motion vectors with associated data. It is used for MISR Near Real Time processing, and is derived from session-based Level 0 input files. proprietary @@ -11643,8 +11643,8 @@ MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_DAYTIME_V2019.0_2019.0 MODIS Aqua Level 3 MODIS_AQUA_L3_SST_THERMAL_MONTHLY_4KM_NIGHTTIME_V2019.0_2019.0 MODIS Aqua Level 3 SST Thermal IR Monthly 4km Nighttime V2019.0 POCLOUD STAC Catalog 2002-07-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036882237-POCLOUD.umm_json Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO4N4 proprietary MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_DAYTIME_V2019.0_2019.0 MODIS Aqua Level 3 SST Thermal IR Monthly 9km Daytime V2019.0 POCLOUD STAC Catalog 2002-07-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036877944-POCLOUD.umm_json Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO9D4 proprietary MODIS_AQUA_L3_SST_THERMAL_MONTHLY_9KM_NIGHTTIME_V2019.0_2019.0 MODIS Aqua Level 3 SST Thermal IR Monthly 9km Nighttime V2019.0 POCLOUD STAC Catalog 2002-07-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036877952-POCLOUD.umm_json Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Average daily, weekly (8 day), monthly and annual skin SST products at are available at both 4.63 and 9.26 km spatial resolution. Aqua was launched by NASA on May 4, 2002, into a sun synchronous, polar orbit with a daylight ascending node at 13:30, formation flying in the A-train with other Earth Observation Satellites (EOS), to study the global dynamics of the Earth atmosphere, land and oceans. MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night (NSST) observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity of SST derived from heritage and current NASA sensors. At night, a second SST product is generated using the mid-infrared 3.95 and 4.05 micron wavelength channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be used at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) and Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires MODIS ocean L3 SST data from the OBPG and is the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODSA-MO9N4 proprietary -MODIS_CCaN_NDVI_Trends_Alaska_1666_1 ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015 ALL STAC Catalog 2000-01-01 2015-12-31 -166.85, 66.99, -140.98, 71.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170972734-ORNL_CLOUD.umm_json This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data. proprietary MODIS_CCaN_NDVI_Trends_Alaska_1666_1 ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015 ORNL_CLOUD STAC Catalog 2000-01-01 2015-12-31 -166.85, 66.99, -140.98, 71.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170972734-ORNL_CLOUD.umm_json This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data. proprietary +MODIS_CCaN_NDVI_Trends_Alaska_1666_1 ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015 ALL STAC Catalog 2000-01-01 2015-12-31 -166.85, 66.99, -140.98, 71.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170972734-ORNL_CLOUD.umm_json This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data. proprietary MODIS_CR_Equal_Angle_3h_1.0 MODIS_CR_Equal_Angle_3h GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089272156-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary MODIS_CR_Equal_Angle_Daily_1.0 MODIS_CR_Equal_Angle_Daily GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089272480-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary MODIS_CR_Equal_Area_3h_1.0 MODIS_CR_Equal_Area_3h GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2084194432-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Area Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary @@ -11741,8 +11741,8 @@ MS_Sound_0 Mississippi (MS) Sound optical measurements OB_DAAC STAC Catalog 2005 MTSAT2-OSPO-L2P-v1.0_1.0 GHRSST Level 2P Western Pacific Regional Skin Sea Surface Temperature from the Multifunctional Transport Satellite 2 (MTSAT-2) (GDS version 2) POCLOUD STAC Catalog 2013-08-01 2015-12-04 64, -80, -134, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2499940520-POCLOUD.umm_json Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. proprietary MUR-JPL-L4-GLOB-v4.1_4.1 GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) POCLOUD STAC Catalog 2002-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996881146-POCLOUD.umm_json "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata ""history:"" attribute to determine if a granule is near-realtime or retrospective." proprietary MUR25-JPL-L4-GLOB-v04.2_4.2 GHRSST Level 4 MUR 0.25deg Global Foundation Sea Surface Temperature Analysis (v4.2) POCLOUD STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036880657-POCLOUD.umm_json A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014). This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. proprietary -MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project OB_DAAC STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project ALL STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary +MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project OB_DAAC STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary MURI_HI_0 A Multi University Research Initiative (MURI) near the Hawaiian Islands OB_DAAC STAC Catalog 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.umm_json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. proprietary MURI_HI_0 A Multi University Research Initiative (MURI) near the Hawaiian Islands ALL STAC Catalog 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.umm_json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. proprietary MUSE_0 Monterey Ocean Observing System (MOOS) Upper-water-column Science Experiment (MUSE) OB_DAAC STAC Catalog 2002-07-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360509-OB_DAAC.umm_json Measurements made near Monterey Bay under the MOOS Upper-water-column Science Experiment (MUSE). proprietary @@ -11891,14 +11891,14 @@ MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index ALL STAC Cat MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary -MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index ALL STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index SCIOPS STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary -MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary +MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index ALL STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary -MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary +MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary -MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary +MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary +MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery SCIOPS STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery ALL STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary MassGIS_GISDATA.IMG_COQ2005 1:5,000 Color Ortho Imagery (2005) SCIOPS STAC Catalog 2005-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592911-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health." proprietary @@ -11913,8 +11913,8 @@ MawsonsHuts2008_2009_1 Mawson's Huts Preservation Program 2007/2008, 2008/2009 a Mawsons_Huts_Dataloggers_2 Dataloggers at Mawson's Hut, Cape Denison - microclimate measurements AU_AADC STAC Catalog 1998-01-26 2008-01-30 142.66, -67.009, 142.662, -67.007 https://cmr.earthdata.nasa.gov/search/concepts/C1214313538-AU_AADC.umm_json Dataloggers were installed in a number of locations inside and outside Mawson's Huts at Cape Denison. The dataloggers measure temperature and relative humidity for the purpose of helping gauge corrosivity in the huts. The data are used to assess whether the removal of ice and snow from inside the Hut is affecting the internal microclimate and, therefore, the condition of the building fabric and other artefacts. Currently the data are downloaded by the Research Centre for Materials Conservation and the Built Environment at the Australian Museum, Sydney. Copies of the data are stored in the Australian Antarctic Data Centre. The fields in this dataset are: Date Time Temperature Relative Humidity Thermocouple Site proprietary Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands SCIOPS STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands ALL STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary -McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary +McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary McMurdo_Predator_Prey_Adelie_Penguins Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106459-SCIOPS.umm_json Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/). proprietary McMurdo_Predator_Prey_Adelie_Penguins Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106459-SCIOPS.umm_json Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/). proprietary Mean_Seasonal_LAI_1653_1 Global Monthly Mean Leaf Area Index Climatology, 1981-2015 ORNL_CLOUD STAC Catalog 1981-08-01 2015-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2764692443-ORNL_CLOUD.umm_json This dataset provides a global 0.25 degree x 0.25 degree gridded monthly mean leaf area index (LAI) climatology as averaged over the period from August 1981 to August 2015. The data were derived from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2, a bi-weekly data product from 1981 to 2015 (GIMMS-LAI3g version 2). The LAI3g version 2 (raw) data were first regridded from 1/12 x 1/12 degree to 0.25 x 0.25 degree resolution, then processed to remove missing and unreasonable values, scaled to obtain LAI values, and the bi-weekly LAI values were averaged for every month. Finally, the monthly long-term mean LAI (1981-2015) was calculated. proprietary @@ -11923,8 +11923,8 @@ Menz50k_1 Mount Menzies 1:50000 Topographic GIS Dataset AU_AADC STAC Catalog 197 MetOpA_GOME2_SIF_V2_2292_2 L2 Daily Solar-Induced Fluorescence (SIF) from MetOp-A GOME-2, 2007-2018, V2 ORNL_CLOUD STAC Catalog 2007-02-01 2018-02-01 -180, -89.78, 180, 89.6 https://cmr.earthdata.nasa.gov/search/concepts/C2847115945-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-A with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land on an orbital basis at a resolution of approximately 40 km x 80 km (before 15 July 2013) or 40 km x 40 km (since 15 July 2013). Data are provided for the period from 2007-02-01 to 2018-02-01. Each file contains daily raw and bias-adjusted solar-induced fluorescence, quality control information, and ancillary data. SIF measurements can provide information on vegetation's functional status, including light-use efficiency and global primary productivity, which can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF format. proprietary MetOpB_GOME2_SIF_2182_1 L2 Daily Solar-Induced Fluorescence (SIF) from MetOp-B GOME-2, 2013-2021 ORNL_CLOUD STAC Catalog 2013-04-01 2021-06-07 -180, -89.77, 180, 89.59 https://cmr.earthdata.nasa.gov/search/concepts/C2840822442-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-B with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land (observations up to 75-degree solar zenith angle) at a resolution of approximately 40 km x 80. Data are provided for the period from 2013-04-01 to 2021-06-07. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data. SIF measurements can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy owing to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF (*.nc) format. proprietary Meteorological_1065_1 BIGFOOT Meteorological Data for North and South American Sites, 1991-2004 ORNL_CLOUD STAC Catalog 1991-01-01 2004-12-31 -156.61, -2.87, -54.96, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751482070-ORNL_CLOUD.umm_json The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model. proprietary -Meteorology_Log_Commonwealth_Bay_1977_1978_1 A log of meteorological observations made at Commonwealth Bay between 1977 and 1978 ALL STAC Catalog 1977-01-01 1978-12-31 142.5, -67, 142.5, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311178-AU_AADC.umm_json This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary Meteorology_Log_Commonwealth_Bay_1977_1978_1 A log of meteorological observations made at Commonwealth Bay between 1977 and 1978 AU_AADC STAC Catalog 1977-01-01 1978-12-31 142.5, -67, 142.5, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311178-AU_AADC.umm_json This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary +Meteorology_Log_Commonwealth_Bay_1977_1978_1 A log of meteorological observations made at Commonwealth Bay between 1977 and 1978 ALL STAC Catalog 1977-01-01 1978-12-31 142.5, -67, 142.5, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311178-AU_AADC.umm_json This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary Methane_Ebullition_Lakes_AK_1861_1 ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014 ALL STAC Catalog 2014-10-08 2014-10-08 -147.94, 64.86, -147.77, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401746-ORNL_CLOUD.umm_json This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils. proprietary Methane_Ebullition_Lakes_AK_1861_1 ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014 ORNL_CLOUD STAC Catalog 2014-10-08 2014-10-08 -147.94, 64.86, -147.77, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401746-ORNL_CLOUD.umm_json This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils. proprietary Methane_Ethane_MA_NH_1982_1 Methane and Ethane Observations for Boston, MA, 2012-2020 ORNL_CLOUD STAC Catalog 2012-08-01 2020-05-31 -72.4, 41.5, -69.8, 43.71 https://cmr.earthdata.nasa.gov/search/concepts/C2345793484-ORNL_CLOUD.umm_json This dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS. Background CH4 concentrations for the urban sites were determined using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model trajectories at the boundary of the study region based on measurements at three boundary sites and wind direction from the North American Mesoscale Forecast System (NAM) 12-kilometer meteorology. proprietary @@ -12095,33 +12095,33 @@ NA_TreeAge_1096_1 NACP Forest Age Maps at 1-km Resolution for Canada (2004) and NBCD2000_V2_1161_2 NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000 ORNL_CLOUD STAC Catalog 1999-01-01 2002-12-31 -126.46, 26.52, -67.96, 49.79 https://cmr.earthdata.nasa.gov/search/concepts/C2539954386-ORNL_CLOUD.umm_json The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project. proprietary NBId0001_101 Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849282-CEOS_EXTRA.umm_json These datasets (Africa Outline, Agricultural Landuse, Africa Soils, Vegetation, Surface Hydrography, Hydrologic Basins, Desertification Hazard Model) are part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses in this case on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP) as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm developed by the US Geological Survey and ESRI to create coverage's for one-degree graticules. For details about each dataset, visit the individual entries. proprietary NBId0001_101 Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849282-CEOS_EXTRA.umm_json These datasets (Africa Outline, Agricultural Landuse, Africa Soils, Vegetation, Surface Hydrography, Hydrologic Basins, Desertification Hazard Model) are part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses in this case on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP) as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm developed by the US Geological Survey and ESRI to create coverage's for one-degree graticules. For details about each dataset, visit the individual entries. proprietary -NBId0006_101 African Meteorology (GIS Coverage of Precipitation and Winds) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848036-CEOS_EXTRA.umm_json New-ID: NBI06 Dataset covers mean annual rainfall distribution, number of wet days, wind speed and velocity. The Africa Meteorological Dataset documentation The Africa Meteorological dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. proprietary NBId0006_101 African Meteorology (GIS Coverage of Precipitation and Winds) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848036-CEOS_EXTRA.umm_json New-ID: NBI06 Dataset covers mean annual rainfall distribution, number of wet days, wind speed and velocity. The Africa Meteorological Dataset documentation The Africa Meteorological dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. proprietary -NBId0007_101 Africa Administrative Units (GIS Coverage of Administrative Boundaries) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847851-CEOS_EXTRA.umm_json "New-ID: NBI07 This dataset shows adminstrative boundries of Africa at continental, national, second and third levels in lat/long. The Administrative units Dataset documentation Files: ADMINLL.E00 Code: 100012-002 Vector Member The files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The administrative units dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit (1983), and the Rand-McNally New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverage""'""s for one-degree graticules. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ADMINLL file shows adminstrative boundries at continental, national, second and third levels in lat/long References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication ESRI, FAO and UNEP FAO, UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. G.M.Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source : FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Geographic Lat/Long Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets TOWNS2 100022-002, Human settlements and airports ROADS2 100021-001, major roads" proprietary +NBId0006_101 African Meteorology (GIS Coverage of Precipitation and Winds) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848036-CEOS_EXTRA.umm_json New-ID: NBI06 Dataset covers mean annual rainfall distribution, number of wet days, wind speed and velocity. The Africa Meteorological Dataset documentation The Africa Meteorological dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. proprietary NBId0007_101 Africa Administrative Units (GIS Coverage of Administrative Boundaries) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847851-CEOS_EXTRA.umm_json "New-ID: NBI07 This dataset shows adminstrative boundries of Africa at continental, national, second and third levels in lat/long. The Administrative units Dataset documentation Files: ADMINLL.E00 Code: 100012-002 Vector Member The files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The administrative units dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit (1983), and the Rand-McNally New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverage""'""s for one-degree graticules. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ADMINLL file shows adminstrative boundries at continental, national, second and third levels in lat/long References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication ESRI, FAO and UNEP FAO, UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. G.M.Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source : FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Geographic Lat/Long Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets TOWNS2 100022-002, Human settlements and airports ROADS2 100021-001, major roads" proprietary +NBId0007_101 Africa Administrative Units (GIS Coverage of Administrative Boundaries) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847851-CEOS_EXTRA.umm_json "New-ID: NBI07 This dataset shows adminstrative boundries of Africa at continental, national, second and third levels in lat/long. The Administrative units Dataset documentation Files: ADMINLL.E00 Code: 100012-002 Vector Member The files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The administrative units dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit (1983), and the Rand-McNally New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverage""'""s for one-degree graticules. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ADMINLL file shows adminstrative boundries at continental, national, second and third levels in lat/long References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication ESRI, FAO and UNEP FAO, UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. G.M.Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source : FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Geographic Lat/Long Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets TOWNS2 100022-002, Human settlements and airports ROADS2 100021-001, major roads" proprietary NBId0012_101 Cattle and Buffalo distribution (Africa) CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848567-CEOS_EXTRA.umm_json The Cattle and Buffalo distribution dataset shows cattle and buffalo distribution for sub-Saharan, East and Central Africa. It is part of the East Coast Fever (ECF) dataset. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by Buffalo. Buffalo is the main wildlife host of the ECF. The study was carried out in Nairobi in collaboration with United Nations Environment Program, Global Resource Information Database (UNEP/GRID) and the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary NBId0016_101 Africa FAO Agro-Ecological Zones (GIS Coverage) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848041-CEOS_EXTRA.umm_json New-ID: NBI16 Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10. The Africa Agro-ecological Zones Dataset documentation Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC. FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division. FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48. Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53) proprietary NBId0016_101 Africa FAO Agro-Ecological Zones (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848041-CEOS_EXTRA.umm_json New-ID: NBI16 Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10. The Africa Agro-ecological Zones Dataset documentation Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC. FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division. FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48. Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53) proprietary -NBId0018_101 Africa FAO Major Infrastructure and Human Settlements (GIS Coverage) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849221-CEOS_EXTRA.umm_json New-ID: NBI18 The Africa Major Infrastructure and Human Settlements Dataset Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002 Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa proprietary NBId0018_101 Africa FAO Major Infrastructure and Human Settlements (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849221-CEOS_EXTRA.umm_json New-ID: NBI18 The Africa Major Infrastructure and Human Settlements Dataset Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002 Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa proprietary +NBId0018_101 Africa FAO Major Infrastructure and Human Settlements (GIS Coverage) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849221-CEOS_EXTRA.umm_json New-ID: NBI18 The Africa Major Infrastructure and Human Settlements Dataset Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002 Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa proprietary NBId0019_101 FAO Major Elevation Zones of Africa (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849111-CEOS_EXTRA.umm_json New-ID: NBI19 The Africa Major Elevation Zones Dataset documentation File: ELEVLL Code: 100070-003 Vector Member The above file is in Arc/Info Export format and should be imported using the Arc/Info command Import cover In-Filename Out-Filename The Africa elevation major zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The manuscript derived from the topographic film separates of the UNESCO/FAO Soil Map of the World (1977) in Miller Oblated Stereographic projection was used to provide a generalized coverage of elevation values providing information as both line-related and polygonal form. The map was prepared by overlaying the topography film separate with a matte drafting film and then delineating the selected elevation contours. Some of the line crenulation was removed during the delineation process, because this map was designed to define general elevation zones rather than constitute a true topographic base. Code values were recorded directly on the map and were key-entered during the digitizing process with a spatial resolution of 0.002 inches, as part of the polygon or line sequence indentification number. The map was then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ELEVLL2 data shows Major Elevation zones of Africa, in lat/lon References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO/FAO Soil Map of the World(1977). Scale 1:5000000. UNESCO, Paris DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source: FAO Soil Map of the World, scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Polygon and line Format: Arc/Info export non compressed Related Datasets: All UNEP/FAO/ESRI Datasets AFELBA elevation and Bathymetry (100048) proprietary NBId0020_101 Countries, Coasts and Islands of Africa (Global Change Data Base - Digital Boundaries and Coastlines) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848088-CEOS_EXTRA.umm_json New-ID: NBI20 Countries, Coasts and Islands Dataset documentation (Micro World Data Bank II) Files: COASTS.E00 Code: 100051-001 COUNTRY.E00 100052-001 ISLANDS.E00 100054-001 Vector Members Original files were in IDRISI VEC format coverted to Arc/Info. The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. Micro World Data Bank II (MWDB-II) comprising Coastlines, Country boundries and Islands data sets is part of NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II and is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact: NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The COASTS file shows African Coastlines The COUNTRY file shows African Country Boundaries without coast, no names - only lines The ISLANDS file shows African Islands References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, Vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map: digitized from available sources Publication Date: Jun 1992 Projection: Lat/Lon Type: Polygon and line Format: Arc/Info Export non-compressed proprietary NBId0022_101 Africa Olson World Ecosystems ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary NBId0022_101 Africa Olson World Ecosystems CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary -NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary -NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers ALL STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary +NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers CEOS_EXTRA STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary -NBId0025_101 Africa Soil Classification by Zobler ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers ALL STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary NBId0025_101 Africa Soil Classification by Zobler CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary -NBId0036_101 Africa Lakes and Rivers (World Data Bank II) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0025_101 Africa Soil Classification by Zobler ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary NBId0036_101 Africa Lakes and Rivers (World Data Bank II) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0036_101 Africa Lakes and Rivers (World Data Bank II) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary NBId0041_101 FNOC Elevation (meters), Terrain and Surface Characteristics for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847281-CEOS_EXTRA.umm_json New-ID: NBI41 Africa FNOC Elevation (meters), Terrain and Surface characteristics. Africa Elevation (meters), Terrain, and Surface Characteristics Dataset Documentation Files: AFMAX.IMG Code: 100082-001 AFMIN.IMG 100082-002 AFMOD.IMG 100082-003 Raster Members The IMG files are in IDRISI format Africa elevation dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFMAX file shows maximum elevation (meters) The AFMIN file shows minimum elevation (meters) The AFMOD shows modal elevation (meters) Reference: Cuming, Michael J. and Barbara A. Hawkins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary NBId0042_101 NOAA Monthly 10-Minute Normalized Vegetation Index (April 1985-December 1988) for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848152-CEOS_EXTRA.umm_json "New-ID: NBI42 NOAA monthly Normalized Vegetation Index (NDVI) for Africa. NOAA Monthly 10-Min Normalized Vegetation Index Dataset (APRIL 1985 - DECEMBER 1988) Files: AFAPR85.IMG-AFDEC85.IMG Code: 100041-001 AFJAN86.IMG-AFDEC86.IMG 100041-001 AFJAN87.IMG-AFDEC87.IMG 100041-001 AFJAN88.IMG-AFDEC88.IMG 100041-001 Raster Members The IMG files are in IDRISI format Africa monthly 10-min normalized difference vegetation index dataset is part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA AFAPR85-AFDEC88 (45 months) show monthly Normalized Vegetation Index (NDVI) References: Kidwell, Katherin B. (ed.). Global Vegetion Index User""'""s Guide (1990). NOAA/NHESDIS/SDSD. for additional references see Appendix A-26-A32 of the Global Change Data Base documentation Source map : digitized from available maps and reprocessed Publication Date : Jun 1992 Projection : Lat/lon Type : Raster Format : IDRISI" proprietary -NBId0043_101 Africa Integrated Elevation and Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0043_101 Africa Integrated Elevation and Bathymetry ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary -NBId0044_101 Africa Ocean Mask CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary +NBId0043_101 Africa Integrated Elevation and Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0044_101 Africa Ocean Mask ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary +NBId0044_101 Africa Ocean Mask CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0053_101 Africa Revised FNOC Percent Water Cover CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary NBId0053_101 Africa Revised FNOC Percent Water Cover ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary NBId0079_101 Lake Chad Datasets, Africa CEOS_EXTRA STAC Catalog 1970-01-01 13, 7, 24, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232848788-CEOS_EXTRA.umm_json The Lake Chad Dataset which is a detailed case study of the UNEP/FAO/ESRI Family was developed by UNEP/GRID, on behalf of the UNEP/Fresh Water Unit for the Lake Chad Commission on Sustainable Development. Lake Chad Dataset covers parts of 7 countries: Cameroon, Chad, Nigeria and Niger, Sudan, Central African Republic and Libya and is a clip (regional version) of Africa Outline Dataset (NBI01). The base maps used for the continental version were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. Files: ADMIN.E00 Code: 115001-001 BASE.E00 115002-001 COUNTRIES.E00 115003-001 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The ADMIN polygon dataset showing administrative areas for 7 countries around Lake Chad. The BASE is a polygon Dataset showing the countries with inland water bodies. The COUNTRIES is a polygon Dataset showing only the country boundaries. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. FAO/UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris FAO. Maps and Statistical Data by Administrative Unit (unpublished) Rand-McNally. New International Atlas (1982). Rand-McNally & Company. Chicago Source: FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1988 Projection: Miller Type: Polygon and line Format: Arc/Info Export, non-compressed Related Datasets: All the Lake Chad Datasets of the UNEP/FAO/ESRI family. proprietary @@ -12141,43 +12141,43 @@ NBId0153_101 Benito River dataset of Equatorial Guinea CEOS_EXTRA STAC Catalog 1 NBId0161_101 Climate Dataset of Senegal CEOS_EXTRA STAC Catalog 1970-01-01 -17.53, 12.02, -10.89, 17.14 https://cmr.earthdata.nasa.gov/search/concepts/C2232849116-CEOS_EXTRA.umm_json New-ID: NBI161 The Climate Dataset of Senegal documentation Files: SENEGAL4.IMG Code: 146005-001 SENEGAL5.IMG 146006-001 SENEGAL6.IMG 146007-001 Raster Members IMG files are in IDRISI format The Senegal Climate Dataset was originally digitized for the UNEP/FAO/ESRI Database for Africa from hand-drawn maps provided by FAO for the Desertification Hazard Mapping project. GRID-Geneva rasterized the coverages for UNEP/GRID/WHO/CISFAM Senegal Database with a cell size of 30 seconds and two minutes lat/lon (approximately one- and four kilometeter-square pixels, respectively). Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy The SENEGAL4 file shows mean annual wind velocity meters per second (8 classes). The SENEGAL5 file shows number of wet days per year (6 classes). The SENEGAL6 file shows mean annual rainfall in millimeters (10 classes). REMARK: file may have limited applicability at national scale as was extracted from continental. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP. CISFAM. Consolidated Information System for Famine Management in Africa, Phase I Report (Apr. 1987), Univ. of Louvain, Brussels, Belgium. Source and scale : unknown Report Publication Date : Dec 1988 Projection : lat/lon Type : Raster Format : IDRISI Related Datasets : All UNEP/FAO/ESRI climate Datasets proprietary NBId0169_101 Baringo (Kenya) Pilot Study for Desertification Assessment and Mapping CEOS_EXTRA STAC Catalog 1984-01-01 1992-12-30 35, -1, 36, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2232849286-CEOS_EXTRA.umm_json The purpose of the Kenya Pilot Study was to evaluate the FAO/UNEP Provisional Methodology for Assessment and Mapping of Desertification, and to recommend an effective, simple methodology for desertification assessment within Kenya. The FAO/UNEP Provisional Methodology (1984) proposes seven processes for consideration in desertification assessment: degradation of vegetation, water erosion, wind erosion, salinization, reduction of organic content, soil crusting and compaction. In late 1985, a pilot project for the assessment of the FAO/UNEP Methodology within Kenya was proposed, and in 1987 a memorandum of understanding between the Government of Kenya and UNEP for the implementation of that study was signed. The study areas were: 1) Models can be useful to assist in desertification assessment. Models can be developed from FAO/UNEP Methodology. 2) Any modeling output requires verification. 3) Ground survey and remote sensing can be important sources of data. 4) An evaluation of data and methodologies necessary to allow verification of desertification assessment modeling is required. 5) A human use component should be incorporated into desertification assessment that considers management implications and social, as well as, economic context. 6) Computer implementation of desertificaiton assessment can be effective, however, procedures should be well defined. This study within the Baringo Study Area was designed to address these concerns. The Baringo Study Area identified in this study would be typical of such a training area. The models developed during this study could be applied to the general region. The study area lies between 0 15'-1 N and 35 30' -36 30' E. It is located between the Laikipia escarpment to the East and the Tugen Hills to the West. Topographic elevations vary from 900m on the Njemps flats to 2000m in the Puka, Tangulbei and Pokot highlands. The size of the study area is approximately 15ookm2. 4.0 DATA COLLECTION A wide variety of data was collected. Detailed data was required to provide a basis for evaluating more general cost effective data gathering techniques and to provide a basis for model verification, particularly the socio/economic data. Physical Environment Topographic Data Topographic contours were digitized directly from 1:250,000 Survey of Kenya topographic maps. The contour interval was 200 feet. A digital elevation model was constructed using triangular irregular networks (TIN). Soil Data Soil types were mapped at 1:100,000 scale using existing soil maps, manual interpretation of SPOT imagery, and field investigations (Figure 3). During field trips, soil samples were taken from each soil unit and analyzed by the Kenya National Agricultural Center. 4.2 Climate Data 4.2.1 Rainfall Data Rainfall data from the Kenya Meteorological Department was analyzed for 33 stations within and surrounding the study area. A rainfall erosivity index was calculated based on the Fourier Index (R). 12 RE (p /P) 12 where P = annual rainfall p = monthly rainfall A relationship between this erosivity index and the annual rainfall for each station was calculated using linear regression (Bake, 1988). A map of rainfall erosivity was generated for the study area by relating annual rainfall isoheyts to the following: y = 0.108x - 0.68 This data was coded and digitized. Wind Erosion Potential The following required conditions were determined to create high wind erosion potential (Kinuthia, 1989): 1) Annual rainfall less than 300mm. 2) P/E greater than zero and less than 1, where: P=mean monthly rainfall (cm). E=mean monthly PET (cm). 3) Wind velocity greater than 4 m/s at 10m height. Vegetation Data A vegetation map for the study area was produced at a scale of 1:100,000 through manual interpretation of a SPOT image and field investigations (Figure 6). A structural classification system as adopted by DRSRS was used for naming vegetation types (Grunb). Systematic Reconnaissance Flight Data Since 1977, DRSRS has been conducting aerial surveys of Kenyan rangelands. In addition to data on the number of wildlife and livestock, observations of land use and environmental condition are also made. Socio/economic Data Social Factors A wide variety of data was collected through literature review and a field administered questionnaire. Nutritional status was estimated by measurement of childrens' mid upper arm. Such data is useful for a Level 1 type assessment. Permanent Structures Data For the Level 2 assessment, data on permanent structures was extracted from DRSRS SRF data. This data was used to indicate presence and concentration of sedentary populations. Example Files: VDS.E00 (Vegetation degradation) DES.E00 (Plant Species) Others available on request. proprietary NBId0177_101 Laikipia (Kenya) Research Programme GIS Datasets CEOS_EXTRA STAC Catalog 1990-01-01 1994-12-30 36, 0, 37, 1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848187-CEOS_EXTRA.umm_json Laikipia Research Programme GIS Datasets are divided into two main different study area scales: the Regional level [Laikipia district, the Ewaso Ng'iro Basin] and the Local level [Land parcels-farm(s), catchments of a few kilometer square]. Coordinate Reference System Coverage data is organized thematically as a series of layers. The coordinate reference systems used in LRP dataset are:- (a) global coordinate system - Universal Transverse Mercator (UTM), (b) Local coordinate system. Digitizing Scale and Fuzzy Tolerance The initial digitizing scale for the LRP GIS Dataset is dependent on the scale of the study areas. There are two major research levels carried by LRP namely Regional and Local. The scales used for regional level are 1:250,000 and 1:50,000. FUZZY TOLERANCE is the minimum distance between coordinates in a coverage. The resolution of a coverage is defined by the minimum distance separating the coordinates used to store coverage features. Resolution is limited by the map scale in initial digitizing. The fuzzy tolerance can be calculated as follows for digitizing table: Initial Scale for Coverage of Fuzzy Tolerance Digitizing Units Value 1;250,000 Meters 6.35 1:50,000 Meters 1.25 1:10,000 Meters 0.25 1:5,000 Meters 0.125 1:2,500 Meters 0.0625 Files: Roads.E00 (Roads) Settle.E00 (Settlement Pattern) Centres.E00 (Urban Centres) (other files exist also) proprietary -NBId0203_101 Africa Water Balance high/lowland crops, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary NBId0203_101 Africa Water Balance high/lowland crops, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary +NBId0203_101 Africa Water Balance high/lowland crops, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary NBId0207_101 IGADD Member Countries Crop types and distribution by administrative units, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 22, -12, 51, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232849119-CEOS_EXTRA.umm_json "The IGADD (Inter-Governmental Authority on Drought and Development) crop zones dataset is part of the Africa UNEP/FAO/ESRI Crops Data. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. The data was provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service, Land and Water Development Division, Italy. The datasets were then developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Administrative Units map and the World Atlas of Agriculture (1969). All sources were re-registered to the base map by comparing known features on the base map and the source maps. In the original Database (Africa), a considerable study was made of crop water requirements for a range of crops in the various African climates during the time of the year when irrigation would be required. It was found that a relatively simple relationship exists between annual rainfall and the crop irrigation water requirements for the African food grain crops. It was also observed that water requirements for food grains vary between fruit and vegetable crops on the one side and fiber crops and fodder on the other. No attempt was made to produce complex crop patterns. There is a maximum of 13 crop types in one country. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO Soil Map of the Africa (1977). Scale 1:5000000. UNESCO, Paris. FAO. Administration units map. Scale 1:5 000 000. Rome. FAO. Irrigation and Water Resources Potential for Africa. (1987) Source :UNESCO/FAO Soil Map of the World. Scale 1:5000000 Publication Date :Nov 1987 Projection :Miller Type :Polygon Format :Arc/Info Export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets FAO Irrigable Data sets 100050: "" IRRIGLB lowland crops, best soils "" IRRIGLT lowland crops, best plus suitable soils "" IRRIGUB upland crops, best soils "" IRRIGUT upland crops, best plus suitable soils FAO Soil water balance 100053: "" WATBALLB lowland crops, best soils "" WATBALLT lowland crops, best plus suitable soils "" WATBALUB upland crops, best soils "" WATBALUT upland crops, best plus suitable soils FAO Agro-ecological zones AEZBLL08 North-west of continent AEZBLL09 North-east of continent AEZBLL10 South of continent" proprietary -NBId0208_101 Africa Major Human Settlements and Landuse, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary NBId0208_101 Africa Major Human Settlements and Landuse, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary -NBId0211_101 Africa Irrigation Potential, Best soils, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary +NBId0208_101 Africa Major Human Settlements and Landuse, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary NBId0211_101 Africa Irrigation Potential, Best soils, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary +NBId0211_101 Africa Irrigation Potential, Best soils, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary -NBId0218_101 Africa Surface Hydrography, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary NBId0218_101 Africa Surface Hydrography, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary +NBId0218_101 Africa Surface Hydrography, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary NBId0220_101 Africa Rainfall and Maximum Temperature Measuring Stations (12 average monthly), 1989 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849335-CEOS_EXTRA.umm_json "The Africa Rain Measuring Stations data set, for monthly rainfall is part of the UNEP/ILRAD, now ILRI East Coast Fever (ECF) Database project. The point data was reformatted (Miller, scale 1:5 000 000) from CIAT tabular data based on 12 average monthly rainfall, evaporation, and minimum/maximum temperature. The data was used in the calculation of interpolated surfaces for rainfall and temperature distribution as the basis for modeling of climatic stress factors that constrain the distribution of ticks that transfer ECF. Vector Member The file is in Arc/Info Export format. The RAINSTNS point data represents rainfall measuring stations (12 average monthly) should go with file DATREAD.ME References: P. Lessard, R. L'Eppattenier, R.A. Norval, B.D. Perry, T.T. Dolan, K. Kundert, H. Croze, J.B. Walker, A.D. Irvin. Geographic Information System for studying the Epidemiology of East Coast Fever (Theileria parva) (1989). K. Kundert. Isolating East Coast Fever High risk Areas (1989). Arc/Info European User Conference, Rome, October 1989. CSIRO. Users guide to CLIMEX, A computer program for comparing climates in ecology. CSIRO Aust. Div Rep No.35, pp.-29 Source : CIAT tabular data Publication Date :Jan 1989 Projection :Miller Type :Point Format :Arc/Info Export non-compressed ""Related Data sets :East Coast Fever (100057-002-/66-002): ECFMAP, TICKSUIT, BUFFALO2, CATTLE, CATTYP, BUFCAT2, RAPOLY, RAPNTS, RDPNTS, RNPNTS and RZPNTS. Comment : No boundary (outline) for Africa" proprietary NBId0220_101 Africa Rainfall and Maximum Temperature Measuring Stations (12 average monthly), 1989 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849335-CEOS_EXTRA.umm_json "The Africa Rain Measuring Stations data set, for monthly rainfall is part of the UNEP/ILRAD, now ILRI East Coast Fever (ECF) Database project. The point data was reformatted (Miller, scale 1:5 000 000) from CIAT tabular data based on 12 average monthly rainfall, evaporation, and minimum/maximum temperature. The data was used in the calculation of interpolated surfaces for rainfall and temperature distribution as the basis for modeling of climatic stress factors that constrain the distribution of ticks that transfer ECF. Vector Member The file is in Arc/Info Export format. The RAINSTNS point data represents rainfall measuring stations (12 average monthly) should go with file DATREAD.ME References: P. Lessard, R. L'Eppattenier, R.A. Norval, B.D. Perry, T.T. Dolan, K. Kundert, H. Croze, J.B. Walker, A.D. Irvin. Geographic Information System for studying the Epidemiology of East Coast Fever (Theileria parva) (1989). K. Kundert. Isolating East Coast Fever High risk Areas (1989). Arc/Info European User Conference, Rome, October 1989. CSIRO. Users guide to CLIMEX, A computer program for comparing climates in ecology. CSIRO Aust. Div Rep No.35, pp.-29 Source : CIAT tabular data Publication Date :Jan 1989 Projection :Miller Type :Point Format :Arc/Info Export non-compressed ""Related Data sets :East Coast Fever (100057-002-/66-002): ECFMAP, TICKSUIT, BUFFALO2, CATTLE, CATTYP, BUFCAT2, RAPOLY, RAPNTS, RDPNTS, RNPNTS and RZPNTS. Comment : No boundary (outline) for Africa" proprietary -NBId0223_101 Africa Zobler Soils (Texture Classes, Slope, Phases), 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848713-CEOS_EXTRA.umm_json "The Zobler soil datasets were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The data set is part of the World Data Bank II and is part of ""The Global Change Data Base"". The World Data Bank II is part of a larger project called ""Global Ecosystems Database Project"". The project was a joint effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. The texture data is based on the FAO Soil Map of the World, and compiled into digital form by Zobler. Each matrix element represents the near-surface texture (upper 30 cm) of the dominant soil unit in a one-degree square cell of the earth's surface. The data conforms in location, and nominal classification (land, land-ice, water) to Matthew's vegetation data set. References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map :FAO/UNESCO Soil Map of the World Publication Date :1987 Projection :lat/lon Type :Raster Format :IDRISI" proprietary NBId0223_101 Africa Zobler Soils (Texture Classes, Slope, Phases), 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848713-CEOS_EXTRA.umm_json "The Zobler soil datasets were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The data set is part of the World Data Bank II and is part of ""The Global Change Data Base"". The World Data Bank II is part of a larger project called ""Global Ecosystems Database Project"". The project was a joint effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. The texture data is based on the FAO Soil Map of the World, and compiled into digital form by Zobler. Each matrix element represents the near-surface texture (upper 30 cm) of the dominant soil unit in a one-degree square cell of the earth's surface. The data conforms in location, and nominal classification (land, land-ice, water) to Matthew's vegetation data set. References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map :FAO/UNESCO Soil Map of the World Publication Date :1987 Projection :lat/lon Type :Raster Format :IDRISI" proprietary -NBId0233_101 Africa Population Density Model (Land Degradation Project), 1992 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848719-CEOS_EXTRA.umm_json The Africa Population density model represents ranges of population density of inhabitants per square kilometer. The estimated population densities are expressed on a regularly spaced latitude/longitude raster grid covering Africa with an approximate resolution of 10 km x 10 km at the Equator. The data set which is an assessment of one of the factors causing soil degradation, namely the spatial distribution and density of population. It was developed for the GEMS/UNITAR Africa Database and later used for GLASOD. The data sources include: 600 African towns and cities with figures standardized to 1988 values ( a combination of 479 cities from Birkbeck College and 363 cities in 51 African countries from PC Globe 3.0); UNEP/FAO population data from the 1984 Africa database; the Sierra Club Wilderness Area IUCN Protected Areas, used to delimit areas with extremely sparse populations and treated as having a density of less than one person per square kilometer. For methodology and further detail refer to references listed: UN Institute for Training & Research (UNITAR). GEMS/UNITAR Africa Database. Deichmann, U. and Lars Eklundh. Global Digital Datasets for Land Degradation Studies (1991), GRID Case Studies No.4. UNEP/GRID, Nairobi. UNEP. World Atlas of Desertification (1992). Edward Arnold: A division of Hodder and Stoughton, London. Projection :Geographic Type :Raster Format :IDRISI Related files :POPDENSL.E00, POPDENGR.E00 Associated files :POPDENS.DOC and POPDENS.PAL proprietary +NBId0223_101 Africa Zobler Soils (Texture Classes, Slope, Phases), 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848713-CEOS_EXTRA.umm_json "The Zobler soil datasets were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The data set is part of the World Data Bank II and is part of ""The Global Change Data Base"". The World Data Bank II is part of a larger project called ""Global Ecosystems Database Project"". The project was a joint effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. The texture data is based on the FAO Soil Map of the World, and compiled into digital form by Zobler. Each matrix element represents the near-surface texture (upper 30 cm) of the dominant soil unit in a one-degree square cell of the earth's surface. The data conforms in location, and nominal classification (land, land-ice, water) to Matthew's vegetation data set. References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map :FAO/UNESCO Soil Map of the World Publication Date :1987 Projection :lat/lon Type :Raster Format :IDRISI" proprietary NBId0233_101 Africa Population Density Model (Land Degradation Project), 1992 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848719-CEOS_EXTRA.umm_json The Africa Population density model represents ranges of population density of inhabitants per square kilometer. The estimated population densities are expressed on a regularly spaced latitude/longitude raster grid covering Africa with an approximate resolution of 10 km x 10 km at the Equator. The data set which is an assessment of one of the factors causing soil degradation, namely the spatial distribution and density of population. It was developed for the GEMS/UNITAR Africa Database and later used for GLASOD. The data sources include: 600 African towns and cities with figures standardized to 1988 values ( a combination of 479 cities from Birkbeck College and 363 cities in 51 African countries from PC Globe 3.0); UNEP/FAO population data from the 1984 Africa database; the Sierra Club Wilderness Area IUCN Protected Areas, used to delimit areas with extremely sparse populations and treated as having a density of less than one person per square kilometer. For methodology and further detail refer to references listed: UN Institute for Training & Research (UNITAR). GEMS/UNITAR Africa Database. Deichmann, U. and Lars Eklundh. Global Digital Datasets for Land Degradation Studies (1991), GRID Case Studies No.4. UNEP/GRID, Nairobi. UNEP. World Atlas of Desertification (1992). Edward Arnold: A division of Hodder and Stoughton, London. Projection :Geographic Type :Raster Format :IDRISI Related files :POPDENSL.E00, POPDENGR.E00 Associated files :POPDENS.DOC and POPDENS.PAL proprietary +NBId0233_101 Africa Population Density Model (Land Degradation Project), 1992 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848719-CEOS_EXTRA.umm_json The Africa Population density model represents ranges of population density of inhabitants per square kilometer. The estimated population densities are expressed on a regularly spaced latitude/longitude raster grid covering Africa with an approximate resolution of 10 km x 10 km at the Equator. The data set which is an assessment of one of the factors causing soil degradation, namely the spatial distribution and density of population. It was developed for the GEMS/UNITAR Africa Database and later used for GLASOD. The data sources include: 600 African towns and cities with figures standardized to 1988 values ( a combination of 479 cities from Birkbeck College and 363 cities in 51 African countries from PC Globe 3.0); UNEP/FAO population data from the 1984 Africa database; the Sierra Club Wilderness Area IUCN Protected Areas, used to delimit areas with extremely sparse populations and treated as having a density of less than one person per square kilometer. For methodology and further detail refer to references listed: UN Institute for Training & Research (UNITAR). GEMS/UNITAR Africa Database. Deichmann, U. and Lars Eklundh. Global Digital Datasets for Land Degradation Studies (1991), GRID Case Studies No.4. UNEP/GRID, Nairobi. UNEP. World Atlas of Desertification (1992). Edward Arnold: A division of Hodder and Stoughton, London. Projection :Geographic Type :Raster Format :IDRISI Related files :POPDENSL.E00, POPDENGR.E00 Associated files :POPDENS.DOC and POPDENS.PAL proprietary NBId0236_101 Africa Cattle Type (East Coast Fever Project), 1989 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847818-CEOS_EXTRA.umm_json The Cattle Type data set is part of the East Coast Fever (ECF) database covering sub-Saharan, East, and Central Africa. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by buffalo which is the main wildlife host of the ECF. The study was carried out in Nairobi by United Nations Environment Program, Global Resource Information Database (UNEP/GRID) in collaboration with the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary NBId0236_101 Africa Cattle Type (East Coast Fever Project), 1989 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847818-CEOS_EXTRA.umm_json The Cattle Type data set is part of the East Coast Fever (ECF) database covering sub-Saharan, East, and Central Africa. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by buffalo which is the main wildlife host of the ECF. The study was carried out in Nairobi by United Nations Environment Program, Global Resource Information Database (UNEP/GRID) in collaboration with the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848868-CEOS_EXTRA.umm_json "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the ""Wilson Henderson-Sellers land cover and soils for global circulation modeling project "" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called """"The Global Change Data Base"""". The Data Bank II is part of larger project called ""Global Ecosystems Database Project"". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software. References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI" proprietary NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848868-CEOS_EXTRA.umm_json "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the ""Wilson Henderson-Sellers land cover and soils for global circulation modeling project "" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called """"The Global Change Data Base"""". The Data Bank II is part of larger project called ""Global Ecosystems Database Project"". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software. References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI" proprietary NBId0270_101 Desertification Atlas (Africa) Maps 1-17 CEOS_EXTRA STAC Catalog 1990-01-01 1992-12-30 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847403-CEOS_EXTRA.umm_json INTRODUCTION Desertification/Land Degradation - The Background More than 6.1 billion hectares, over one third of the Earth's land area, is dryland. Nearly one billion hectares of this area are naturally hvperarid deserts, with very low biological productivity. The remaining 5.1 billion hectares are made up of arid, semiarid and dry subhumid areas, part of which have become desert since the dawn of civilization while other parts of these areas are still being degraded by human action today. These lands are the habitat and the source of livelihood for one quarter of the world's population. They are areas characterized by the persistent natural menace of recurrent drought, a natural hazard accentuated by imbalanced management of natural resources. Particularly acute drought years in the Sahelian region of Africa from 1968 to 1973, and their tragic effects on the peoples of the region, drew worldwide attention to the problems of human survival and development in drylands, particularly on desert margins. These problems have been addressed by the United Nations (UN) General Assembly, in conformity with the Charter of the United Nations. The UN General Assembly's Resolution 3202 (vi) of 1 May 1974 recommended that the international community undertake concrete and speedy measures to arrest desertification and assist the economic development of affected areas. The Economic and Social Council's Resolution 1878 (LVII) of 16 July 1974 requested all the concerned organizations of the UN system to pursue a broad attack on the drought problem. Decisions of the Governing Councils of the UN Development Programme (UNDP) and the UN Environment Programme (UNEP) emphasized the need for undertaking measures to check the spread of desert conditions. The General Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974, to initiate concerted international action to combat desertification and, in order to provide an impetus to this action, to convene a UN Conference on Desertification (UNCOD), between 29 August and 9 September 1977 in Nairobi, Kenya, which would produce an effective, comprehensive and coordinated programme for solving the problem. For the purposes of this atlas, desertification/land degradation is defined as: Land degradation in arid, semiarid and dry subhumid areas resulting mainly from adverse human impact. proprietary NBId0288_101 Desertification Atlas (Global) Maps 1-20 CEOS_EXTRA STAC Catalog 1990-01-01 1992-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848998-CEOS_EXTRA.umm_json INTRODUCTION Desertification/Land Degradation - The Background More than 6.1 billion hectares, over one third of the Earth's land area, is dryland. Nearly one billion hectares of this area are naturally hvperarid deserts, with very low biological productivity. The remaining 5.1 billion hectares are made up of arid, semiarid and dry subhumid areas, part of which have become desert since the dawn of civilization while other parts of these areas are still being degraded by human action today. These lands are the habitat and the source of livelihood for one quarter of the world's population. They are areas characterized by the persistent natural menace of recurrent drought, a natural hazard accentuated by imbalanced management of natural resources. Particularly acute drought years in the Sahelian region of Africa from 1968 to 1973, and their tragic effects on the peoples of the region, drew worldwide attention to the problems of human survival and development in drylands, particularly on desert margins. These problems have been addressed by the United Nations (UN) General Assembly, in conformity with the Charter of the United Nations. The UN General Assembly's Resolution 3202 (vi) of 1 May 1974 recommended that the international community undertake concrete and speedy measures to arrest desertification and assist the economic development of affected areas. The Economic and Social Council's Resolution 1878 (LVII) of 16 July 1974 requested all the concerned organizations of the UN system to pursue a broad attack on the drought problem. Decisions of the Governing Councils of the UN Development Programme (UNDP) and the UN Environment Programme (UNEP) emphasized the need for undertaking measures to check the spread of desert conditions. The General Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974, to initiate concerted international action to combat desertification and, in order to provide an impetus to this action, to convene a UN Conference on Desertification (UNCOD), between 29 August and 9 September 1977 in Nairobi, Kenya, which would produce an effective, comprehensive and coordinated programme for solving the problem. For the purposes of this atlas, desertification/land degradation is defined as: Land degradation in arid, semiarid and dry subhumid areas resulting mainly from adverse human impact. proprietary -NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates ALL STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates SCIOPS STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary +NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates ALL STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary NCALDAS_NOAH0125_D_2.0 NCA-LDAS Noah-3.3 Land Surface Model L4 Daily 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_D) at GES DISC GES_DISC STAC Catalog 1979-01-02 2016-12-31 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1454297282-GES_DISC.umm_json The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. An overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document. This NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0. NCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product. proprietary NCALDAS_NOAH0125_Trends_2.0 NCA-LDAS Noah-3.3 Land Surface Model L4 Trends 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_Trends) at GES DISC GES_DISC STAC Catalog 1979-10-01 2015-09-30 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1646132439-GES_DISC.umm_json The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. This dataset consists of a suite of historical trends in terrestrial hydrology over the conterminous United States estimated for the water years of 1980-2015 using the NCA-LDAS daily reanalysis. NCA-LDAS provides gridded daily outputs from the uncoupled Noah version 3.3 land surface model (LSM) at 1/8th degree resolution forced with NLDAS-2 meteorology (Xia et al., 2012), rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products (Jasinski et al., 2019; Kumar et al., 2019). Trends in annual hydrologic indicators are reported using the nonparametric Mann-Kendall test at p < 0.1 significance. An additional precipitation trend field (annual total), with no significance test applied, is included for comparison purposes. Collectively, these fields represent the bulk of the results presented in Jasinski et al. (2019). proprietary -NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC SCIOPS STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC ALL STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary -NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends ALL STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary +NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC SCIOPS STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends SCIOPS STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary +NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends ALL STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B ALL STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B SCIOPS STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data ALL STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data SCIOPS STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary -NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata ALL STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata NOAA_NCEI STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary +NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata ALL STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary NCEI DSI 2001_01_Not Applicable Climate Forecast System Version 2 (CFSv2) Operational Forecasts NOAA_NCEI STAC Catalog 2011-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093673-NOAA_NCEI.umm_json The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format. proprietary NCEI DSI 2002_01_Not Applicable Climate Forecast System Version 2 (CFSv2) Operational Analysis NOAA_NCEI STAC Catalog 2011-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093682-NOAA_NCEI.umm_json The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The CFSv2 Operational Analysis or Climate Data Assimilation System (CDAS), consist of all 6-Hourly CDAS, and the monthly CDAS monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format. proprietary NCEI DSI 3298_01 (original)_Not Applicable Climate Record Books Keyed Data NOAA_NCEI STAC Catalog 1850-01-01 1990-12-31 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893128-NOAA_NCEI.umm_json Climate Record Books (CRB) Data were keyed as part of the Climate Database Modernization Program (CDMP). These original keyed files as well as documentation relating to the format and keying process is available within the 3298_01 archive. The Northeast Regional Climate Center (NRCC) reformatted and performed quality control checks on the data, ensuring that the data could be used in high quality datasets and applications. Data and documentation for this data is available within the 3298_02 archive. The dataset consists of 171 stations that are located throughout the US. Variables include: maximum temperature, minimum temperature, average temperature, precipitation, and snowfall. Temporal resolution is daily, but observation times are not available for this dataset. However, data coverage varies by station. The records for individual stations range in length from 9 months to 121 years. Parts of the records may be duplicated in other, higher-priority ACIS data sources. proprietary @@ -12210,8 +12210,8 @@ NCEI DSI 9949_01_Not Applicable Automation of Field Operations and Services (AFO NCEI DSI: 2017_01_Not Applicable BP Public Release data for the Deepwater Horizon Response and Assessment in the Gulf of Mexico, dating from 2010-05-01 to 2013-09-30 NOAA_NCEI STAC Catalog 2010-05-01 2013-09-30 -98, 24, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2107094541-NOAA_NCEI.umm_json These BP Public Release data were gathered and utilized during the Response and Assessment phases of the Deepwater Horizon oil spill in the Gulf of Mexico. These data include datasets made public by BP that were standardize and integrated into NOAA's DIVER database. It includes discrete samples. The data were compiled by the NOAA Office of Response and Restoration (OR&R) and Trustees in the Data Integration, Visualization, Exploration, and Reporting (DIVER) data warehouse prior to being archived by the NOAA National Centers for Environmental Information (NCEI). The collection of files include environmental data used to determine the extent and magnitude of injury to the Gulf of Mexico ecosystem from the Deepwater Horizon oil spill. These data were used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP) developed through the Natural Resource Damage Assessment (NRDA) conducted as a result of the April 20, 2010 explosion and subsequent sinking of the Deepwater Horizon offshore drilling rig in the Gulf of Mexico, about 40 miles (60 km) southeast off the Louisiana coast, that led to a major oil spill in the region. proprietary NCEI WebARTIS: CARN_Not Applicable Carnegie Institution Atmospheric-Electricity and Meteorological Data NOAA_NCEI STAC Catalog 1916-01-01 1956-12-31 -172, -31, 116, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2107093956-NOAA_NCEI.umm_json The Department of Terrestrial Magnetism at the Carnegie Institute of Science conducted observations of atmospheric electricity and magnetic storms. In addition to observatories in Washington DC and Tucson AZ, the Department operated observatories in Watheroo, Australia, Huancayo, Peru, and Apia, Samoa. Included are climatological records as well as potential gradient and conductivity data. Observations were conducted between 1916-1956, contained in 92 boxes. In addition to monitoring magnetic events, the observatories initially studied the variation of the electric potential and conductivity of the air, earth currents, cosmic rays, and disturbances in the Sun's chromosphere. They also provided meteorological information for the benefit of the local regions. DTM developed and supplied equipment for Huancayo and Watheroo for magnetic, electrical, cosmic ray, and seismic investigations. proprietary NCEI WebARTIS: CCSP_Not Applicable Climate Change Science Program Collection NOAA_NCEI STAC Catalog 2007-01-01 2009-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093933-NOAA_NCEI.umm_json The Climate Change Science Program (CCSP) Collection consists of publications and other resources produced between 2007 and 2009 by the CCSP with the intention of providing sound climate science for national and international consideration to mitigate potential global change risks. The CCSP worked with a number of United States Agencies to collect climate data and research, culminating in 21 separate assessments, discussing the current state of the climate as well as expected changes and impacts. The archive only maintains a subset of these assessments. In 2009, the Program name changed to the US Global Change Research Program (USGCRP). Since 2009, USGCRP has released updated assessments to address climate change and impacts the global ecosystem. proprietary -NCEI WebARTIS: WBAN31_Not Applicable Adiabatic Charts ALL STAC Catalog 1929-01-01 1995-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093259-NOAA_NCEI.umm_json WBAN-31 is a form on which the Weather Bureau, Army and Navy recorded weather observations in the upper air as observed by rawinsonde and radiosonde. The collection includes thousands of these Adiabatic Charts, with the physical archive collection beginning primarily in the 1930s and ending in the mid 1990s and represents stations located throughout the world. The major parameters presented are pressure (Mb), height of pressure level, temperature (degrees C), dew point depression (degrees C), wind direction, and wind speed (knots). In the mid-1970s, the plotting of adiabatic charts was transitioned from paper forms to digital records. Many of the records in the latter part of the collection are computer printouts rather than the historical analog forms of the early 20th century. The bulk of this collection is available only on microfilm. proprietary NCEI WebARTIS: WBAN31_Not Applicable Adiabatic Charts NOAA_NCEI STAC Catalog 1929-01-01 1995-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093259-NOAA_NCEI.umm_json WBAN-31 is a form on which the Weather Bureau, Army and Navy recorded weather observations in the upper air as observed by rawinsonde and radiosonde. The collection includes thousands of these Adiabatic Charts, with the physical archive collection beginning primarily in the 1930s and ending in the mid 1990s and represents stations located throughout the world. The major parameters presented are pressure (Mb), height of pressure level, temperature (degrees C), dew point depression (degrees C), wind direction, and wind speed (knots). In the mid-1970s, the plotting of adiabatic charts was transitioned from paper forms to digital records. Many of the records in the latter part of the collection are computer printouts rather than the historical analog forms of the early 20th century. The bulk of this collection is available only on microfilm. proprietary +NCEI WebARTIS: WBAN31_Not Applicable Adiabatic Charts ALL STAC Catalog 1929-01-01 1995-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093259-NOAA_NCEI.umm_json WBAN-31 is a form on which the Weather Bureau, Army and Navy recorded weather observations in the upper air as observed by rawinsonde and radiosonde. The collection includes thousands of these Adiabatic Charts, with the physical archive collection beginning primarily in the 1930s and ending in the mid 1990s and represents stations located throughout the world. The major parameters presented are pressure (Mb), height of pressure level, temperature (degrees C), dew point depression (degrees C), wind direction, and wind speed (knots). In the mid-1970s, the plotting of adiabatic charts was transitioned from paper forms to digital records. Many of the records in the latter part of the collection are computer printouts rather than the historical analog forms of the early 20th century. The bulk of this collection is available only on microfilm. proprietary ND01_Age_Maps_1184_1 LBA-ECO ND-01 Primary Forests Land Cover Transition Maps, Rondonia, Brazil: 1975-1999 ORNL_CLOUD STAC Catalog 1975-06-19 1999-10-16 -64.64, -12.43, -61.18, -9.18 https://cmr.earthdata.nasa.gov/search/concepts/C2781575223-ORNL_CLOUD.umm_json This data set provides classified land cover transition images (maps) derived from Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery for Ariquemes, Luiza, and Ji-Paranao areas in Rondonia, Brazil, at 30-m resolution. Images depict the age relative to the year 2000, of cleared land from the date the land was cut, to the date when primary forests transitioned into nonforest class (for example, 25 = cut by 1975, or 25 years before the year 2000). Temporal changes in three regions are represented by 31 TM scenes acquired between 1984 and 1999, and a pair of MSS scenes from 1975 and 1978. Data are provided as three GeoTiff (*.tif) images, one for each of the three areas. proprietary ND01_Georectified_Products_1165_1 LBA-ECO ND-01 Landsat 28.5-m Land Cover Time Series, Rondonia, Brazil: 1984-2010 ORNL_CLOUD STAC Catalog 1984-06-24 2010-07-29 -64.6, -13.86, -58.8, -7.83 https://cmr.earthdata.nasa.gov/search/concepts/C2781412277-ORNL_CLOUD.umm_json This data set provides a 27-year land cover time series of 28.5-m resolution products derived from Landsat images for 80% of Rondonia, Brazil, for the period 1984 to 2010. Selected Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) images from the years 1984 through 2010, for seven path/row scenes (PortoVelho, Ariquemes, Jiparana, Luiza (or Urupa), Cacoal, Chapuingaia, and Vilhena) were mosaicked for each year. Each mosaicked image was georectified and classified into seven land-cover classes--savanna/rock, pasture, secondary forest, primary forest, cloud, urban, or water. This 27-year time series allows the long-term assessment of land-cover variation across the state. There are 27 GeoTIFF image files (.tif) and one accompanying .xml file for each GeoTIFF file, compressed and available as *.zip files, one file for each year for the period 1984-2010, with this data set. proprietary ND01_Land_Cover_Maps_1259_1 LBA-ECO ND-01 Land Cover Classification, Rondonia, Brazil: 1975-2000 ORNL_CLOUD STAC Catalog 1975-06-19 2000-06-28 -64.64, -12.43, -61.18, -9.18 https://cmr.earthdata.nasa.gov/search/concepts/C2781624044-ORNL_CLOUD.umm_json This data set provides a time series of land cover classifications for Ariquemes, Ji-Parana, and Luiza, research sites in Rondonia, Brazil. The land cover classifications are derived from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) sensors. The time period ranges from June 1975 through June 2000, but all areas do not have images for all the years. The images were classified into the following categories: 1. Primary upland forest, representing the dominant natural vegetation in the area; 2. Pasture and green pasture; 3. Second growth, dominated by small trees and shrubs with low species diversity and biomass relative to primary forest; 4. Soil/urban; 5. Rock/savanna; 6. Water; and 7. Cloud and smoke obscured. In addition, areas covered by rock and savanna were mapped and all areas outside of the overlap zone between all dates within a scene, and scene edges, were masked.There are 75 GeoTIFF files (.tif) with this data set which includes: classified images (*ful.tif) and a corresponding image mask (*ful_mask .tif) for each date (with the exception of 1978 and 1996 images for Ji-Parana, for which there are only ful_mask.tif files), and three mask files for rock, savannah, and scene edges, for each area. By area, there are 31 images for Ariquemes, 23 images for Ji-Parana, and 21 images for Luiza. proprietary @@ -12265,28 +12265,28 @@ ND30_REE_Water_Chemistry_1131_1 LBA-ECO ND-30 Water Chemistry, Rainfall Exclusio NDVI_Forest_Structure_1797_1 NDVI, Species Cover, and LAI, Burned and Unburned sites, Interior Alaska, 2017-2018 ORNL_CLOUD STAC Catalog 2017-08-29 2018-08-20 -149.96, 63.82, -144.96, 65.96 https://cmr.earthdata.nasa.gov/search/concepts/C2162189202-ORNL_CLOUD.umm_json This dataset provides leaf area index (LAI), tree species and canopy cover, normalized difference vegetation index (NDVI), and NDVI trends for boreal forests in interior Alaska, U.S. These data were collected to investigate NDVI trends with forest structure and composition as influenced by disturbance and succession. The data are from 102 sites surveyed in 2017 and 2018 and include locations with and without a fire since 1940. A time series of NDVI was developed from Landsat (1999-2018) to measure NDVI trends. The field data cover the period 2017-08-29 to 2018-08-20. The surveyed forest stands spanned a distance of over 425 km across interior Alaska. The sites were selected before visiting the field to include locations with and without a fire since 1940. Recently burned sites were selected to span a range of years since fire, while sites without a recent fire were selected to include a range of Landsat NDVI trends. For each year, the median NDVI during the growing season was calculated. Then, a simple linear regression trend was calculated for years 1999-2018. proprietary NEMSN5L2_001 NEMS/Nimbus-5 Level 2 Output Data V001 (NEMSN5L2) at GES DISC GES_DISC STAC Catalog 1972-12-17 1973-10-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1990675367-GES_DISC.umm_json NEMSN5L2 is the Nimbus-5 or Nimbus-E Microwave Spectrometer (NEMS) Level-2 Output Data product and contains surface reflectivity, water vapor, liquid water, layer thickness, temperature at standard pressure levels, surface brightness temperature, and surface type information, as well as the input antenna and brightness temperatures at 5 microwave channels (H2O channels 22.235 and 31.4 GHz, and O2 channels 53.65, 54.9 and 58.8 GHz). The NEMS instrument views the nadir with a footprint is a 180-km diameter circle on the earth's surface. Data are available for the time period from 1972-12-17 to 1973-10-31 with data for about five days stored in a single binary data file. The principal investigator for the NEMS experiment was David H. Staelin from MIT. An advanced version of this instrument, the Scanning Microwave Spectrometer (SCAMS) was flown on the subsequent Nimbus-6 satellite. proprietary NES-LTER_0 Northeast U.S. Shelf (NES), Long-Term Ecological Research (LTER) OB_DAAC STAC Catalog 2018-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208430341-OB_DAAC.umm_json The Northeast U.S. Shelf (NES) Long-Term Ecological Research (LTER) project integrates observations, experiments, and models to understand and predict how planktonic food webs are changing, and how those changes impact the productivity of higher trophic levels. The NES-LTER is co-located with the Northeast U.S. Continental Shelf Large Marine Ecosystem, spanning the Middle Atlantic Bight and Gulf of Maine. Our focal cross-shelf transect extends about 150 km southward from Martha's Vineyard, MA, to just beyond the shelf break. proprietary -NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia SCIOPS STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary -NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary +NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia SCIOPS STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary -NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary +NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary -NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary +NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary -NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary +NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary -NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary +NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary +NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NEUROST_SSH-SST_L4_V2024.0_2024.0 Daily NeurOST L4 Sea Surface Height and Surface Geostrophic Currents POCLOUD STAC Catalog 2010-01-01 2024-06-15 -180, -70, 180, 79.9 https://cmr.earthdata.nasa.gov/search/concepts/C3085229833-POCLOUD.umm_json This Daily NeurOST Level 4 Sea Surface Height and Surface Geostrophic Currents analysis product from the University of Washington and JPL was mapped by a neural network trained with sparse Level 3 nadir altimetry observations (CMEMS, E.U. Copernicus Marine Service Information) and the MUR Level 4 gridded sea surface temperature product (PO.DAAC). proprietary NEWS_WEB_ACLIM_1.0 NASA Energy and Water cycle Study (NEWS) Annual Climatology of the 1st decade of the 21st Century V1.0 (NEWS_WEB_ACLIM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233781718-GES_DISC.umm_json NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the annual climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/year, W/m^2, cm/year, and mm/day), in three formats (NetCDF, xlsx, and csv). proprietary NEWS_WEB_MCLIM_1.0 NASA Energy and Water cycle Study (NEWS) Monthly Climatology of the 1st decade of the 21st Century V1.0 (NEWS_WEB_MCLIM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233781717-GES_DISC.umm_json NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the monthly climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/month, W/m^2, cm/month, and mm/day), in three formats (NetCDF, xlsx, and csv). proprietary NEX-DCP30_1 Downscaled 30 Arc-Second CMIP5 Climate Projections for Studies of Climate Change Impacts in the United States NCCS STAC Catalog 1950-01-01 2099-12-31 -125.0208333, 24.0625, -66.4791667, 49.9375 https://cmr.earthdata.nasa.gov/search/concepts/C1542175061-NCCS.umm_json This NASA dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future climate patterns and climate impacts at the scale of individual neighborhoods and communities. This dataset is intended for use in scientific research only, and use of this dataset for other purposes, such as commercial applications, and engineering or design studies is not recommended without consultation with a qualified expert. Community feedback to improve and validate the dataset for modeling usage is appreciated. Email comments to bridget@climateanalyticsgroup.org. Dataset File Name: NASA Earth Exchange (NEX) Downscaled Climate Projections (NEXDCP30), https://portal.nccs.nasa.gov/portal_home/published/NEX.html proprietary NEX-GDDP_1 NASA Earth Exchange Global Daily Downscaled Projections NCCS STAC Catalog 1950-01-01 2100-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1374483929-NCCS.umm_json The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). proprietary NFRDI_0 National Fisheries Research and Development Institute (NFRDI) OB_DAAC STAC Catalog 2000-02-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360518-OB_DAAC.umm_json Measurements made by the National Fisheries Research and Development Institute (NFRDI), Ministry of Oceans and Fisheries for Korea, in the East China Sea in 2000. proprietary -"NGA178 - _1.0" Advanced Terrestrial Simulator SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary "NGA178 _1.0" Advanced Terrestrial Simulator ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary +"NGA178 + _1.0" Advanced Terrestrial Simulator SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary "NGA183 _1.0" Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388529-SCIOPS.umm_json Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary "NGA183 @@ -12300,12 +12300,12 @@ NIH-NSF_Lake_Erie_0 Lake Erie optical measurements OB_DAAC STAC Catalog 2013-08- NIMBUS7_ERB_Ch10C_TSI_NAT_1 Nimbus-7 Total Solar Irradiance Data in Native Format LARC_ASDC STAC Catalog 1978-11-16 1993-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1373953856-LARC_ASDC.umm_json The NIMBUS7_ERB_Ch10C_TSI_NAT data set is the Nimbus-7 Channel 10C (Ch10C) Total Solar Irradiance (TSI) aboard the Earth Radiation Budget (ERB) satellite Data in Native (NAT) format.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure. proprietary NIMBUS7_ERB_SEFDT_1 Nimbus-7 Solar and Earth Flux Data in Native Binary Format LARC_ASDC STAC Catalog 1978-01-01 1993-12-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C4211374-LARC_ASDC.umm_json The NIMBUS7_ERB_SEFDT data set is the Solar and Earth Flux Data Tape (SEFDT) generated from Nimbus-7 Earth Radiation Budget (ERB) instrument data. The main purpose of the SEFDT program was to produce a tape containing the solar data and the wide angle terrestrial flux data only. On Nimbus-7, the ERB had two total irradiance channels, Channel 3 and Channel 10C.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure. proprietary NIMBUS7_NFOV_MLCE_1 Nimbus-7 Narrow Field of View (NFOV) Maximum Likelihood Cloud Estimation (MLCE) Data in Native Format LARC_ASDC STAC Catalog 1979-05-01 1980-05-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1328028152-LARC_ASDC.umm_json NIMBUS7_NFOV_MLCE data are Nimbus 7 Narrow Field of View (NFOV) Maximum Likelihood Cloud Estimation (MLCE) Data in Native Format.The NIMBUS7_NFOV_MLCE data set uses the Nimbus-7 measurements and the MLCE algorithm for better regional and temporal resolution. The Earth Radiation Budget (ERB) parameters, derived from the Nimbus-7 scanner measurements, were rederived in 1990 using a Maximum Likelihood Cloud Estimation (MLCE) algorithm similar, but not identical, to the Earth Radiation Budget Experiment (ERBE) algorithm. Daily and monthly means are presented on two commensurate equal area world grids: (167 km by 167 km) and (500 km by 500 km). The MLCE procedure also yielded a rough estimate of the regional cloud cover.The scanner took measurements from November 16, 1978 through June 20, 1980; however, only 13 months (May 1979 through May 1980) of data sampling were reprocessed using the Sorting into Angular Bins and MLCE algorithms. There was poorer temporal sampling during the first five months of the experiment.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure. proprietary -NIPR-GEO-1 Airborne Magnetic Survey Data in Antarctica by JARE SCIOPS STAC Catalog 1980-01-01 20, -72, 60, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214584952-SCIOPS.umm_json The digital data which can be supplied are total intensity raw data, and not reduced to magnetic anomaly data. However, the user can analyze the data by him/herself with the Data Reports. The data processing is still being made at NIPR. proprietary NIPR-GEO-1 Airborne Magnetic Survey Data in Antarctica by JARE ALL STAC Catalog 1980-01-01 20, -72, 60, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214584952-SCIOPS.umm_json The digital data which can be supplied are total intensity raw data, and not reduced to magnetic anomaly data. However, the user can analyze the data by him/herself with the Data Reports. The data processing is still being made at NIPR. proprietary +NIPR-GEO-1 Airborne Magnetic Survey Data in Antarctica by JARE SCIOPS STAC Catalog 1980-01-01 20, -72, 60, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214584952-SCIOPS.umm_json The digital data which can be supplied are total intensity raw data, and not reduced to magnetic anomaly data. However, the user can analyze the data by him/herself with the Data Reports. The data processing is still being made at NIPR. proprietary NIPR_GEO_SEIS_SEAL_MIZUHO Acitve source digital seismic waveforms by SEAL exploration ALL STAC Catalog 2000-01-01 38, -70, 45, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214590137-SCIOPS.umm_json "Deep Seismic Surveys (DSS) were carried out in 2000 and 2002 austral summers on the continental ice-sheet of the Lutzow-Holm Complex (LHC), Eastern Dronning Maud Land, East Antarctica . The surveys were carried out as a program of the ""Structure and Evolution of the East Antarctic Lithosphere (SEAL)"" by JARE. Detailed crustal velocity models and reflection sections were obtained in the LHC. In both surveys, more than 170 plant-type 2 Hz geophones were installed on the continental ice-sheet totally 190 km in length. A total of 8,300kg dynamite charge at the fourteen sites on the Mizuho Plateau gave information concerning the deep structure of a continental margin of the LHC. Archived digital waveforms are available from Library Server of Polar Data Center of NIPR." proprietary NIPR_GEO_SEIS_SEAL_MIZUHO Acitve source digital seismic waveforms by SEAL exploration SCIOPS STAC Catalog 2000-01-01 38, -70, 45, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214590137-SCIOPS.umm_json "Deep Seismic Surveys (DSS) were carried out in 2000 and 2002 austral summers on the continental ice-sheet of the Lutzow-Holm Complex (LHC), Eastern Dronning Maud Land, East Antarctica . The surveys were carried out as a program of the ""Structure and Evolution of the East Antarctic Lithosphere (SEAL)"" by JARE. Detailed crustal velocity models and reflection sections were obtained in the LHC. In both surveys, more than 170 plant-type 2 Hz geophones were installed on the continental ice-sheet totally 190 km in length. A total of 8,300kg dynamite charge at the fourteen sites on the Mizuho Plateau gave information concerning the deep structure of a continental margin of the LHC. Archived digital waveforms are available from Library Server of Polar Data Center of NIPR." proprietary -NIPR_PMG_AIR_ARCHIVE_ANT Air samples for archive SCIOPS STAC Catalog 1995-02-01 2009-01-31 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590122-SCIOPS.umm_json Air samples for archive proprietary NIPR_PMG_AIR_ARCHIVE_ANT Air samples for archive ALL STAC Catalog 1995-02-01 2009-01-31 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590122-SCIOPS.umm_json Air samples for archive proprietary +NIPR_PMG_AIR_ARCHIVE_ANT Air samples for archive SCIOPS STAC Catalog 1995-02-01 2009-01-31 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590122-SCIOPS.umm_json Air samples for archive proprietary NISE_2 Near-Real-Time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent V002 NSIDC_ECS STAC Catalog 1995-05-04 2009-09-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1647528934-NSIDC_ECS.umm_json "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 2 product contains SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F13 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For DMSP-F18, SSMIS-derived data, see NISE Version 5." proprietary NISE_3 Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent V003 NSIDC_ECS STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1997866870-NSIDC_ECS.umm_json "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 3 product contains DMSP-F16, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F16 satellite. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2." proprietary NISE_4 Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent V004 NSIDC_ECS STAC Catalog 2009-08-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1450086509-NSIDC_ECS.umm_json "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 4 product contains DMSP-F17, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F17 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2." proprietary @@ -12435,8 +12435,8 @@ NPP_WBW_819_2 Walker Branch Watershed Vegetation Inventory, 1967-2006, R1 ORNL_C NPP_WOODY_655_2 NPP Multi-Biome: Production and Mortality for Eastern US Forests, 1962-1996, R1 ORNL_CLOUD STAC Catalog 1962-01-01 1996-12-31 -100, 25, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2755666809-ORNL_CLOUD.umm_json There are two data files (tab-delimited .txt format) with this data set that provide estimates of above-ground biomass per county; county-level annual above-ground biomass growth, removals (harvest), and mortality of woody biomass per hectare; county-level total annual above-ground woody biomass production per hectare; forest area per county; mortality (%) in forests within each county; and total annual production and mortality per county. The data provide annual mean above-ground wood increments for temperate forests in 1,956 counties of the 28 eastern US states. The data are derived from forest inventory data from 1960s to 1990s that were collected from an extensive network of permanent inventory plots as part of the US Department of Agriculture Forest Service Forest Inventory and Analysis (FIA). Based on the analysis of the above-ground production data (Brown and Schroeder, 1999), above-ground production of woody biomass (APWB) for hardwood forests ranged from 0.6 to 28 Mg/ha/yr and averaged 5.2 Mg/ha/yr. For softwood forests, APWB ranged from 0.2 to 31 Mg/ha/yr and averaged 4.9 Mg/ha/yr. APWB was generally highest in southeastern and southern counties, mostly along an arc from southern Virginia to Louisiana and eastern Texas. No clear spatial pattern of mortality of woody biomass (MWB) existed, except for a distinct area of high mortality in South Carolina as a result of Hurricane Hugo in 1989. For hardwood forests, MWB ranged from 0 to 15 Mg/ha/yr and averaged 1.1 Mg/ha/yr. The average MWB for softwood forests was 0.6 Mg/ha/yr with a range of 0 to 10 Mg/ha/yr. The rate of above-ground MWB averaged <1%/yr for both hardwood and softwood forests. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2003. proprietary NPP_XLN_156_2 NPP Grassland: Xilingol, China, 1980-1989, R1 ORNL_CLOUD STAC Catalog 1978-01-01 1989-12-31 116.63, 43.72, 116.63, 43.72 https://cmr.earthdata.nasa.gov/search/concepts/C2751940176-ORNL_CLOUD.umm_json This data set provides two data files in text format (.txt). One file contains bi-weekly measurements of above-ground live biomass recorded during the growing season (early May to early October) from 1980 through 1989 on a cold desert steppe at the Inner Mongolia Grassland Research Station of the Chinese Academy of Sciences within the Xilingol Biosphere Reserve. The second file contains monthly and annual climate data recorded at the study site from 1978 through 1989. The study site contains grassland steppes of Leymus chinense and Stipa grandis which are the dominant vegetation types, respectively, in the Eastern Eurasian steppe zone (semi-arid and sub-humid) and the middle Eurasian steppe zone (semi-arid). Both steppes provide good livestock forage and are used mainly as natural grazing lands. Above-ground net primary production (ANPP) was estimated by summing peak live biomass of each of 5 species categories. Peak live biomass of L. chinense steppe occurred between late July and late August and averaged 182.68 g/m2 between 1980 and 1988 while that of S. grandis steppe occurred in mid August to early September and averaged 144.43 g/m2 over the same time period. Mean ANPP for L. chinense steppe during 1980-1989 was 248.63 g/m2/yr. ANPP for S. grandis steppe was not calculated. Data are only provided for the Leymus chinense steppe in this data set.Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1996. proprietary NPP_surfaces_750_1 BigFoot NPP Surfaces for North and South American Sites, 2000-2004 ORNL_CLOUD STAC Catalog 2000-01-01 2004-12-31 -156.61, -2.86, -54.96, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751481549-ORNL_CLOUD.umm_json The BigFoot project gathered Net Primary Production (NPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html. proprietary -NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002 Alien Plants Ranking System (APRS) Implementation Team ALL STAC Catalog 1970-01-01 -115, 30, -85, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231551762-CEOS_EXTRA.umm_json The Alien Plants Ranking System (APRS) is a computer-implemented system to help land managers make difficult decisions concerning invasive nonnative plants. The management of invasive plants is difficult, expensive, and requires a long-term commitment. Therefore, land managers must focus their limited resources, targeting the species that cause major impacts or threats to resources within their management, or the species that impede attainment of management goals. APRS provides an analytical tool to separate the innocuous species from the invasive ones (typically around 10% of the nonnative species). APRS not only helps identify those species that currently impact a site, but also those that have a high potential do so in the future. Finally, the system addresses the feasibility of control of each species, enabling the manager to weigh the costs of control against the level of impact. This system has been developed and tested primarily in grassland and prairie parks in the central United States. proprietary NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002 Alien Plants Ranking System (APRS) Implementation Team CEOS_EXTRA STAC Catalog 1970-01-01 -115, 30, -85, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231551762-CEOS_EXTRA.umm_json The Alien Plants Ranking System (APRS) is a computer-implemented system to help land managers make difficult decisions concerning invasive nonnative plants. The management of invasive plants is difficult, expensive, and requires a long-term commitment. Therefore, land managers must focus their limited resources, targeting the species that cause major impacts or threats to resources within their management, or the species that impede attainment of management goals. APRS provides an analytical tool to separate the innocuous species from the invasive ones (typically around 10% of the nonnative species). APRS not only helps identify those species that currently impact a site, but also those that have a high potential do so in the future. Finally, the system addresses the feasibility of control of each species, enabling the manager to weigh the costs of control against the level of impact. This system has been developed and tested primarily in grassland and prairie parks in the central United States. proprietary +NPWRC_alienplantsrankingsystem_version 5.1, Version 30 Sep 2002 Alien Plants Ranking System (APRS) Implementation Team ALL STAC Catalog 1970-01-01 -115, 30, -85, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231551762-CEOS_EXTRA.umm_json The Alien Plants Ranking System (APRS) is a computer-implemented system to help land managers make difficult decisions concerning invasive nonnative plants. The management of invasive plants is difficult, expensive, and requires a long-term commitment. Therefore, land managers must focus their limited resources, targeting the species that cause major impacts or threats to resources within their management, or the species that impede attainment of management goals. APRS provides an analytical tool to separate the innocuous species from the invasive ones (typically around 10% of the nonnative species). APRS not only helps identify those species that currently impact a site, but also those that have a high potential do so in the future. Finally, the system addresses the feasibility of control of each species, enabling the manager to weigh the costs of control against the level of impact. This system has been developed and tested primarily in grassland and prairie parks in the central United States. proprietary NPWRC_effectsoffireonbirdpops Effects of Fire on Bird Populations in Mixed-grass Prairie CEOS_EXTRA STAC Catalog 1997-01-01 1997-12-31 -101, 46.5, -97, 48.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549248-CEOS_EXTRA.umm_json The mixed-grass prairie is one of the largest ecosystems in North America, originally covering about 69 million hectares (Bragg and Steuter 1995). Although much of the natural vegetation has been replaced by cropland and other uses (Samson and Knopf 1994, Bragg and Steuter 1995), significant areas have been preserved in national wildlife refuges, waterfowl production areas, state game management areas, and nature preserves. Mixed-grass prairie evolved with fire (Bragg 1995), and fire is frequently used as a management tool for prairie (Berkey et al. 1993). Much of the mixed-grass prairie that has been protected is managed to enhance the reproductive success of waterfowl and other gamebirds, but nongame birds now are receiving increasing emphasis. Despite the importance of the area to numerous species of birds and the aggressive management applied to many sites, relatively little is known about the effects of fire on the suitability of mixed-grass prairie for breeding birds. Several studies have examined effects of fire on breeding birds in the tallgrass prairie (e.g., Tester and Marshall 1961, Eddleman 1974, Halvorsen and Anderson 1983, Westenmeier and Buhnerkempe 1983, Zimmerman 1992, Herkert 1994), in western sagebrush grasslands (Peterson and Best 1987), and in shrubsteppe (Bock and Bock 1987). Studies of fire effects in the mixed-grass prairie are limited. Huber and Steuter (1984) examined the effects on birds during the breeding season following an early-May prescribed burn on a 122-ha site in South Dakota. They contrasted the bird populations on that site to those on a nearby 462-ha unburned site that had been lightly grazed by bison (Bison bison). Pylypec (1991) monitored breeding bird populations occurring in fescue prairies of Canada on a single 12.9-ha burned area and on an adjacent 5.6-ha unburned fescue prairie for three years after a prescribed burn. proprietary NRMSC_carnivorerecolonisation Carnivore Re-Colonisation: Reality, Possibility and a Non-Equilibrium Century for Grizzly Bears in the Southern Yellowstone Ecosystem CEOS_EXTRA STAC Catalog 1900-01-01 2000-12-31 -111, 44, -110, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231549197-CEOS_EXTRA.umm_json Most large native carnivores have experienced range contractions due to conflicts with humans, although neither rates of spatial collapse nor expansion have been well characterized. In North America, the grizzly bear (Ursus arctos) once ranged from Mexico northward to Alaska, however its range in the continental United States has been reduced by 95-98%. Under the U.S. Endangered Species Act, the Yellowstone grizzly bear population has re-colonized habitats outside Yellowstone National Park. We analyzed historical and current records, including data on radio-collared bears, (i) to evaluate changes in grizzly bear distribution in the southern Greater Yellowstone Ecosystem over a 100-year period, (ii) to utilise historical rates of recolonization to project future expansion trends and (iii) to evaluate the reality of future expansion based on human limitations and land use. Analysis of distribution in 20-year increments reflects range reduction from south to north (1900-1940) and expansion to the south (1940-2000). Expansion was exponential and the area occupied by grizzly bears doubled approximately every 20 years. A complementary analysis of bear occurrence in Grand Teton National Park also suggests an unprecedented period of rapid expansion during the last 20-30 years. The grizzly bear population currently has re-occupied about 50% of the southern GYE. Based on assumptions of continued protection and ecological stasis, our model suggests total occupancy in 25 years. Alternatively, extrapolation of linear expansion rates from the period prior to protection suggests total occupancy could take > 100 years. Analyses of historical trends can be useful as a restoration tool because they enable a framework and timeline to be constructed to pre-emptively address the social challenges affecting future carnivore recovery. One of the purposes of the dataset is to predict when grizzly bear occupation of Southern Yellowstone Ecosystem will be total. We focused on a 24,000 square kilometer mosaic of mostly public land that is managed by various federal and state agencies. Our analysis of changes in grizzly bear distribution during 1900-2000 was divided into 20-year periods. For each, we used various data sources for grizzly bear occurrence to create digital maps of bear distribution using ArcView GIS 32. (ESRI, Redlands, CA) We digitized reports, interviews, conflicts, mortalities and observations as points. We created a polygon for the 1920 source data, a hand-drawn distribution map by Merriam (1922). More methodology given in Pyare, 2004 paper. proprietary NRSCC_GLASS_ FAPAR_MODIS_0.05D_11 NRSCC_GLASS_ FAPAR_MODIS_0.05D NRSCC STAC Catalog 2010-02-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2205351149-NRSCC.umm_json This Global LAnd Surface Satellite (GLASS) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product was generated using MODIS products. proprietary @@ -12477,21 +12477,21 @@ NSCAT_LEVEL_2_V2_2 NSCAT Level 2 Ocean Wind Vector Geophysical Data Record POCLO NSCAT_LEVEL_3_BROWSE_IMAGES_2 NSCAT Level 3 Daily Gridded Ocean Surface Wind Vector Browse Images (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-29 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2617226745-POCLOUD.umm_json This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203. proprietary NSCAT_LEVEL_3_V2_2 NSCAT Level 3 Daily Gridded Ocean Surface Wind Vectors (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-30 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2617226815-POCLOUD.umm_json The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. proprietary NSCAT_W25_RMGDR_V2_2 NSCAT High Resolution R-MGDR, Selected Ocean Wind Vectors (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617226887-POCLOUD.umm_json The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function. proprietary -NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary +NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary NSF-ANT02-28842 Boron in Antarctic granulite-facies rocks: under what conditions is boron retained in the middle crust? AMD_USAPDC STAC Catalog 2003-06-01 2009-11-30 76, -69.5, 76.5, -69.3 https://cmr.earthdata.nasa.gov/search/concepts/C2534797156-AMD_USAPDC.umm_json This award, provided by the Antarctic Geology and Geophysics Program of the Office of Polar Programs, supports a project to investigate the role and fate of Boron in high-grade metamorphic rocks of the Larsemann Hills region of Antarctica. Trace elements provide valuable information on the changes sedimentary rocks undergo as temperature and pressure increase during burial. One such element, boron, is particularly sensitive to increasing temperature because of its affinity for aqueous fluids, which are lost as rocks are buried. Boron contents of unmetamorphosed pelitic sediments range from 20 to over 200 parts per million, but rarely exceed 5 parts per million in rocks subjected to conditions of the middle and lower crust, that is, temperatures of 700 degrees C or more in the granulite-facies, which is characterized by very low water activities at pressures of 5 to 10 kbar (18-35 km burial). Devolatization reactions with loss of aqueous fluid and partial melting with removal of melt have been cited as primary causes for boron depletion under granulite-facies conditions. Despite the pervasiveness of both these processes, rocks rich in boron are locally found in the granulite-facies, that is, there are mechanisms for retaining boron during the metamorphic process. The Larsemann Hills, Prydz Bay, Antarctica, are a prime example. More than 20 lenses and layered bodies containing four borosilicate mineral species crop out over a 50 square kilometer area, which thus would be well suited for research on boron-rich granulite-facies metamorphic rocks. While most investigators have focused on the causes for loss of boron, this work will investigate how boron is retained during high-grade metamorphism. Field observations and mapping in the Larsemann Hills, chemical analyses of minerals and their host rocks, and microprobe age dating will be used to identify possible precursors and deduce how the precursor materials recrystallized into borosilicate rocks under granulite-facies conditions. The working hypothesis is that high initial boron content facilitates retention of boron during metamorphism because above a certain threshold boron content, a mechanism 'kicks in' that facilitates retention of boron in metamorphosed rocks. For example, in a rock with large amounts of the borosilicate tourmaline, such as stratabound tourmalinite, the breakdown of tourmaline to melt could result in the formation of prismatine and grandidierite, two borosilicates found in the Larsemann Hills. This situation is rarely observed in rocks with modest boron content, in which breakdown of tourmaline releases boron into partial melts, which in turn remove boron when they leave the system. Stratabound tourmalinite is associated with manganese-rich quartzite, phosphorus-rich rocks and sulfide concentrations that could be diagnostic for recognizing a tourmalinite protolith in a highly metamorphosed complex where sedimentary features have been destroyed by deformation. Because partial melting plays an important role in the fate of boron during metamorphism, our field and laboratory research will focus on the relationship between the borosilicate units, granite pegmatites and other granitic intrusives. The results of our study will provide information on cycling of boron at deeper levels in the Earth's crust and on possible sources of boron for granites originating from deep-seated rocks. An undergraduate student will participate in the electron microprobe age-dating of monazite and xenotime as part of a senior project, thereby integrating the proposed research into the educational mission of the University of Maine. In response to a proposal for fieldwork, the Australian Antarctic Division, which maintains Davis station near the Larsemann Hills, has indicated that they will support the Antarctic fieldwork. proprietary NSF-ANT04-36190_1 Biodiversity, Buoyancy and Morphological Studies of Non-Antarctic Notothenioid Fishes AMD_USAPDC STAC Catalog 2005-04-01 2009-03-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069293-AMD_USAPDC.umm_json Patterns of biodiversity, as revealed by basic research in organismal biology, may be derived from ecological and evolutionary processes expressed in unique settings, such as Antarctica. The polar regions and their faunas are commanding increased attention as declining species diversity, environmental change, commercial fisheries, and resource management are now being viewed in a global context. Commercial fishing is known to have a direct and pervasive effect on marine biodiversity, and occurs in the Southern Ocean as far south as the Ross Sea. The nature of fish biodiversity in the Antarctic is different than in all other ocean shelf areas. Waters of the Antarctic continental shelf are ice covered for most of the year and water temperatures are nearly constant at -1.5 C. In these waters components of the phyletically derived Antarctic clade of Notothenioids dominate fish diversity. In some regions, including the southwestern Ross Sea, Notothenioids are overwhelmingly dominant in terms of number of species, abundance, and biomass. Such dominance by a single taxonomic group is unique among shelf faunas of the world. In the absence of competition from a taxonomically diverse fauna, Notothenioids underwent a habitat or depth related diversification keyed to the utilization of unfilled niches in the water column, especially pelagic or partially pelagic zooplanktivory and piscivory. This has been accomplished in the absence of a swim bladder for buoyancy control. They also may form a special type of adaptive radiation known as a species flock, which is an assemblage of a disproportionately high number of related species that have evolved rapidly within a defined area where most species are endemic. Diversification in buoyancy is the hallmark of the notothenioid radiation. Buoyancy is the feature of notothenioid biology that determines whether a species lives on the substrate, in the water column or both. Buoyancy also influences other key aspects of life history including swimming, feeding and reproduction and thus has implications for the role of the species in the ecosystem. With similarities to classic evolutionary hot spots, the Antarctic shelf and its Notothenioid radiation merit further exploration. The 2004 'International Collaborative Expedition to collect and study Fish Indigenous to Sub-Antarctic Habitats,' or, 'ICEFISH,' provided a platform for collection of notothenioid fishes from sub-Antarctic waters between South America and Africa, which will be examined in this project. This study will determine buoyancy for samples of all notothenioid species captured during the ICEFISH cruise. This essential aspect of the biology is known for only 19% of the notothenioid fauna. Also, the gross and microscopic anatomy of brains and sense organs of the phyletically basal families Bovichtidae, Eleginopidae, and of the non-Antarctic species of the primarily Antarctic family Nototheniidae will be examined. The fish biodiversity and endemicity in poorly known localities along the ICEFISH cruise track, seamounts and deep trenches will be quantified. Broader impacts include improved information for comprehending and conserving biodiversity, a scientific and societal priority. proprietary -NSF-ANT04-39906_1 Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change ALL STAC Catalog 2005-09-15 2009-08-31 162, -78, 168, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532069615-AMD_USAPDC.umm_json During previous NSF-sponsored research, the PI's discovered that southern elephant seal colonies once existed along the Victoria Land coast (VLC) of Antarctica, a region where they are no longer observed. Molted seal skin and hair occur along 300 km of coastline, more than 1000 km from any extant colony. The last record of a seal at a former colony site is at ~A.D. 1600. Because abandonment occurred prior to subantarctic sealing, disappearance of the VLC colony probably was due to environmental factors, possibly cooling and encroachment of land-fast, perennial sea ice that made access to haul-out sites difficult. The record of seal inhabitation along the VLC, therefore, has potential as a proxy for climate change. Elephant seals are a predominantly subantarctic species with circumpolar distribution. Genetic studies have revealed significant differentiation among populations, particularly with regard to that at Macquarie I., which is the extant population nearest to the abandoned VLC colony. Not only is the Macquarie population unique genetically, but it is has undergone unexplained decline of 2%/yr over the last 50 years3. In a pilot study, genetic analyses showed a close relationship between the VLC seals and those at Macquarie I. An understanding of the relationship between the two populations, as well as of the environmental pressures that led to the demise of the VLC colonies, will provide a better understanding of present-day population genetic structure, the effect of environmental change on seal populations, and possibly the reasons underlying the modern decline at Macquarie Island. This project addresses several key research problems: (1) Why did elephant seals colonize and then abandon the VLC? (2) What does the elephant seal record reveal about Holocene climate change and sea-ice conditions? (3) What were the foraging strategies of the seals and did these strategies change over time as climate varied? (4) How does the genetic structure of the VLC seals relate to extant populations? (5) How did genetic diversity change over time and with colony decline? (6) Using ancient samples to estimate mtDNA mutation rates, what can be learned about VLC population dynamics over time? (7) What was the ecological relationship between elephant seals and Adelie penguins that occupied the same sites, but apparently at different times? The proposed work includes the professional training of young researchers and incorporation of data into graduate and undergraduate courses. Because of extreme isolation of the Antarctic continent since the Early Oligocene, one expects a unique invertebrate benthic fauna with a high degree of endemism. Yet some invertebrate taxa that constitute important ecological components of sedimentary benthic communities include more than 40 percent non-endemic species (e.g., benthic polychaetes). To account for non-endemic species, intermittent genetic exchange must occur between Antarctic and other (e.g. South American) populations. The most likely mechanism for such gene flow, at least for in-faunal and mobile macrobenthos, is dispersal of planktonic larvae across the sub- Antarctic and Antarctic polar fronts. To test for larval dispersal as a mechanism of maintaining genetic continuity across polar fronts, the scientists propose to (1) take plankton samples along transects across Drake passage during both the austral summer and winter seasons while concurrently collecting the appropriate hydrographic data. Such data will help elucidate the hydrographic mechanisms that allow dispersal across Drake Passage. Using a molecular phylogenetic approach, they will (2) compare seemingly identical adult forms from Antarctic and South America continents to identify genetic breaks, historical gene flow, and control for the presence of cryptic species. (3) Similar molecular tools will be used to relate planktonic larvae to their adult forms. Through this procedure, they propose to link the larval forms respectively to their Antarctic or South America origins. The proposed work builds on previous research that provides the basis for this effort to develop a synthetic understanding of historical gene flow and present day dispersal mechanism in South American/Drake Passage/ Antarctic Peninsular region. Furthermore, this work represents one of the first attempts to examine recent gene flow in Antarctic benthic invertebrates. Graduate students and a postdoctoral fellow will be trained during this research proprietary NSF-ANT04-39906_1 Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change AMD_USAPDC STAC Catalog 2005-09-15 2009-08-31 162, -78, 168, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532069615-AMD_USAPDC.umm_json During previous NSF-sponsored research, the PI's discovered that southern elephant seal colonies once existed along the Victoria Land coast (VLC) of Antarctica, a region where they are no longer observed. Molted seal skin and hair occur along 300 km of coastline, more than 1000 km from any extant colony. The last record of a seal at a former colony site is at ~A.D. 1600. Because abandonment occurred prior to subantarctic sealing, disappearance of the VLC colony probably was due to environmental factors, possibly cooling and encroachment of land-fast, perennial sea ice that made access to haul-out sites difficult. The record of seal inhabitation along the VLC, therefore, has potential as a proxy for climate change. Elephant seals are a predominantly subantarctic species with circumpolar distribution. Genetic studies have revealed significant differentiation among populations, particularly with regard to that at Macquarie I., which is the extant population nearest to the abandoned VLC colony. Not only is the Macquarie population unique genetically, but it is has undergone unexplained decline of 2%/yr over the last 50 years3. In a pilot study, genetic analyses showed a close relationship between the VLC seals and those at Macquarie I. An understanding of the relationship between the two populations, as well as of the environmental pressures that led to the demise of the VLC colonies, will provide a better understanding of present-day population genetic structure, the effect of environmental change on seal populations, and possibly the reasons underlying the modern decline at Macquarie Island. This project addresses several key research problems: (1) Why did elephant seals colonize and then abandon the VLC? (2) What does the elephant seal record reveal about Holocene climate change and sea-ice conditions? (3) What were the foraging strategies of the seals and did these strategies change over time as climate varied? (4) How does the genetic structure of the VLC seals relate to extant populations? (5) How did genetic diversity change over time and with colony decline? (6) Using ancient samples to estimate mtDNA mutation rates, what can be learned about VLC population dynamics over time? (7) What was the ecological relationship between elephant seals and Adelie penguins that occupied the same sites, but apparently at different times? The proposed work includes the professional training of young researchers and incorporation of data into graduate and undergraduate courses. Because of extreme isolation of the Antarctic continent since the Early Oligocene, one expects a unique invertebrate benthic fauna with a high degree of endemism. Yet some invertebrate taxa that constitute important ecological components of sedimentary benthic communities include more than 40 percent non-endemic species (e.g., benthic polychaetes). To account for non-endemic species, intermittent genetic exchange must occur between Antarctic and other (e.g. South American) populations. The most likely mechanism for such gene flow, at least for in-faunal and mobile macrobenthos, is dispersal of planktonic larvae across the sub- Antarctic and Antarctic polar fronts. To test for larval dispersal as a mechanism of maintaining genetic continuity across polar fronts, the scientists propose to (1) take plankton samples along transects across Drake passage during both the austral summer and winter seasons while concurrently collecting the appropriate hydrographic data. Such data will help elucidate the hydrographic mechanisms that allow dispersal across Drake Passage. Using a molecular phylogenetic approach, they will (2) compare seemingly identical adult forms from Antarctic and South America continents to identify genetic breaks, historical gene flow, and control for the presence of cryptic species. (3) Similar molecular tools will be used to relate planktonic larvae to their adult forms. Through this procedure, they propose to link the larval forms respectively to their Antarctic or South America origins. The proposed work builds on previous research that provides the basis for this effort to develop a synthetic understanding of historical gene flow and present day dispersal mechanism in South American/Drake Passage/ Antarctic Peninsular region. Furthermore, this work represents one of the first attempts to examine recent gene flow in Antarctic benthic invertebrates. Graduate students and a postdoctoral fellow will be trained during this research proprietary +NSF-ANT04-39906_1 Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change ALL STAC Catalog 2005-09-15 2009-08-31 162, -78, 168, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532069615-AMD_USAPDC.umm_json During previous NSF-sponsored research, the PI's discovered that southern elephant seal colonies once existed along the Victoria Land coast (VLC) of Antarctica, a region where they are no longer observed. Molted seal skin and hair occur along 300 km of coastline, more than 1000 km from any extant colony. The last record of a seal at a former colony site is at ~A.D. 1600. Because abandonment occurred prior to subantarctic sealing, disappearance of the VLC colony probably was due to environmental factors, possibly cooling and encroachment of land-fast, perennial sea ice that made access to haul-out sites difficult. The record of seal inhabitation along the VLC, therefore, has potential as a proxy for climate change. Elephant seals are a predominantly subantarctic species with circumpolar distribution. Genetic studies have revealed significant differentiation among populations, particularly with regard to that at Macquarie I., which is the extant population nearest to the abandoned VLC colony. Not only is the Macquarie population unique genetically, but it is has undergone unexplained decline of 2%/yr over the last 50 years3. In a pilot study, genetic analyses showed a close relationship between the VLC seals and those at Macquarie I. An understanding of the relationship between the two populations, as well as of the environmental pressures that led to the demise of the VLC colonies, will provide a better understanding of present-day population genetic structure, the effect of environmental change on seal populations, and possibly the reasons underlying the modern decline at Macquarie Island. This project addresses several key research problems: (1) Why did elephant seals colonize and then abandon the VLC? (2) What does the elephant seal record reveal about Holocene climate change and sea-ice conditions? (3) What were the foraging strategies of the seals and did these strategies change over time as climate varied? (4) How does the genetic structure of the VLC seals relate to extant populations? (5) How did genetic diversity change over time and with colony decline? (6) Using ancient samples to estimate mtDNA mutation rates, what can be learned about VLC population dynamics over time? (7) What was the ecological relationship between elephant seals and Adelie penguins that occupied the same sites, but apparently at different times? The proposed work includes the professional training of young researchers and incorporation of data into graduate and undergraduate courses. Because of extreme isolation of the Antarctic continent since the Early Oligocene, one expects a unique invertebrate benthic fauna with a high degree of endemism. Yet some invertebrate taxa that constitute important ecological components of sedimentary benthic communities include more than 40 percent non-endemic species (e.g., benthic polychaetes). To account for non-endemic species, intermittent genetic exchange must occur between Antarctic and other (e.g. South American) populations. The most likely mechanism for such gene flow, at least for in-faunal and mobile macrobenthos, is dispersal of planktonic larvae across the sub- Antarctic and Antarctic polar fronts. To test for larval dispersal as a mechanism of maintaining genetic continuity across polar fronts, the scientists propose to (1) take plankton samples along transects across Drake passage during both the austral summer and winter seasons while concurrently collecting the appropriate hydrographic data. Such data will help elucidate the hydrographic mechanisms that allow dispersal across Drake Passage. Using a molecular phylogenetic approach, they will (2) compare seemingly identical adult forms from Antarctic and South America continents to identify genetic breaks, historical gene flow, and control for the presence of cryptic species. (3) Similar molecular tools will be used to relate planktonic larvae to their adult forms. Through this procedure, they propose to link the larval forms respectively to their Antarctic or South America origins. The proposed work builds on previous research that provides the basis for this effort to develop a synthetic understanding of historical gene flow and present day dispersal mechanism in South American/Drake Passage/ Antarctic Peninsular region. Furthermore, this work represents one of the first attempts to examine recent gene flow in Antarctic benthic invertebrates. Graduate students and a postdoctoral fellow will be trained during this research proprietary NSF-ANT04-53680 Application of a New Method for Isotopic Analysis of Diatom Microfossil-bound Nitrogen AMD_USAPDC STAC Catalog 2005-05-01 2009-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069333-AMD_USAPDC.umm_json The Southern Ocean may play a central role in causing ice ages and general global climate change. This work will reveal key characteristics of the glacial ocean, and may explain the cause of glacial/interglacial cycles by measuring the abundances of certain isotopes of nitrogen found in fossil diatoms from Antarctic marine sediments. Diatom-bound N is a potentially important recorder of nutrient utilization. The Southern Ocean's nutrient status, productivity and circulation may be central to setting global atmospheric CO2 contents and other aspects of climate. Previous attempts to make these measurements have yielded ambiguous results. This project includes both technique development and analyses, including measurements on diatoms from both sediment traps and culture experiments. With regard to broader impacts, this grant is focused around the education and academic development of a graduate student, by coupling their research with mentorship of an undergraduate researcher. proprietary -NSF-ANT05-37371 A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica AMD_USAPDC STAC Catalog 2007-10-01 2013-09-30 40, -84, 140, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532069799-AMD_USAPDC.umm_json This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach. proprietary NSF-ANT05-37371 A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica ALL STAC Catalog 2007-10-01 2013-09-30 40, -84, 140, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532069799-AMD_USAPDC.umm_json This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach. proprietary +NSF-ANT05-37371 A Broadband Seismic Experiment to Image the Lithosphere Beneath the Gamburtsev Mountains and Surrounding Areas, East Antarctica AMD_USAPDC STAC Catalog 2007-10-01 2013-09-30 40, -84, 140, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532069799-AMD_USAPDC.umm_json This award supports a seismological study of the Gamburtsev Subglacial Mountains (GSM), a Texas-sized mountain range buried beneath the ice sheets of East Antarctica. The project will perform a passive seismic experiment deploying twenty-three seismic stations over the GSM to characterize the structure of the crust and upper mantle, and determine the processes driving uplift. The outcomes will also offer constraints on the terrestrial heat flux, a key variable in modeling ice sheet formation and behavior. Virtually unexplored, the GSM represents the largest unstudied area of crustal uplift on earth. As well, the region is the starting point for growth of the Antarctic ice sheets. Because of these outstanding questions, the GSM has been identified by the international Antarctic science community as a research focus for the International Polar Year (2007-2009). In addition to this seismic experiment, NSF is also supporting an aerogeophysical survey of the GSM under award number 0632292. Major international partners in the project include Germany, China, Australia, and the United Kingdom. For more information see IPY Project #67 at IPY.org. In terms of broader impacts, this project also supports postdoctoral and graduate student research, and various forms of outreach. proprietary NSF-ANT05-37609_1 An Integrated Geomagnetic and Petrologic Study of the Dufek Complex AMD_USAPDC STAC Catalog 2006-10-01 2011-09-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069732-AMD_USAPDC.umm_json This project studies remnant magnetization in igneous rocks from the Dufek igneous complex, Antarctica. Its primary goal is to understand variations in the Earth's magnetic field during the Mesozoic Dipole Low (MDL), a period when the Earth's magnetic field underwent dramatic weakening and rapid reversals. This work will resolve the MDL's timing and nature, and assess connections between reversal rate, geomagnetic intensity and directional variability, and large-scale geodynamic processes. The project also includes petrologic studies to determine cooling rate effects on magnetic signatures, and understand assembly of the Dufek as an igneous body. Poorly studied, the Dufek is amongst the world's largest intrusions and its formation is connected to the break-up of Gondwana. The broader impacts of this project include graduate and undergraduate education and international collaboration with a German and Chilean IPY project. proprietary NSF-ANT05-38580 Antarctica's Geological History Reflected in Sedimentary Radiogenic Isotopes AMD_USAPDC STAC Catalog 2006-09-15 2010-08-31 60, -70, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069644-AMD_USAPDC.umm_json This project studies sediment from the ocean floor to understand Antarctica's geologic history. Glacially eroded from the Antarctic continent, these sediments may offer insight into the 99% Antarctica covered by ice. The work's central focus is determining crust formation ages and thermal histories for three key areas of East Antarctica--Prydz Bay, eastern Weddell Sea, and Wilkes Land--through a combination of petrography, bulk sediment geochemistry and radiogenic isotopes, as well as isotope chronology of individual mineral grains. One specific objective is characterizing the composition of the Gamburtsev Mountains through studies of Eocene fluvial sediments from Prydz Bay. In addition to furthering our understanding of the hidden terrains of Antarctica, these terrigenous sediments will also serve as a natural laboratory to evaluate the effects of continental weathering on the Hf/Nd isotope systematics of seawater. An important broader impact of the project is providing exciting research projects for graduate and postdoctoral students using state of the art techniques in geochemistry. proprietary NSF-ANT06-36850 Central Scotia Seafloor and the Drake Passage Deep Ocean Current Gateway AMD_USAPDC STAC Catalog 2007-07-15 2009-06-30 -70, -62, -35, -52 https://cmr.earthdata.nasa.gov/search/concepts/C2532069299-AMD_USAPDC.umm_json This project studies the opening of the Drake Passage between South America and Antarctica through a combined marine geophysical survey and geochemical study of dredged ocean floor basalts. Dating the passage's opening is key to understanding the formation of the circum-Antarctic current, which plays a major role in worldwide ocean circulation, and whose formation is connected with growth of the Antarctic ice sheet. Dredge samples will undergo various geochemical studies to determine their age and constrain mantle flow beneath the region. Broader impacts include support for graduate education, as well as undergraduate and K12 teacher involvement in a research cruise. The project also involves international collaboration with the UK and is part of IPY Project #77: Plates&Gates, which aims to reconstruct the geologic history of polar ocean basins and gateways for computer simulations of climate change. See http://www.ipy.org/index.php?/ipy/detail/plates_gates/ for more information. proprietary NSF-ANT06-36899_1 Antarctic Auroral Imaging AMD_USAPDC STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069257-AMD_USAPDC.umm_json Auroral protons are not energized by electric fields directly above the auroral atmosphere and therefore they are a much better diagnostic of processes deep in the magnetosphere. It has been shown from measurements from space by the IMAGE spacecraft that the dayside hydrogen emission is directly related to dayside reconnection processes. A four channel all-sky images had been operating at South Pole during 2004-2007 to observe auroral features in specific wavelengths channels that allowed a quantitative investigation of proton aurora. This was accomplished by measuring the Hydrogen Balmer beta line at 486.1 nm and by monitoring another wavelength band for subtracting non proton produced background emissions. South Pole allows these measurements because of the 24 hour darkness and favorable conditions even on the dayside. To increase the scientific return it was also attempted to measure the Doppler shift of the hydrogen emissions because that provides diagnostics regarding the energy of the protons. Thus the proton camera measured 3 wavelength bands simultaneously in the vicinity of the Balmer beta line to provide the line intensity near zero Doppler shift, at a substantial Doppler shift and a third channel for background. The 4-channel all-sky camera at South Pole was modified in 2008 in order to observe several types of auroras, and to distinguish the cusp reconnection aurora from the normal plasma sheet precipitation. The camera simultaneously operates in four wavelength regions that allow a distinction between auroras that are created by higher energy electrons (greater than 1 keV) and those created by low energy (less than 500 eV) precipitation. The cusp is the location where plasma enters the magnetosphere through the process of magnetic reconnection. This reconnection occurs where the Interplanetary Magnetic Field (IMF) and the terrestrial magnetic field are oriented in opposite directions. The data are represented as keograms (geomagnetic north-south slices through the time series of images) for the four different wavelengths. The top of the keogram points to the magnetic south pole. The time series allows a very quick assessment about the presence of aurora, motion, intensity, and brightness differences in the four simultaneously registered channels. proprietary -NSF-ANT06-36928 A VLF Beacon Transmitter at South Pole ALL STAC Catalog 2007-09-15 2011-08-31 -180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069583-AMD_USAPDC.umm_json This proposal seeks funding to resume operation of the VLF Beacon Transmitter at the South Pole Station used to quantify temporal and spatial variations in the state of the lower ionosphere between the polar cap and subauroral zone, to determine the ionosphere's response to precipitation of highly energetic radiation belt electrons and solar protons, and to monitor the loss of these particles into the atmosphere. Although fluctuations in the relativistic particle population are extensively observed on satellites, little is known about the extent of associated precipitation into the ionosphere. Upon precipitation, these highly energetic particles penetrate to altitudes as low as 30-40 km, producing ionization, X-rays, and possibly affecting chemical reactions involving ozone production. It is proposed to continue recording the VLF beacon's signal at various Antarctic coastal stations (Palmer, Halley, etc). The broader impact of the proposed program includes the synergistic use of the South Pole VLF beacon with ongoing satellite-based measurements of trapped and precipitating high-energy electrons both at low and high altitudes and with other Antarctic Upper Atmospheric research efforts, such as the Automatic Geophysical Observatory programs and routine upper atmospheric observations at manned bases. The proposed project also promotes international collaboration via multi-points recording of the South Pole VLF beacon signal while providing the basis of a graduate or doctoral student thesis. proprietary NSF-ANT06-36928 A VLF Beacon Transmitter at South Pole AMD_USAPDC STAC Catalog 2007-09-15 2011-08-31 -180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069583-AMD_USAPDC.umm_json This proposal seeks funding to resume operation of the VLF Beacon Transmitter at the South Pole Station used to quantify temporal and spatial variations in the state of the lower ionosphere between the polar cap and subauroral zone, to determine the ionosphere's response to precipitation of highly energetic radiation belt electrons and solar protons, and to monitor the loss of these particles into the atmosphere. Although fluctuations in the relativistic particle population are extensively observed on satellites, little is known about the extent of associated precipitation into the ionosphere. Upon precipitation, these highly energetic particles penetrate to altitudes as low as 30-40 km, producing ionization, X-rays, and possibly affecting chemical reactions involving ozone production. It is proposed to continue recording the VLF beacon's signal at various Antarctic coastal stations (Palmer, Halley, etc). The broader impact of the proposed program includes the synergistic use of the South Pole VLF beacon with ongoing satellite-based measurements of trapped and precipitating high-energy electrons both at low and high altitudes and with other Antarctic Upper Atmospheric research efforts, such as the Automatic Geophysical Observatory programs and routine upper atmospheric observations at manned bases. The proposed project also promotes international collaboration via multi-points recording of the South Pole VLF beacon signal while providing the basis of a graduate or doctoral student thesis. proprietary +NSF-ANT06-36928 A VLF Beacon Transmitter at South Pole ALL STAC Catalog 2007-09-15 2011-08-31 -180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069583-AMD_USAPDC.umm_json This proposal seeks funding to resume operation of the VLF Beacon Transmitter at the South Pole Station used to quantify temporal and spatial variations in the state of the lower ionosphere between the polar cap and subauroral zone, to determine the ionosphere's response to precipitation of highly energetic radiation belt electrons and solar protons, and to monitor the loss of these particles into the atmosphere. Although fluctuations in the relativistic particle population are extensively observed on satellites, little is known about the extent of associated precipitation into the ionosphere. Upon precipitation, these highly energetic particles penetrate to altitudes as low as 30-40 km, producing ionization, X-rays, and possibly affecting chemical reactions involving ozone production. It is proposed to continue recording the VLF beacon's signal at various Antarctic coastal stations (Palmer, Halley, etc). The broader impact of the proposed program includes the synergistic use of the South Pole VLF beacon with ongoing satellite-based measurements of trapped and precipitating high-energy electrons both at low and high altitudes and with other Antarctic Upper Atmospheric research efforts, such as the Automatic Geophysical Observatory programs and routine upper atmospheric observations at manned bases. The proposed project also promotes international collaboration via multi-points recording of the South Pole VLF beacon signal while providing the basis of a graduate or doctoral student thesis. proprietary NSF-ANT06-49609_1 Aging in Weddell Seals: Proximate Mechanisms of Age-Related Changes in Adaptations to Breath-Hold Hunting in an Extreme Environment ALL STAC Catalog 2006-08-01 2010-08-31 165.975, -77.849, 166.856, -77.54 https://cmr.earthdata.nasa.gov/search/concepts/C2532069573-AMD_USAPDC.umm_json The primary objectives of this research are to investigate the proximate effects of aging on diving capability in the Weddell Seal and to describe mechanisms by which aging may influence foraging ecology, through physiology and behavior. This model pinniped species has been the focus of three decades of research in McMurdo Sound, Antarctica. Compared to the knowledge of pinniped diving physiology and ecology during early development and young adulthood, little is known about individuals nearing the upper limit of their normal reproductive age range. Evolutionary aging theories predict that elderly diving seals should exhibit senescence. This should be exacerbated by surges in the generation of oxygen free radicals via hypoxia-reoxygenation during breath-hold diving and hunting, which are implicated in age-related damage to cellular mitochondria. Surprisingly, limited observations of non-threatened pinniped populations indicate that senescence does not occur to a level where reproductive output is affected. The ability of pinnipeds to avoid apparent senescence raises two major questions: what specific physiological and morphological changes occur with advancing age in pinnipeds; and what subtle adjustments are made by these animals to cope with such changes? This investigation will focus on specific, functional physiological and behavioral changes relating to dive capability with advancing age. Data will be compared between Weddell seals in the peak, and near the end, of their reproductive age range. The investigators will quantify age-related changes in general health and body condition, combined with fine scale assessments of external and internal ability to do work in the form of diving. Specifically, patterns of muscle morphology, oxidant status and oxygen storage with age will be examined. The effects of age on skeletal muscular function and exercise performance will also be examined. The investigators hypothesize that senescence does occur in Weddell seals at the level of small-scale, proximate physiological effects and performance, but that behavioral plasticity allows for a given degree of compensation. Broader impacts include the training of students and outreach activities including interviews and articles written for the popular media. This study should also establish diving seals as a novel model for the study of cardiovascular and muscular physiology of aging and develop a foundation for similar research on other species. Advancement of the understanding of aging by medical science has been impressive in recent years but basic mammalian aging is an area of study the still requires considerable effort. The development of new models for the study of aging has tremendous potential benefits to society at large. proprietary NSF-ANT06-49609_1 Aging in Weddell Seals: Proximate Mechanisms of Age-Related Changes in Adaptations to Breath-Hold Hunting in an Extreme Environment AMD_USAPDC STAC Catalog 2006-08-01 2010-08-31 165.975, -77.849, 166.856, -77.54 https://cmr.earthdata.nasa.gov/search/concepts/C2532069573-AMD_USAPDC.umm_json The primary objectives of this research are to investigate the proximate effects of aging on diving capability in the Weddell Seal and to describe mechanisms by which aging may influence foraging ecology, through physiology and behavior. This model pinniped species has been the focus of three decades of research in McMurdo Sound, Antarctica. Compared to the knowledge of pinniped diving physiology and ecology during early development and young adulthood, little is known about individuals nearing the upper limit of their normal reproductive age range. Evolutionary aging theories predict that elderly diving seals should exhibit senescence. This should be exacerbated by surges in the generation of oxygen free radicals via hypoxia-reoxygenation during breath-hold diving and hunting, which are implicated in age-related damage to cellular mitochondria. Surprisingly, limited observations of non-threatened pinniped populations indicate that senescence does not occur to a level where reproductive output is affected. The ability of pinnipeds to avoid apparent senescence raises two major questions: what specific physiological and morphological changes occur with advancing age in pinnipeds; and what subtle adjustments are made by these animals to cope with such changes? This investigation will focus on specific, functional physiological and behavioral changes relating to dive capability with advancing age. Data will be compared between Weddell seals in the peak, and near the end, of their reproductive age range. The investigators will quantify age-related changes in general health and body condition, combined with fine scale assessments of external and internal ability to do work in the form of diving. Specifically, patterns of muscle morphology, oxidant status and oxygen storage with age will be examined. The effects of age on skeletal muscular function and exercise performance will also be examined. The investigators hypothesize that senescence does occur in Weddell seals at the level of small-scale, proximate physiological effects and performance, but that behavioral plasticity allows for a given degree of compensation. Broader impacts include the training of students and outreach activities including interviews and articles written for the popular media. This study should also establish diving seals as a novel model for the study of cardiovascular and muscular physiology of aging and develop a foundation for similar research on other species. Advancement of the understanding of aging by medical science has been impressive in recent years but basic mammalian aging is an area of study the still requires considerable effort. The development of new models for the study of aging has tremendous potential benefits to society at large. proprietary NSF-ANT07-32625_1 Collaborative Research in IPY: Abrupt Environmental Change in the Larsen Ice Shelf System, a Multidisciplinary Approach - Marine and Quaternary Geosciences AMD_USAPDC STAC Catalog 2007-10-01 2013-09-30 -65.4, -66.1, -57.8, -57 https://cmr.earthdata.nasa.gov/search/concepts/C2532069808-AMD_USAPDC.umm_json This award supports a research cruise to perform geologic studies in the area under and surrounding the former Larsen B ice shelf, on the Antarctic Peninsula. The ice shelf's disintegration in 2002 coupled with the unique marine geology of the area make it possible to understand the conditions leading to ice shelf collapse. Bellwethers of climate change that reflect both oceanographic and atmospheric conditions, ice shelves also hold back glacial flow in key areas of the polar regions. Their collapse results in glacial surging and could cause rapid rise in global sea levels. This project characterizes the Larsen ice shelf's history and conditions leading to its collapse by determining: 1) the size of the Larsen B during warmer climates and higher sea levels back to the Eemian interglacial, 125,000 years ago; 2) the configuration of the Antarctic Peninsula ice sheet during the LGM and its subsequent retreat; 3) the causes of the Larsen B's stability through the Holocene, during which other shelves have come and gone; 4) the controls on the dynamics of ice shelf margins, especially the roles of surface melting and oceanic processes, and 5) the changes in sediment flux, both biogenic and lithogenic, after large ice shelf breakup. The broader impacts include graduate and undergraduate education through research projects and workshops; outreach to the general public through a television documentary and websites, and international collaboration with scientists from Belgium, Spain, Argentina, Canada, Germany and the UK. The work also has important societal relevance. Improving our understanding of how ice shelves behave in a warming world will improve models of sea level rise. The project is supported under NSF's International Polar Year (IPY) research emphasis area on 'Understanding Environmental Change in Polar Regions'. proprietary @@ -12512,20 +12512,20 @@ NSF-ANT09-44727 ASPIRE: Amundsen Sea Polynya International Research Expedition A NSF-ANT10-43145_1 Bromide in Snow in the Sea Ice Zone AMD_USAPDC STAC Catalog 2011-08-15 2015-07-31 164.1005, -77.8645, 166.7398, -77.1188 https://cmr.earthdata.nasa.gov/search/concepts/C2532070132-AMD_USAPDC.umm_json A range of chemical and microphysical pathways in polar latitudes, including spring time (tropospheric) ozone depletion, oxidative pathways for mercury, and cloud condensation nuclei (CCN) production leading to changes in the cloud cover and attendant surface energy budgets, have been invoked as being dependent upon the emission of halogen gases formed in sea-ice. The prospects for climate warming induced reductions in sea ice extent causing alteration of these incompletely known surface-atmospheric feedbacks and interactions requires confirmation of mechanistic details in both laboratory studies and field campaigns. One such mechanistic question is how bromine (BrO and Br) enriched snow migrates or is formed through processes in sea-ice, prior to its subsequent mobilization as an aerosol fraction into the atmosphere by strong winds. Once aloft, it may react with ozone and other atmospheric species. Dartmouth researchers will collect snow from the surface of sea ice, from freely blowing snow and in sea-ice cores from Cape Byrd, Ross Sea. A range of spectroscopic, microanalytic and and microstructural approaches will be subsequently used to determine the Br distribution gradients through sea-ice, in order to shed light on how sea-ice first forms and then releases bromine species into the polar atmospheric boundary layer. proprietary NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary -NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary +NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins ALL STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary -NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices AMD_USAPDC STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices ALL STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary +NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices AMD_USAPDC STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary NSF-ANT10-44978 BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole AMD_USAPDC STAC Catalog 2008-05-15 2017-09-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532070162-AMD_USAPDC.umm_json BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole. The proposed work is a four-year program of research activities directed toward upgrading the BICEP (Background Imaging of Cosmic Extragalactic Polarization) telescope operating at South Pole since early 2006 to reach far =stretching goals of detection of the Cosmic Gravitational-wave Background (CGB). This telescope is a first Cosmic Microwave Background (CMB) B-mode polarimeter, specifically designed to search for CGB signatures while mapping ~2% of the southern sky that is free of the Milky Way foreground galactic radiation at 100 GH and 150 GHz. The BICEP1 telescope will reach its designed sensitivity by the end of 2008. A coordinated series of upgrades to BICEP1 will provide the increased sensitivity and more exacting control of instrumental effects and potential confusion from galactic foregrounds necessary to search for the B-mode signal more deeply through space. A powerful new 150 GHz receiver, BICEP2, will replace the current detector at the beginning of 2009, increasing the mapping speed almost ten-fold. In 2010, the first of a series of compact, mechanically-cooled receivers (called SPUD - Small Polarimeter Upgrade for DASI) will be deployed on the existing DASI mount and tower, providing similar mapping speed at 100 GHz in parallel with BICEP2. The latter instrument will reach (and exceed with the addition of a SPUD polarimeter) the target sensitivity r = 0.15 set forth by the Interagency (NSF/NASA/DoE) Task Force on CMB Research for a future space mission dedicated to the detection and characterization of primordial gravitational waves. This Task Force has identified detection of the Inflation's gravitational waves as the number one priority for the modern cosmology. More broadly, as the cosmology captures a lot of the public imagination, it is a remarkably effective vehicle for stimulating interest in basic science. The CGB detection would be to Inflation what the discovery of the CMB radiation was to the Big Bang. The project will contribute to the training of the next generation of cosmologists by integrating graduate and undergraduate education with the technology and instrumentation development, astronomical observations and scientific analysis. Sharing of the forefront research results with public extends the new knowledge beyond the universities. This project will be undertaken in collaboration between the California Institute of Technology and the University of Chicago. proprietary NSF-ANT10-48343_1 CAREER: Deciphering Antarctic Climate Variability during the Temperate/Polar Transition and Improving Climate Change Literacy in Louisiana through a Companion Outreach Program AMD_USAPDC STAC Catalog 2011-03-01 2016-02-29 57.217, -70.373, 153.359, -63.664 https://cmr.earthdata.nasa.gov/search/concepts/C2532069731-AMD_USAPDC.umm_json Intellectual Merit: The PI proposes a high-resolution paleoenvironmental study of pollen, spore, fresh-water algae, and dinoflagellate cyst assemblages to investigate the palynological record of sudden warming events in the Antarctic as recorded by the ANDRILL SMS drill core and terrestrial sections. These data will be used to derive causal mechanisms for these rapid climate events. Terrestrial samples will be obtained at various altitudes in the Dry Valleys region. The pollen and spores will provide data on atmospheric conditions, while the algae will provide data on sea-surface conditions. These data will help identify the triggers for sudden climatic shifts. If they are caused by changes in oceanic currents, a signal will be visible in the dinocyst assemblages first as currents influence their distribution. Conversely, if these shifts are triggered by atmospheric factors, then the shifts will first affect plants and be visible in the pollen record. Broader impacts: The PI proposes a suite of activities to bring field-based climate change research to a broader audience. The PI will advise a diverse group of students and educators. The palynological data collected as part of this research will be utilized, in part, to develop new lectures on Antarctic palynology and these new lectures will be made available via a collaboration with the LSU HHMI program. In addition, the PI will direct three Louisiana middle-school teachers as they pursue a Masters of Natural Science for science educators. These teachers will help the PI develop a professional development program for science teachers. Community-based activities will be organized to raise science awareness and alert students and the public of opportunities in science. proprietary NSF-ANT10-63592_1 Application for an Early-concept Grant for Exploratory Reasearch (EAGER) to develop a Pathway/Genome Database (PGDB) for the Southern Ocean Haptophyte Phaeocystis Antarctica. AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532069964-AMD_USAPDC.umm_json Phaeocystis antarctica is capable of forming blooms that are denser and more extensive than any other member of the Southern Ocean phytoplankton community. The factors that enable P Antarctica to dominate its competitors are not clear but are likely related to its colonial lifestyle. The goal of the project is to map all the reactions in metabolic pathways that are key to defining the ecological niche of Phaeocystis antarctica by developing a Pathway/Genome Database (PGDB) using Pathway Tools software. The investigators will assign proteins and enzymes to key pathways in P. Antarctica, continually improve and edit the database as the full Phaeocystis genome comes online, and host the database on the BioCyc webpage. The end product will be the first database for a eukaryotic phytoplankton genome where researchers can query extant metabolic pathways and place new proteins and enzymes of interest within metabolic networks. The risk is that a substantial percentage of catalytic enzymes may belong to pathways that are poorly characterized. The science impact is to link genomes to metabolic potential in the context of Phaeocystis life history but also in comparison to other organisms across the tree of life. The education and outreach includes work with a high school teacher and intern and curriculum development. proprietary -NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems ALL STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary +NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary NSF-ANT11-42102 An Integrated Ecological Investigation of McMurdo Dry Valley's Active Soil Microbial Communities AMD_USAPDC STAC Catalog 2012-07-01 2015-06-30 161, -77.5, 164, -77 https://cmr.earthdata.nasa.gov/search/concepts/C2532070421-AMD_USAPDC.umm_json The McMurdo Dry Valleys in Antarctica are among the coldest, driest habitats on the planet. Previous research has documented the presence of surprisingly diverse microbial communities in the soils of the Dry Valleys despite these extreme conditions. However, the degree to which these organisms are active is unknown; it is possible that much of this diversity reflects microbes that have blown into this environment that are subsequently preserved in these cold, dry conditions. This research will use modern molecular techniques to answer a fundamental question regarding these communities: which organisms are active and how do they live in such extreme conditions? The research will include manipulations to explore how changes in water, salt and carbon affect the microbial community, to address the role that these organisms play in nutrient cycling in this environment. The results of this work will provide a broader understanding of how life adapts to such extreme conditions as well as the role of dormancy in the life history of microorganisms. Results will be widely disseminated through publications as well as through presentations at national and international meetings; raw data will be made available through a high-profile web-based portal. The research will support two graduate students, two undergraduate research assistants and a postdoctoral fellow. The results will be incorporated into a webinar targeted to secondary and post-secondary educators and a complimentary hands-on class activity kit will be developed and made available to various teacher and outreach organizations. proprietary -NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network ALL STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network AMD_USAPDC STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary +NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network ALL STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization ALL STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization AMD_USAPDC STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary NSF-ANT90-24544 Atmospheric Boundary Layer Measurements on the Weddell Sea Drifting Station AMD_USAPDC STAC Catalog 1992-02-21 1992-06-05 -53.8, -71.4, -43.2, -61.2 https://cmr.earthdata.nasa.gov/search/concepts/C2534797194-AMD_USAPDC.umm_json Location: Ice camp on perennial sea ice in the southwestern corner of the Weddell Sea, Antarctic The first direct radiative and turbulent surface flux measurements ever made over floating Antarctic sea ice. The data are from Ice Station Weddell as it drifted in the western Weddell Sea from February to late May 1992. Data Types: Hourly measurements of the turbulent surface fluxes of momentum and sensible and latent heat by eddy covariance at a height of 4.65 m above snow-covered sea ice. Instruments were a 3-axis sonic anemometer/thermometer and a Lyman-alpha hygrometer. Hourly, surface-level measurements of the four radiation components: in-coming and out-going longwave and shortwave radiation. Instruments were hemispherical pyranometers and pyrgeometers. Hourly mean values of standard meteorological variables: air temperature, dew point temperature, wind speed and direction, barometric pressure, surface temperature. Instruments were a propeller-vane for wind speed and direction and cooled-mirror dew-point hygrometers and platinum resistance thermometers for dew-points and temperatures. Surface temperature came from a Barnes PRT-5 infrared thermometer. Flux Data The entire data kit is bundled as a zip file named ISW_Flux_Data.zip The main data file is comma delimited. The README file is ASCII. The associated reprints of publications are in pdf. Radiosounding data: On Ice Station Weddell, typically twice a day from 21 February through 4 June 1992 made with both tethered (i.e., only boundary-layer profiles) and (more rarely) free-flying sondes that did not measure wind speed. (168 soundings). ISW Radiosoundings The entire data kit is bundled as a zip file named ISW_Radiosounding.zip. The README file is in ASCII. Two summary files that include the list of sounding and the declinations are in ASCII. The 168 individual sounding files are in ASCII. Two supporting publications that describe the data and some analyses are in pdf. Radiosounding data collected from the Russian ship Akademic Fedorov from 26 May through 5 June 1992 at 6-hourly intervals as it approached Ice Station Weddell from the north. These soundings include wind vector, temperature, humidity, and pressure. (40 soundings) Akademic Federov Radiosoundings The entire data kit is bundled as a zip file named Akad_Federov_Radiosounding.zip. The README file is in ASCII. A summary file that lists the soundings is in ASCII. The 40 individual sounding files are in ASCII. Two supporting publications that describe the data and some analyses are in pdf. Documentation: Andreas, E. L, and K. J. Claffey, 1995: Air-ice drag coefficients in the western Weddell Sea: 1. Values deduced from profile measurements. Journal of Geophysical Research, 100, 4821–4831. Andreas, E. L, K. J. Claffey, and A. P. Makshtas, 2000: Low-level atmospheric jets and inversions over the western Weddell Sea. Boundary-Layer Meteorology, 97, 459–486. Andreas, E. L, R. E. Jordan, and A. P. Makshtas, 2004: Simulations of snow, ice, and near-surface atmospheric processes on Ice Station Weddell. Journal of Hydrometeorology, 5, 611–624. Andreas, E. L, R. E. Jordan, and A. P. Makshtas, 2005: Parameterizing turbulent exchange over sea ice: The Ice Station Weddell results. Boundary-Layer Meteorology, 114, 439–460. Andreas, E. L, P. O. G. Persson, R. E. Jordan, T. W. Horst, P. S. Guest, A. A. Grachev, and C. W. Fairall, 2010: Parameterizing turbulent exchange over sea ice in winter. Journal of Hydrometeorology, 11, 87–104. Claffey, K. J., E. L Andreas, and A. P. Makshtas, 1994: Upper-air data collected on Ice Station Weddell. Special Report 94-25, U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, 62 pp. ISW Group, 1993: Weddell Sea exploration from ice station. Eos, Transactions, American Geophysical Union, 74, 121–126. Makshtas, A. P., E. L Andreas, P. N. Svyaschennikov, and V. F. Timachev, 1999: Accounting for clouds in sea ice models. Atmospheric Research, 52, 77–113. proprietary @@ -12590,8 +12590,8 @@ NSIDC-0202_1 Atmospheric CO2 Trapped in the Ice Core from Siple Dome, Antarctica NSIDC-0209_1 Baltic Sea Experiment (BALTEX) Ground-Based Radar Polar Volume Data, Version 1 NSIDCV0 STAC Catalog 2002-09-01 2003-05-31 18.39, 57.24, 18.39, 57.24 https://cmr.earthdata.nasa.gov/search/concepts/C1386204148-NSIDCV0.umm_json This data set includes non-Doppler polar volume reflectivity data from the Baltic Sea Experiment (BALTEX). Data were collected on Sweden's Gotland Island, using an Ericsson radar mounted at 56 m above sea level. proprietary NSIDC-0210_1 Double Rain Gauge Network, Iowa, Version 1 NSIDCV0 STAC Catalog 2002-06-18 2003-11-13 -91.75, 41.5, -91.5, 41.75 https://cmr.earthdata.nasa.gov/search/concepts/C1386204149-NSIDCV0.umm_json This data set includes rainfall data from 25 sites in Iowa, centered on the Iowa City Municipal Airport. proprietary NSIDC-0211_1 CLPX-Model: Rapid Update Cycle 40km (RUC-40) Model Output Reduced Data, Version 1 NSIDCV0 STAC Catalog 2002-10-01 2003-06-30 -108.615, 38.394, -103.971, 42.568 https://cmr.earthdata.nasa.gov/search/concepts/C1386250242-NSIDCV0.umm_json The Rapid Update Cycle, version 2 at 40km (RUC-2, known to the Cold Land Processes community as RUC40) model is a Mesoscale Analysis and Prediction System (MAPS) data set that uses the Model Output Reduced Data Set (MORDS) version. This data set has been subsetted for use in the Cold Land Processes Field Experiment (CLPX). proprietary -NSIDC-0212_1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 NSIDCV0 STAC Catalog 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.umm_json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. proprietary NSIDC-0212_1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 ALL STAC Catalog 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.umm_json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. proprietary +NSIDC-0212_1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 NSIDCV0 STAC Catalog 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.umm_json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. proprietary NSIDC-0218_1 Greenland Ice Sheet Melt Characteristics Derived from Passive Microwave Data, Version 1 NSIDCV0 STAC Catalog 1979-04-02 2007-12-31 -73, 60, -10, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1386250243-NSIDCV0.umm_json The Greenland ice sheet melt extent data, acquired as part of the NASA Program for Arctic Regional Climate Assessment (PARCA), is a daily (or every other day, prior to August 1987) estimate of the spatial extent of wet snow on the Greenland ice sheet since 1979. It is derived from passive microwave satellite brightness temperature characteristics using the Cross-Polarized Gradient Ratio (XPGR) of Abdalati and Steffen (1997). It is physically based on the changes in microwave emission characteristics observable in data from the Scanning Multi-channel Microwave Radiometer (SMMR) and the Special Sensor Microwave/Imager (SSM/I) instruments when surface snow melts. It is not a direct measure of the snow wetness but rather is a binary indicator of the state of melt of each SMMR and SSM/I pixel on the ice sheet for each day of observation. It is, however, a useful proxy for the amount of melt that occurs on the Greenland ice sheet. The data are provided in a variety of formats including raw data in ASCII format, gridded daily data in binary format, and annual and complete time series climatologies in gridded binary and GeoTIFF format. All data are in a 60 x 109 pixel subset of the standard Northern Hemisphere polar stereographic grid with a 25 km resolution and are available via FTP. proprietary NSIDC-0223_1 Elevation Change of the Southern Greenland Ice Sheet from 1978-88, Version 1 NSIDCV0 STAC Catalog 1978-01-01 1988-12-31 -52, 61, -30, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1386204165-NSIDCV0.umm_json Southern Greenland ice sheet elevation change estimates are derived from SEASAT and GEOSAT radar altimetry data from 1978 to 1988. Data are confined to 61-72 deg N, 30-50 deg W, above 1700 m elevation. The addition of GEOSAT Geodetic Mission (GM) data results in twice as many crossover points and 50% greater coverage than previous studies. Coverage above 2000 m elevation is improved to 90%, and about 75% of the area between 1700 m and 2000 m is now covered. Data are in ASCII text format, available via FTP, and consist of elevation change rate (dH/dt, cm/year) and corresponding error estimates in 50 km grid cells. proprietary NSIDC-0240_1 Antarctic Aerogeophysics Data AMD_USAPDC STAC Catalog 1994-01-01 2004-12-31 -90, -75, 90, -68.73 https://cmr.earthdata.nasa.gov/search/concepts/C2532073961-AMD_USAPDC.umm_json The data that the Support Office for Aerogeophysical Research (SOAR) provides include various aerogeophysical measurements taken in the West Antarctic Ice Shelf (WAIS) from 1994 to 2001. The instruments used in experiments include ice-penetrating radar, laser altimetry and magnetics, and an integrated aerogeophysical platform that includes airborne gravity with carrier-phase GPS to support kinematic differential positioning. SOAR is a part of the University of Texas Institute for Geophysics (UTIG) and provides several types of data associated with various campaigns over the years. This material is based on work supported by the National Science Foundation under Grants: OPP-9120464, 9319369, 9319379, and 9911617. proprietary @@ -12614,10 +12614,10 @@ NSIDC-0314_1 Atmospheric CO2 and Climate: Byrd Ice Core, Antarctica AMD_USAPDC S NSIDC-0315_1 Atmospheric CO2 and Climate: Taylor Dome Ice Core, Antarctica AMD_USAPDC STAC Catalog 1970-01-01 158, -77.666667, 158, -77.666667 https://cmr.earthdata.nasa.gov/search/concepts/C2532070838-AMD_USAPDC.umm_json Using new and existing ice core CO2 data from 65 - 30 ka BP a new chronology for Taylor Dome ice core CO2 is established and synchronized with Greenland ice core records to study how high latitude climate change and the carbon cycle were linked during the last glacial period. The new data and chronology should provide a better target for models attempting to explain CO2 variability and abrupt climate change. proprietary NSIDC-0318_1 Antarctic Mean Annual Temperature Map AMD_USAPDC STAC Catalog 1957-01-01 2003-12-31 -180, -90, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532070844-AMD_USAPDC.umm_json The Mean Annual Temperature map was calculated by creating a contour map using compiled 10 meter firn temperature data from NSIDC and other mean annual temperature data from both cores and stations. The 10 meter data contains temperature measurements dating back to 1957 and the International Geophysical Year, including measurements from several major recent surveys. Data cover the entire continental ice sheet and several ice shelves, but coverage density is generally low. Data are stored in Microsoft Excel and Tagged Image File Format (TIFF), and are available sporadically from 1957 to 2003 via FTP. proprietary NSIDC-0321_1 Global EASE-Grid 8-day Blended SSM/I and MODIS Snow Cover, Version 1 NSIDCV0 STAC Catalog 2000-03-05 2008-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1386250333-NSIDCV0.umm_json This data set comprises global, 8-day Snow-Covered Area (SCA) and Snow Water Equivalent (SWE) data from 2000 through 2008. Global SWE data are derived from the Special Sensor Microwave Imager (SSM/I) and are enhanced with MODIS/Terra Snow Cover 8-Day Level 3 Global 0.05 degree Climate Modeling Grid (CMG) data. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). These data are suitable for continental-scale time-series studies of snow cover and snow water equivalent. proprietary -NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica AMD_USAPDC STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica ALL STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary -NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica AMD_USAPDC STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary +NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica AMD_USAPDC STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica ALL STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary +NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica AMD_USAPDC STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary NSIDC-0336_1 Antarctic Subglacial Lake Classification Inventory AMD_USAPDC STAC Catalog 1998-12-01 2001-02-28 -160, -90, 15, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2532070882-AMD_USAPDC.umm_json This data set is an Antarctic radar-based subglacial lake classification collection, which focuses on the radar reflection properties of each given lake. The Subglacial lakes are separated into four categories specified by radar reflection properties. Additional information includes: latitude, longitude, length (in kilometers), hydro-potential (in meters), bed elevation (in meters above WGS84), and ice thickness (in meters). Source data used to compile this data set were collected between 1998 and 2001. Data are available via FTP as a Microsoft Excel Spreadsheet (XLS), and Tagged Image File Format (TIF). proprietary NSIDC-0393_1 Arctic Sea Ice Freeboard and Thickness, Version 1 NSIDCV0 STAC Catalog 2003-02-20 2008-10-19 -180, 65, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1386250451-NSIDCV0.umm_json This data set provides measurements of sea ice freeboard and sea ice thickness for the Arctic region. The data were derived from measurements made by from the Ice, Cloud, and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) instrument, the Special Sensor Microwave/Imager (SSM/I), and climatologies of snow and drift of ice. proprietary NSIDC-0394_1 Atmospheric Mixing Ratios of Hydroperoxides above the West Antarctic Ice Sheet AMD_USAPDC STAC Catalog 2000-11-20 2003-01-15 -124, -90, -84, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532071044-AMD_USAPDC.umm_json This data set contains atmospheric mixing ratios of hydrogen peroxide and methylhydroperoxide at 21 sites on the West Antarctic Ice Sheet (WAIS) were obtained from 2000 to 2003 during the US International Trans-Antarctic Scientific Expedition (US ITASE) deployments. Sample location from the WAIS region (76-90�S / 84-124�W) were approximately 100-300 km apart and correspond to US ITASE ice core sites. At each site, ambient air from 1 m above the snow surface was sampled between two to five days. Atmospheric hydroperoxides (ROOH) were continuously scrubbed from the sample air with a glass coil scrubber and subsequently quantified using a fluorescence detection method. Data are available via FTP as ASCII text files (.txt). proprietary @@ -12644,12 +12644,12 @@ NSIDC-0478_2 MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data V002 NSID NSIDC-0481_4 MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR V004 NSIDC_ECS STAC Catalog 2008-06-12 2023-09-20 -70, 60, -20, 82 https://cmr.earthdata.nasa.gov/search/concepts/C2076118670-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX). The measurements in this data set are provided in addition to the ice sheet-wide data from the related data set, MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data. See Greenland Ice Mapping Project (GrIMP) for more related data." proprietary NSIDC-0484_2 MEaSUREs InSAR-Based Antarctica Ice Velocity Map V002 NSIDC_ECS STAC Catalog 1996-01-01 2016-12-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1414573008-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides the first comprehensive, high-resolution, digital mosaics of ice motion in Antarctica assembled from multiple satellite interferometric, synthetic-aperture radar systems. Data were largely acquired during the International Polar Years 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 were used as needed to maximize coverage. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary NSIDC-0498_2 MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry V002 NSIDC_ECS STAC Catalog 1992-02-07 2014-12-17 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1573480652-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides 22 years of comprehensive high-resolution mapping of grounding lines in Antarctica from 1992 to 2014. The data were derived using differential satellite synthetic aperture radar interferometry (DInSAR) measurements from the following platforms: Earth Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2), RADARSAT-1, RADARSAT-2, the Advanced Land Observing System Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), Cosmo Skymed, and Copernicus Sentinel-1. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary -NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland AMD_USAPDC STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland ALL STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary +NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland AMD_USAPDC STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary NSIDC-0515_1 Annual Layers at Siple Dome, Antarctica, from Borehole Optical Stratigraphy AMD_USAPDC STAC Catalog 2000-12-15 2001-11-15 -148.82, -81.66, -148.82, -81.66 https://cmr.earthdata.nasa.gov/search/concepts/C2532070824-AMD_USAPDC.umm_json Researchers gathered data on annual snow layers at Siple Dome, Antarctica, using borehole optical stratigraphy. This data set contains annual layer depths and firn optical brightness. The brightness log is a record of reflectivity of the firn, and peaks in brightness are interpreted to be fine-grained high-density winter snow, as part of the wind slab depth-hoar couplet. Data are available via FTP in ASCII text (.txt) format proprietary NSIDC-0516_1 Antarctic Peninsula 100 m Digital Elevation Model Derived from ASTER GDEM AMD_USAPDC STAC Catalog 2000-01-01 2009-12-31 -70, -70, -55, -63 https://cmr.earthdata.nasa.gov/search/concepts/C2532070816-AMD_USAPDC.umm_json This data set provides a 100 meter resolution surface topography Digital Elevation Model (DEM) of the Antarctic Peninsula. The DEM is based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) data. proprietary -NSIDC-0517_1 AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica AMD_USAPDC STAC Catalog 2004-12-10 2005-01-29 -125, -83, -90, -73 https://cmr.earthdata.nasa.gov/search/concepts/C2532070806-AMD_USAPDC.umm_json This data set contains line-based radar-derived ice thickness and bed elevation data, collected as part of the Airborne Geophysical Survey of the Amundsen Embayment (AGASEA) expedition, which took place over Thwaites Glacier in West Antarctica from 2004 to 2005. The data set includes ice thickness, ice sheet bed elevation, and ice sheet surface elevation, derived from ice-penetrating radar and aircraft GPS positions. The data are spaced on a 15 km by 15 km grid over the entire catchment of the glacier, and sampled at approximately 15 meters along track. Most of the radar data used for this dataset has been processed using a 1-D focusing algorithm, to reduce the along track resolution to tens of meters, to improve boundary conditions for ice sheet models. Data are available via FTP in space-delimited ASCII format. proprietary NSIDC-0517_1 AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica ALL STAC Catalog 2004-12-10 2005-01-29 -125, -83, -90, -73 https://cmr.earthdata.nasa.gov/search/concepts/C2532070806-AMD_USAPDC.umm_json This data set contains line-based radar-derived ice thickness and bed elevation data, collected as part of the Airborne Geophysical Survey of the Amundsen Embayment (AGASEA) expedition, which took place over Thwaites Glacier in West Antarctica from 2004 to 2005. The data set includes ice thickness, ice sheet bed elevation, and ice sheet surface elevation, derived from ice-penetrating radar and aircraft GPS positions. The data are spaced on a 15 km by 15 km grid over the entire catchment of the glacier, and sampled at approximately 15 meters along track. Most of the radar data used for this dataset has been processed using a 1-D focusing algorithm, to reduce the along track resolution to tens of meters, to improve boundary conditions for ice sheet models. Data are available via FTP in space-delimited ASCII format. proprietary +NSIDC-0517_1 AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica AMD_USAPDC STAC Catalog 2004-12-10 2005-01-29 -125, -83, -90, -73 https://cmr.earthdata.nasa.gov/search/concepts/C2532070806-AMD_USAPDC.umm_json This data set contains line-based radar-derived ice thickness and bed elevation data, collected as part of the Airborne Geophysical Survey of the Amundsen Embayment (AGASEA) expedition, which took place over Thwaites Glacier in West Antarctica from 2004 to 2005. The data set includes ice thickness, ice sheet bed elevation, and ice sheet surface elevation, derived from ice-penetrating radar and aircraft GPS positions. The data are spaced on a 15 km by 15 km grid over the entire catchment of the glacier, and sampled at approximately 15 meters along track. Most of the radar data used for this dataset has been processed using a 1-D focusing algorithm, to reduce the along track resolution to tens of meters, to improve boundary conditions for ice sheet models. Data are available via FTP in space-delimited ASCII format. proprietary NSIDC-0522_1 Coastal and Terminus History of the Eastern Amundsen Sea Embayment, West Antarctica, 1972 - 2011 AMD_USAPDC STAC Catalog 1947-01-01 2011-11-30 -110, -76, -100, -74 https://cmr.earthdata.nasa.gov/search/concepts/C2532070771-AMD_USAPDC.umm_json This data set provides a coastline history of the eastern Amundsen Sea Embayment and terminus histories of its outlet glaciers derived from those coastlines. These outlet glaciers include Smith, Haynes, Thwaites, and Pine Island Glaciers. The coastlines were derived from detailed tracing of Landsat imagery between late 1972 and late 2011 (at a scale of 1:50,000). The data set also uses some additional data from other sources. The terminus histories are calculated as the intersections between these coastlines and 1996 flowlines. Data are available via FTP in ESRI shapefile and comma separated value (.csv) formats. proprietary NSIDC-0525_1 MEaSUREs InSAR-Based Ice Velocity Maps of Central Antarctica: 1997 and 2009 V001 NSIDC_ECS STAC Catalog 1997-09-09 2009-12-31 -180, -90, 180, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1353062834-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of two high-resolution digital mosaics of ice motion in Central Antarctica. The mosaics were assembled from satellite interferometric synthetic-aperture radar (InSAR) data acquired by RADARSAT-1 in 1997 and by RADARSAT-2 in 2009. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary NSIDC-0530_1 MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0 V001 NSIDC_ECS STAC Catalog 1999-01-01 2012-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000001840-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users 25 km Northern Hemisphere snow cover extent represented by four different variables. Three of the snow cover variables are derived from the Interactive Multisensor Snow and Ice Mapping System, MODIS Cloud Gap Filled Snow Cover, and passive microwave brightness temperatures, respectively. The fourth variable merges the three source products into a single representation of snow cover. proprietary @@ -12659,8 +12659,8 @@ NSIDC-0533_1 MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0 V001 NSIDC NSIDC-0534_1 MEaSUREs Northern Hemisphere State of Cryosphere Daily 25km EASE-Grid 2.0 V001 NSIDC_ECS STAC Catalog 1999-01-01 2012-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1402083137-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between three different snow cover data sources. proprietary NSIDC-0535_1 MEaSUREs Northern Hemisphere State of Cryosphere Weekly 100km EASE-Grid 2.0 V001 NSIDC_ECS STAC Catalog 1979-01-02 2012-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1628163642-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between snow cover maps derived from two different sources. proprietary NSIDC-0538_1 Bubble Number-density Data and Modeled Paleoclimates AMD_USAPDC STAC Catalog 2008-01-10 2008-06-18 -112.3, -79.433333, -112.3, -79.433333 https://cmr.earthdata.nasa.gov/search/concepts/C2532070716-AMD_USAPDC.umm_json This data set includes bubble number-density measured at depths from 120 meters to 560 meters at 20-meter intervals in both horizontal and vertical samples. The data set also includes modeled temperature reconstructions based on the model developed by Spencer and others (2006). proprietary -NSIDC-0539_1 Abrupt Change in Atmospheric CO2 During the Last Ice Age AMD_USAPDC STAC Catalog 2009-01-01 2012-12-31 -148.82, -81.66, -119.83, -80.01 https://cmr.earthdata.nasa.gov/search/concepts/C2532070709-AMD_USAPDC.umm_json During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes. proprietary NSIDC-0539_1 Abrupt Change in Atmospheric CO2 During the Last Ice Age ALL STAC Catalog 2009-01-01 2012-12-31 -148.82, -81.66, -119.83, -80.01 https://cmr.earthdata.nasa.gov/search/concepts/C2532070709-AMD_USAPDC.umm_json During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes. proprietary +NSIDC-0539_1 Abrupt Change in Atmospheric CO2 During the Last Ice Age AMD_USAPDC STAC Catalog 2009-01-01 2012-12-31 -148.82, -81.66, -119.83, -80.01 https://cmr.earthdata.nasa.gov/search/concepts/C2532070709-AMD_USAPDC.umm_json During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes. proprietary NSIDC-0541_1 Allan Hills Stable Water Isotopes AMD_USAPDC STAC Catalog 2009-01-01 2011-12-31 159, -76.83, 159.25, -75.67 https://cmr.earthdata.nasa.gov/search/concepts/C2532070698-AMD_USAPDC.umm_json This data set includes stable water isotope values at 10 m resolution along an approximately 5 km transect through the main icefield of the Allan Hills Blue Ice Area, and at 15 cm within a 225 m core drilled at the midpoint of the transect. proprietary NSIDC-0543_1 AMSR-E/Aqua Monthly Global Microwave Land Surface Emissivity, Version 1 NSIDCV0 STAC Catalog 2002-07-01 2008-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1386205524-NSIDCV0.umm_json This data set is a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). The data set complements existing land emissivity products from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Sounding Unit (AMSU) by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Land surface emissivity estimates for this data set were collected at the following vertically and horizontally polarized (V-pol and H-pol) frequencies: 6.9, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, were obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties were obtained from TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. The data set is in monthly format that is extracted from instantaneous emissivity estimates. Data are stored in HDF4 files and are available via FTP. proprietary NSIDC-0545_1 MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica V001 NSIDC_ECS STAC Catalog 1996-01-01 2012-12-31 -127.3826, -80.4614, 82.8345, -71.9876 https://cmr.earthdata.nasa.gov/search/concepts/C1353062858-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides high-resolution, digital mosaics of ice motion in the Amundsen Sea Embayment (ASE) and West Antarctica, including the Pine Island, Thwaites, Haynes, Pope, Smith, and Kohler glaciers. The mosaics were assembled from interferometric synthetic-aperture radar (InSAR) data acquired in 1996, 2000, 2002, and 2006-2012 by various satellites. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary @@ -12676,8 +12676,8 @@ NSIDC-0611_4 EASE-Grid Sea Ice Age, Version 4 NSIDCV0 STAC Catalog 1984-01-01 20 NSIDC-0627_1 Borehole Temperatures at Pine Island Glacier, Antarctica AMD_USAPDC STAC Catalog 2012-12-20 2013-05-10 -100.5, -75.1, -100.5, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2532070657-AMD_USAPDC.umm_json This data set is a time series of borehole temperatures at different depths from three thermistor strings deployed in three boreholes drilled through the Pine Island Glacier ice shelf, Antarctica. proprietary NSIDC-0630_1 MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR V001 NSIDC_ECS STAC Catalog 1978-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1371883515-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, is an improved, enhanced-resolution, gridded passive microwave Earth System Data Record (ESDR) for monitoring cryospheric and hydrologic time series from SMMR, SSM/I-SSMIS, and AMSR-E. It is derived from the most mature and available Level-2 satellite passive microwave records from 1978 through the present. proprietary NSIDC-0630_2 Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR V002 NSIDC_ECS STAC Catalog 1978-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464104-NSIDC_ECS.umm_json The Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 2 data set is a multi-sensor Level 3 Earth Science Data Record (ESDR) with improvements upon Version 1 in cross-sensor calibration and quality checking, modern file formats, better quality control, improved projection grids, and local time-of-day (LTOD) processing. These data are gridded to three EASE-Grid 2.0 projections (North Azimuthal, South Azimuthal, and Cylindrical) and include enhanced-resolution imagery, as well as coarse-resolution, averaged imagery. Inputs include brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2). proprietary -NSIDC-0634_1 Alaska Tidewater Glacier Terminus Positions, Version 1 ALL STAC Catalog 1948-01-01 2012-12-31 -151, 56.5, -132, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C1386250732-NSIDCV0.umm_json This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images. proprietary NSIDC-0634_1 Alaska Tidewater Glacier Terminus Positions, Version 1 NSIDCV0 STAC Catalog 1948-01-01 2012-12-31 -151, 56.5, -132, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C1386250732-NSIDCV0.umm_json This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images. proprietary +NSIDC-0634_1 Alaska Tidewater Glacier Terminus Positions, Version 1 ALL STAC Catalog 1948-01-01 2012-12-31 -151, 56.5, -132, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C1386250732-NSIDCV0.umm_json This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images. proprietary NSIDC-0637_1 Borehole Temperature Measurement in WDC05A in January 2008 and January 2009 AMD_USAPDC STAC Catalog 2008-01-01 2009-01-01 -112.125, -79.463, -112.125, -79.463 https://cmr.earthdata.nasa.gov/search/concepts/C2532071518-AMD_USAPDC.umm_json This data set includes borehole temperature measurements performed in January 2008 and January 2009 at the West Antarctic Ice sheet divide from the 300 m hole WDC05A. proprietary NSIDC-0642_2 MEaSUREs Annual Greenland Outlet Glacier Terminus Positions from SAR Mosaics V002 NSIDC_ECS STAC Catalog 1972-09-16 2021-03-25 -75, 60, -14, 83 https://cmr.earthdata.nasa.gov/search/concepts/C2139015179-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of annual, digitized (polyline) ice front positions for 239 outlet glaciers in Greenland. Ice front positions are derived from Sentinel-1A, Sentinel-1B, and RADARSAT-1 synthetic aperture radar (SAR) mosaics, plus imagery from Landsat 1 through Landsat 5 and Landsat 7 and Landsat 8. Although temporal coverage varies by glacier, data are available for the winter seasons 1972–1973 through 2020–2021. Data are provided as shapefiles. See Greenland Ice Mapping Project (GrIMP) for related data." proprietary NSIDC-0644_1 Greenland Annual Accumulation along the EGIG Line, 1959–2004, from Airborne Radar and Neutron Probe Densities, Version 1 NSIDCV0 STAC Catalog 1959-10-01 2004-09-30 -42.838297, 70.585609, -36.232431, 71.207715 https://cmr.earthdata.nasa.gov/search/concepts/C1436304012-NSIDCV0.umm_json This data set reports mean annual snow accumulation rates in meters water equivalent (m·w.e.) from 1959 to 2004 along a 250 km segment of the Expéditions Glaciologiques Internationales au Groenland (EGIG) line. Accumulation rates are derived from Airborne SAR/Interferometric Radar Altimeter System (ASIRAS) data and high resolution neutron-probe (NP) density profiles. proprietary @@ -12739,8 +12739,8 @@ NVAP_OCEAN_Total-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP-M) NVAP_WEATHER_Layered-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP-M) WEATHER Layered Precipitable Water LARC_ASDC STAC Catalog 1988-01-01 2009-12-01 180, -90, -179.9, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1596748680-LARC_ASDC.umm_json NVAP_WEATHER_Layered-Precipitable-Water data set is designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Land GPS sites were added beginning in 1997. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets. proprietary NVAP_WEATHER_Total-Precipitable-Water_1 NASA Water Vapor Project MEaSUREs (NVAP-M) NVAP WEATHER Total Precipitable Water LARC_ASDC STAC Catalog 1988-01-01 2009-12-01 180, -90, -179.9, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1600355222-LARC_ASDC.umm_json NVAP_WEATHER_Total-Precipitable-Water data set is designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets. proprietary NWS0007 Compilation/Evaluation of Historical Tsunamis in the Pacific Using the USGS/NEIC Earthquake Data, NOAA/NGDC Tsunami Data, and Imamura-Iida Scale CEOS_EXTRA STAC Catalog 1690-01-01 95, -60, -65, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2231550342-CEOS_EXTRA.umm_json These data sets are based on an area-by-area study of the Pacific Basin to document historical tsunamis and quantify historical coastal damage both near the source and at far-field locations. An operational modification of the Imamura-Iida Scale is used for this purpose. proprietary -NWT_Burn_Severity_Maps_1694_1 ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015 ALL STAC Catalog 2014-05-01 2015-10-01 -124.03, 58.29, -108.83, 65.55 https://cmr.earthdata.nasa.gov/search/concepts/C2143402644-ORNL_CLOUD.umm_json This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks. proprietary NWT_Burn_Severity_Maps_1694_1 ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015 ORNL_CLOUD STAC Catalog 2014-05-01 2015-10-01 -124.03, 58.29, -108.83, 65.55 https://cmr.earthdata.nasa.gov/search/concepts/C2143402644-ORNL_CLOUD.umm_json This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks. proprietary +NWT_Burn_Severity_Maps_1694_1 ABoVE: Burn Severity of Soil Organic Matter, Northwest Territories, Canada, 2014-2015 ALL STAC Catalog 2014-05-01 2015-10-01 -124.03, 58.29, -108.83, 65.55 https://cmr.earthdata.nasa.gov/search/concepts/C2143402644-ORNL_CLOUD.umm_json This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks. proprietary NW_microcosm_results_1 Mineralisation results using 14C octadecane at a range of water, nutrient levels and freeze thaw cycles AU_AADC STAC Catalog 2001-06-01 2001-10-29 110.45953, -66.31249, 110.59637, -66.261 https://cmr.earthdata.nasa.gov/search/concepts/C1214313663-AU_AADC.umm_json Geochemical, microbial and 14C data on remediation of petroleum hydrocarbons in Antarctica. This record is part of ASAC project 1163 (ASAC_1163). Microcosm study using Old Casey petroleum hydrocarbon contaminated sediment investgating the effect of water, nutrients and freze/thaw cycles on biodegradation. Temperature range -4 to 28 degrees. Microcosms with three different levels of nutrients and three different levels of water were investigated. The experiment was run over 95 days. Degradation was traced by radiometric methods and total aliphatic hydrocarbons were measured by gas chromatography. Radiometric data in file radiometric_01.xls, Gas Chromatography data in file gc_01.xls. This work was completed as part of ASAC project 1163 (ASAC_1163). The radiometric spreadsheet is divided up as follows: CODES is a summary of what went into each microcosm. CALCULATIONS is how much nutrients, water, radioactivity was added to the sediment. SUMMARY is what went into each microcosm flask. CT1, CT2 etc is the raw data, what was measured and calculations of radioactivity and recovery of isotope. Note that the Evaporation flasks (i.e., E10a) the number refers to the temperature that the flasks were incubated at, 'a' and 'b' refer to duplicates. AVERAGE is the average recoveries and first order rates of the triplicate microcosm for each treatment. GRAPHS is the graphs. The fields in this dataset are: Days Hours Initial flask weight NaOH removed NaOH added Weight of NaOH (g) Count (dpm) Discarded dpm's Volume NaOH (ml) dpm in trap Absolute dpm's %dpm recovered millimole octadecane mineralised proprietary NatalMuseum Natal Museum - Mollusc Collection (Bivalvia and Gastropoda) CEOS_EXTRA STAC Catalog 1894-01-01 2005-07-09 11.38667, -43.19167, 55.13334, -11 https://cmr.earthdata.nasa.gov/search/concepts/C2232477685-CEOS_EXTRA.umm_json The Natal Museum's Department of Mollusca had its origins in the shell collection and library of Henry Burnup, a dedicated amateur who was honorary curator of molluscs until his death in 1928. Subsequently, the collection has been expanded many times over through field work, donation, exchange and purchase. Its historical value was greatly increased by absorption of important shell collections housed the Transvaal Museum (1978) and Albany Museum (1980), as well as the Rodney Wood collection from the Seychelles received from the Mutare Museum in Zimbabwe and the Kurt Grosch collection, built up over 25 years of residence in northern Mozambique. The mollusc collection now ranks among the 15 largest in the world and is certainly the largest both in Africa and on the Indian Ocean rim. It currently contains 7233 Bivalvia records, and 20112 Gastropoda records (total 27345 records of 282 families). The collection will be updated in the near future. proprietary Nested_DGGE_1 Molecular comparison of bacterial diversity in uncontaminated and hydrocarbon contaminated marine sediment AU_AADC STAC Catalog 1997-11-01 1998-11-30 110.32471, -66.51764, 110.67627, -66.2226 https://cmr.earthdata.nasa.gov/search/concepts/C1214313662-AU_AADC.umm_json Sediment samples which were originally collected as part of ASAC 868 (ASAC_868) are now being investigated using molecular microbial techniques as part of ASAC 1228 (ASAC_1228). Samples were collected in a nested survey design in two hydrocarbon impacted areas and two unimpacted areas. Denaturing gradient gel electrophoresis (DGGE) of a region of the 16S RNA gene was used to investigate the microbial community structure. Banding patterns obtained from the DGGE were transformed into a presence / absence matrix and analysed with a multivariate statistical approach. The download file contains an excel spreadsheet, a csv version of the data, plus a readme file. proprietary @@ -12774,8 +12774,8 @@ NmTHIRmtg-1T_1 Nimbus Temperature-Humidity Infrared Radiometer Global Montage Gr Nome_Veg_Plots_1372_1 Arctic Vegetation Plots at Nome, Alaska, 1951 ORNL_CLOUD STAC Catalog 1951-07-30 1951-08-02 -165.26, 64.63, -165.26, 64.63 https://cmr.earthdata.nasa.gov/search/concepts/C2170969899-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species and cover, and soil characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping and analysis of geo-botanical factors in the Nome River Valley and across Alaska. proprietary Non-Forest_Trees_Sahara_Sahel_1832_1 An Unexpectedly Large Count of Trees in the West African Sahara and Sahel ORNL_CLOUD STAC Catalog 2005-11-01 2018-03-31 -18, 11.35, -5.49, 24.03 https://cmr.earthdata.nasa.gov/search/concepts/C2761798565-ORNL_CLOUD.umm_json This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50-cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NDVI) images at 0.5-m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time. proprietary Nongrowing_Season_CO2_Flux_1692_1 Synthesis of Winter In Situ Soil CO2 Flux in pan-Arctic and Boreal Regions, 1989-2017 ORNL_CLOUD STAC Catalog 1989-09-01 2017-04-30 -163.71, 53.88, 161.99, 78.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143403370-ORNL_CLOUD.umm_json This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (>53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources. proprietary -NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ORNL_CLOUD STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ALL STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary +NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ORNL_CLOUD STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary North_Carolina_Coast_0 Measurements made off the North Carolina coast OB_DAAC STAC Catalog 2001-04-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360528-OB_DAAC.umm_json Measurements made off the North Carolina coast. proprietary North_Carolina_Sabrina_0 Measurements from the Outer Banks and coastal regions of North Carolina onboard the R/V Sabrina OB_DAAC STAC Catalog 2002-09-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360529-OB_DAAC.umm_json Measurements taken by the research vessel Sabrina in the Outer Banks and coastal regions of North Carolina in 2002 and 2003. proprietary North_Sea_0 Measurements taken in the North Sea in 1994 OB_DAAC STAC Catalog 1994-07-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360530-OB_DAAC.umm_json Measurements taken in the North Sea in 1994. proprietary @@ -12895,8 +12895,8 @@ OCO3_L2_Standard_10 OCO-3 Level 2 geolocated XCO2 retrievals results, physical m OCO3_L2_Standard_10r OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V10r (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2082387252-GES_DISC.umm_json Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary OCO3_L2_Standard_11 OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Forward Processing V11 (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3272764617-GES_DISC.umm_json Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary OCO3_L2_Standard_11r OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V11r (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2910086890-GES_DISC.umm_json Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary -OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary @@ -12909,14 +12909,14 @@ OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2 OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834711-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034341-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary @@ -12927,46 +12927,46 @@ OCTS_L3b_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate OCTS_L3b_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034363-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PIC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834762-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PIC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834762-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034382-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034382-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_POC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834780-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034382-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_POC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834780-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034364-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_POC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834780-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034364-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_RRS_2022.0 ADEOS-I OCTS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834794-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034364-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_RRS_2022.0 ADEOS-I OCTS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834794-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034342-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_RRS_2022.0 ADEOS-I OCTS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834794-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034342-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034342-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_CHL_2022.0 ADEOS-I OCTS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834809-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_CHL_2022.0 ADEOS-I OCTS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834809-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PIC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834831-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PIC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834831-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary ODIN.SMR_5.0 ODIN SMR data products ESA STAC Catalog 2001-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689700-ESA.umm_json The latest Odin Sub-Millimetre Radiometer (SMR) datasets have been generated by Chalmers University of Technology and Molflow within the Odin-SMR Recalibration and Harmonisation project (http://odin.rss.chalmers.se/), funded by the European Space Agency (ESA) to create a fully consistent and homogeneous dataset from the 20 years of satellite operations. The Odin satellite was launched in February 2001 as a joint undertaking between Sweden, Canada, France and Finland, and is part of the ESA Third Party Missions (TPM) programme since 2007. The complete Odin-SMR data archive was reprocessed applying a revised calibration scheme and upgraded algorithms. The Level 1b dataset is entirely reconsolidated, while Level 2 products are regenerated for the main mesospheric and stratospheric frequency modes (i.e., FM 01, 02, 08, 13, 14, 19, 21, 22, 24). The resulting dataset represents the first full-mission reprocessing campaign of the mission, which is still in operation. proprietary ODU_CBM_0 Old Dominion University (ODU) - Chesapeake Bay Mouth (CBM) measurements OB_DAAC STAC Catalog 2004-05-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360566-OB_DAAC.umm_json Measurements made of the Chesapeake Bay Mouth (CBM) by Old Dominion University (ODU) between 2004 and 2006. proprietary -OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 ALL STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 CEOS_EXTRA STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary +OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 ALL STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 ALL STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 CEOS_EXTRA STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary OFR_95-78_1 Geometeorological data collected by the USGS Desert Winds Project at Gold Spring, Great Basin Desert, northeastern Arizona, 1979-1992 CEOS_EXTRA STAC Catalog 1979-01-27 1992-12-31 -111, 35, -111, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550505-CEOS_EXTRA.umm_json This data set contains meteorological data files pertaining to the Gold Spring Geomet research site. Documentation files and data-accessing display software are also included. The meteorological data are wind speed, peak gust, wind direction, precipitation, air temperature, soil temperature, barometric pressure, and humidity. Data from the monitoring station are voluminous; 14 observations from each station are made as often as ten times per hour, totaling more than a million observations per station per year. proprietary @@ -13059,8 +13059,8 @@ OMAEROZ_003 OMI/Aura Aerosol product Multi-wavelength Algorithm Zoomed 1-Orbit L OMAERO_003 OMI/Aura Multi-wavelength Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 (OMAERO) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966755-GES_DISC.umm_json The Level-2 Aura Ozone Monitoring Instrument (OMI) Aerosol Product (OMAERO) is now available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. This is the second public release of version 003. The data was re-processed in late 2011 using an improved algorithm (processing version 1.2.3.1). After some quick validation the reprocessed data was released to the public in March 2012. The shortname for this Level-2 Aerosol Product is OMAERO_V003. There are two Level-2 Aura OMI aerosol products OMAERUV and OMAERO. The OMAERUV product uses the near-UV algorithm. The OMAERO product is based on the multi-wavelength algorithm and that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAERO retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAERO product contains Aerosol Optical Depths, Single Scattering Albedo, and other ancillary and geolocation information. The OMAERO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERO data product is about 6 Mbytes. proprietary OMAEROe_003 OMI/Aura Multi-wavelength Aerosol Optical Depth and Single Scattering Albedo L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMAEROe) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136062-GES_DISC.umm_json The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle). The OMAEROe files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROe data product is about 7 Mbytes. (The shortname for this Level-3 Global Gridded Aerosol Product is OMAEROe) proprietary OMAERUVG_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMAERUVG) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136097-GES_DISC.umm_json This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes. proprietary -OMAERUV_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ proprietary OMAERUV_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 (OMAERUV) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966768-GES_DISC.umm_json The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. proprietary +OMAERUV_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000120-OMINRT.umm_json The OMI/Aura level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml NASA Aura satellite sensors are tracking important atmospheric pollutants from space since its launch in July, 2004. The Ozone Monitoring Instrument(OMI), one of the four Aura satellite sensors with its 2600 km viewing swath width provides daily global measurements of four important US Environmental Protection Agency criteria pollutants (Tropospheric ozone, Nitrogen dioxide,Sulfur dioxide and Aerosols from biomass burning and industrial emissions, HCHO, BrO, OClO and surface UV irradiance. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). The Level-2 OMI Aerosol Product OMAERUV from the Aura-OMI is now available from NASAs GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). Another standard OMI aerosol product is OMAERO, that is based on the KNMI multi-wavelength spectral fitting algorithm. OMAERUV files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMAERUV Readme Document that includes brief algorithm description and currently known data quality issues is provided by the OMAERUV Algorithm lead (see http://disc.gsfc.nasa.gov/Aura/OMI/omaeruv_v003.shtml) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . OMAERUV Data Groups and Parameters: The OMAERUV data file contains a swath which consists of two groups: Data fields: Total Aerosol Optical Depth (extinction optical depth) and Aerosol Absorption Optical Depths (at 354, 388 and 500 nm), Single Scattering Albedo, UV Aerosol Index, Visible Aerosol Index, and other intermediate and ancillary parameters (e.g. Estimates of Aerosol Total Extinction and Absorption Optical Depths and Single Scattering Albedo at five atmospheric levels, Aerosol Type, Aerosol Layer Height, Normalized Radiance, Lambert equivalent Reflectivity, Surface Albedo, Imaginary Component of Refractive Index) and Data Quality Flags. Geolocation Fields: Latitude, Longitude, Time(TAI93), Seconds, Solar Zenith Angles, Viewing Zenith Angles, Relative Azimuth Angle, Terrain Pressure, Ground Pixel Quality Flags. For the full set of Aura products available from the GES DISC, please see the link below. http://disc.sci.gsfc.nasa.gov/Aura/ Atmospheric Composition data from Aura and other satellite sensors can be ordered from the following sites: http://disc.sci.gsfc.nasa.gov/acdisc/ proprietary OMAERUV_004 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13x24 km V004 (OMAERUV) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3185856256-GES_DISC.umm_json The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product OMAERUV (Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Optical Depth, Aerosol Single Scattering Albedo, Absorption Optical Depth, UV Aerosol Index, and Aerosol Optical Depth over clouds at three wavelengths (354, 388, and 500 nm), and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 4.0 Network Common Data Form (NetCDF). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 17 Mbytes. proprietary OMAERUV_CPR_003 OMI/Aura Level 2 Near UV Aerosol Optical Depth and Single Scattering Albedo 200-m swath subset along CloudSat track V003 (OMAERUV_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2017-05-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350969-GES_DISC.umm_json This is a CloudSat-collocated subset of the original OMI product OMAERUV, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 near-UV aerosol subset is OMAERUV_CPR_003) proprietary OMAERUVd_003 OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo L3 1 day 1.0 degree x 1.0 degree V3 (OMAERUVd) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136096-GES_DISC.umm_json The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV. The OMAERUV data product is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data. The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm). The OMAERUVd files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAERUVd data product is about 0.2 Mbytes. proprietary @@ -13154,15 +13154,15 @@ OMPS_NPP_NPBUVO3_L2_2 OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital G OMPS_NPP_NPBUVO3_L2_2.9 OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital V2.9 (OMPS_NPP_NPBUVO3_L2) at GES DISC GES_DISC STAC Catalog 2011-11-13 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C2821060582-GES_DISC.umm_json The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The files are written using the Hierarchical Data Format Version 5 or HDF5. proprietary OMPS_NPP_NPEV_L1B_2 OMPS/NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital V2 (OMPS_NPP_NPEV_L1B) at GES DISC GES_DISC STAC Catalog 2011-11-13 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1279850611-GES_DISC.umm_json The OMPS-NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Profiler sensor on the Suomi-NPP satellite. Each granule typically contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is nearly global (-82 to 82 degrees latitude), and there are about 14.5 orbits per day each with a single nadir measurement along the satellite track. proprietary OMSO2G_003 OMI/Aura Sulphur Dioxide (SO2) Total Column Daily L2 Global Gridded 0.125 degree x 0.125 degree V3 (OMSO2G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136113-GES_DISC.umm_json This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags. The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes. proprietary -OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/ proprietary OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li. The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds. proprietary +OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/ proprietary OMSO2_CPR_003 OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350970-GES_DISC.umm_json "This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003) This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2. The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the ""zoom mode"" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: 1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, 4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes." proprietary OMSO2e_003 OMI/Aura Sulfur Dioxide (SO2) Total Column Daily L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMSO2e) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136112-GES_DISC.umm_json "The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the ""best pixel"", selected from all ""good"" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid. The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model." proprietary OMTO3G_003 OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMTO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136114-GES_DISC.umm_json This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes. proprietary -OMTO3G_004 OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V004 (OMTO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3377057175-GES_DISC.umm_json This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes. proprietary +OMTO3G_004 OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V004 (OMTO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3377057175-GES_DISC.umm_json This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes. proprietary OMTO3_003 OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ . proprietary OMTO3_003 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary -OMTO3_004 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V004 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3377057082-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary +OMTO3_004 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V004 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3377057082-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm for this product was originally developed by a team led by Dr. Pawan K. Bhartia at NASA Goddard Space Flight Center. The current product lead is Dr. Can Li. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary OMTO3_CPR_003 OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350982-GES_DISC.umm_json This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003) proprietary OMTO3d_003 OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction L3 1 day 1 degree x 1 degree V3 (OMTO3d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136070-GES_DISC.umm_json The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes. proprietary OMTO3d_004 OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction L3 1 day 1 degree x 1 degree V004 (OMTO3d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3377057241-GES_DISC.umm_json The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes. proprietary @@ -13374,8 +13374,8 @@ PAL-LTER_0 Palmer Station Antarctica (PAL) Long Term Ecological Research Network PARASOLRB_CPR_001 POLDER/Parasol L2 Radiation Budget subset along CloudSat track V001 (PARASOLRB_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2010-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350976-GES_DISC.umm_json This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA. The algorithm first converts the original POLDER binary data, which is Level-2 but nevertheless in a sinusoidal grid, into HDF4 format, and thus stores the full-sized data in HDF4. Then, it calculates the CloudSat ground track coordinates, and proceeds to extract the closest POLDER grid cells. Along with the extraction, the algorithm re-orders the subset grid cells in a line-by-line fashion, so that the output subset is in array format and resembles a swath. This array has a cross-track dimension of 11 columns. That makes about 200-km-wide coverage. All original parameters are preserved in the subset. As it is collocated with CloudSat, the subset is automatically collocated with CALIPSO as well. proprietary PASSCAL_ABBA Adirondack Broad Band Array (ABBA) ALL STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary PASSCAL_ABBA Adirondack Broad Band Array (ABBA) SCIOPS STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary -PASSCAL_ALAR Aleutian Arc Seismic Experiment SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary PASSCAL_ALAR Aleutian Arc Seismic Experiment ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary +PASSCAL_ALAR Aleutian Arc Seismic Experiment SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary PASSCAL_WABASH A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley ALL STAC Catalog 1995-11-01 1996-06-30 -88.1706, 38.2057, -88.1706, 38.2057 https://cmr.earthdata.nasa.gov/search/concepts/C1214608969-SCIOPS.umm_json Recent paleoseismic evidence had shown there were 5-8 magnitude greater than 6 earthquakes in this region in the past 20,000 years. The study area has always been at the fringe of previously operated seismic networks. A focused, short-term deployment was designed to lower the detection threshold to determine seismicity rates for the region for comparison with estimates derived from paleoseismicity. The researchers hoped to relate observed seismicity to faults mapped in the subsurface through new seismic reflection data made available to the Illinois Basin Consortium. proprietary PASSCAL_WABASH A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley SCIOPS STAC Catalog 1995-11-01 1996-06-30 -88.1706, 38.2057, -88.1706, 38.2057 https://cmr.earthdata.nasa.gov/search/concepts/C1214608969-SCIOPS.umm_json Recent paleoseismic evidence had shown there were 5-8 magnitude greater than 6 earthquakes in this region in the past 20,000 years. The study area has always been at the fringe of previously operated seismic networks. A focused, short-term deployment was designed to lower the detection threshold to determine seismicity rates for the region for comparison with estimates derived from paleoseismicity. The researchers hoped to relate observed seismicity to faults mapped in the subsurface through new seismic reflection data made available to the Illinois Basin Consortium. proprietary PATEX_0 PATagonia EXperiment (PATEX) Project OB_DAAC STAC Catalog 2004-11-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360589-OB_DAAC.umm_json PATagonia EXperiment (PATEX) Project is a Brazilian research project, which has the overall objective of characterizing the environmental constraints, phytoplankton assemblages, primary production rates, bio-optical characteristics, and air-sea CO2 fluxes waters along the Argentinean shelf-break during austral spring and summer. A set of seven PATEX cruises were conducted from 2004 to 2009. Garcia et al., 2011 (doi:10.1029/2010JC006595) proprietary @@ -13411,14 +13411,14 @@ POLARIS_TraceGas_AircraftInSitu_ER2_Data_1 POLARIS ER-2 Aircraft In-situ Trace G POLARIS_jValue_AircraftInSitu_ER2_Data_1 POLARIS Photolysis Frequencies (J-Values) LARC_ASDC STAC Catalog 1997-01-06 1997-09-26 180, -3.37, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2712794398-LARC_ASDC.umm_json POLARIS_jValue_AircraftInSitu_ER2_Data is the photolysis frequencies (j-values) collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the the Composition and Photo-Dissociative Flux Measurement (CPFM) is featured in this collection. Data collection for this product is complete. The POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport. proprietary POLYNYA_ship_1 Mertz Polynya Experiment, Aurora Australis science cruises au9807 and au9901, and Tangaroa science cruise ta0051 - ship-based CTD, ADCP, LADCP and mooring data AU_AADC STAC Catalog 1998-04-03 2000-03-20 142, -67.5, 148, -64.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214313670-AU_AADC.umm_json Oceanographic measurements were conducted in the vicinity of the Mertz Polynya, encompassing 2 consecutive seasonal cycles from 1998 to 2000. In the southern winter of 1999, a total of 92 CTD/LADCP vertical profile stations were taken, most to within 20 m of the bottom, with 3 laps completed around the boundary of a box adjacent to the Mertz Glacier. Over 700 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, oxygen 18, dimethyl sulphide, and biological parameters, using a 12 bottle rosette sampler mounted on a 24 bottle frame. Additional CTD vertical profiles were taken in April 1998, July 1998 and February 2000. Near surface current data were collected on all cruises using ship mounted ADCP. Two mooring arrays comprising thermosalinographs, current meters and upward looking sonars were deployed in the region of the Polynya. The first array of 7 moorings was deployed in April 1998. The second array of 4 moorings was deployed in the winter of 1999. All 11 Polynya moorings were recovered in February 2000. A summary of all data and data quality is presented in the data report. This work was completed as part of ASAC projects 2223 and 189. proprietary POMME_0 Programme Ocean Multidisciplinaire Meso-Echelle (POMME) OB_DAAC STAC Catalog 2001-02-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360620-OB_DAAC.umm_json Measurements made during the Programme Ocean Multidisciplinaire Meso-Echelle (POMME) or Multidisciplinary middle-level ocean program in 2001. proprietary -POSTER-03CYCLONE_Not Applicable 2003 Tropical Cyclones of the World ALL STAC Catalog 2003-01-08 2003-12-21 -180, -65, 180, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2107093337-NOAA_NCEI.umm_json "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36""x 27""." proprietary POSTER-03CYCLONE_Not Applicable 2003 Tropical Cyclones of the World NOAA_NCEI STAC Catalog 2003-01-08 2003-12-21 -180, -65, 180, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2107093337-NOAA_NCEI.umm_json "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36""x 27""." proprietary -POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster NOAA_NCEI STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary +POSTER-03CYCLONE_Not Applicable 2003 Tropical Cyclones of the World ALL STAC Catalog 2003-01-08 2003-12-21 -180, -65, 180, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2107093337-NOAA_NCEI.umm_json "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36""x 27""." proprietary POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster ALL STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary +POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster NOAA_NCEI STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster ALL STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster NOAA_NCEI STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary -POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster ALL STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster NOAA_NCEI STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary +POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster ALL STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary PRECIP_AMSR2_GCOMW1_1 NASA MEASURES Precipitation Ensemble based on AMSR2 GCOMW1 NASA PPS L1C V05 TBs 1-orbit L2 Swath 10x10km V1 (PRECIP_AMSR2_GCOMW1) at GES DISC GES_DISC STAC Catalog 2012-07-02 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368305620-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) flown on the Global Climate Observing Mission-Water 1 (GCOM-W1). Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2012 to 2020 with one file per orbit. proprietary PRECIP_AMSRE_AQUA_1 NASA MEASURES Precipitation Ensemble based on AMSRE AQUA NASA PPS L1C V05 Tbs 1-orbit L2 Swath 12x12km V1 (PRECIP_AMSRE_AQUA) at GES DISC GES_DISC STAC Catalog 2002-06-01 2011-10-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368306433-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-E (AMSR-E) flown on the AQUA satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2002 to 2011 with one file per orbit. proprietary PRECIP_GMI_GPM_1 NASA MEASURES Precipitation Ensemble based on GMI GPM NASA PPS L1C V05 Tbs 1-orbit L2 Swath 10x10km V1 (PRECIP_GMI_GPM) at GES DISC GES_DISC STAC Catalog 2014-03-04 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368306937-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) flown on the GPM satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2014 to 2020 with one file per orbit. proprietary @@ -13468,8 +13468,8 @@ PVST_SMARTS_0 Validating PACE aerosol columnar properties and OCI water-leaving PVST_VDIUP_0 Validation of Ocean Surface Downwelling Irradiance and Its Underwater Propagation for the PACE Mission OB_DAAC STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3252791852-OB_DAAC.umm_json This project contributes to the validation of global surface radiation products and diffuse attenuation coefficients (Kd) generated by the PACE mission, essential for quantifying net primary production. The radiation products include instantaneous, daily mean, planar, and scalar fluxes products, in particular daily mean photosynthetically available radiation (PAR). In-situ observations are gathered through a network of automatic stations measuring hyperspectral downward planar irradiance (Ed(0+)) at selected AERONET-OC sites, and BGC-Argo profilers equipped with hyperspectral Ed sensors. BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System https://doi.org/10.17882/42182. Link to BGC-Argo GDAC for raw float data: https://data-argo.ifremer.fr/aux/coriolis/. proprietary PanamaCity_0 Panama City, Florida optical measurements in 1993 OB_DAAC STAC Catalog 1993-10-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360586-OB_DAAC.umm_json Measurements taken in the Gulf of Mexico near Panama City, Florida in 1993. proprietary Panhandle_OWQ_0 Optical Water quality measurements made in the Florida Panhandle estuaries OB_DAAC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360587-OB_DAAC.umm_json Measurements made in the Florida Panhandle estuaries in partnership with USF and FWC-FWRI. proprietary -Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ALL STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary +Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ALL STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary Patagonian_Coastal_0 Measurements off the Argentinian coast near Drakes Passage OB_DAAC STAC Catalog 2008-12-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360588-OB_DAAC.umm_json Measurements made in the South Atlantic Ocean in 2008 and 2009 off the Argentinian coast near Drakes Passage. proprietary Peatland_carbon_balance_1382_1 Global Peatland Carbon Balance and Land Use Change CO2 Emissions Through the Holocene ORNL_CLOUD STAC Catalog 1000-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216864221-ORNL_CLOUD.umm_json This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes. proprietary Pelican_PCO2_0 Partial pressure of carbon dioxide (PCO2) onboard the Pelican research vessel OB_DAAC STAC Catalog 2006-04-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360591-OB_DAAC.umm_json Measurements from the Pelican research vessel made off the southern coast of Louisiana in the Gulf of Mexico from 2006. proprietary @@ -13477,8 +13477,8 @@ PenBaySurvey_0 Penobscot Bay Optical Survey OB_DAAC STAC Catalog 2007-11-15 -18 PermafrostThaw_CarbonEmissions_1872_1 Projections of Permafrost Thaw and Carbon Release for RCP 4.5 and 8.5, 1901-2299 ORNL_CLOUD STAC Catalog 1901-01-01 2300-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2254686682-ORNL_CLOUD.umm_json This dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude. The model simulations resulted from a synthesis effort organized by the Permafrost Carbon Network to evaluate the impacts of climate change on the carbon cycle in permafrost regions in the high northern latitudes. The model teams used different historical input weather data, but most used driver data developed by the Climate Research Unit - National Centers for Environmental Prediction (CRUNCEP) as modified for the Multiscale Terrestrial Model Intercomparison Project (MsTMIP). The teams scaled the driver data for the projections using output from global climate models from the fifth Coupled Model Intercomparison Project (CMIP5). The synthesis evaluated the terrestrial carbon cycle in the modern era and projected future emissions of carbon under two climate warming scenarios: Representative Concentration Pathways 4.5 and 8.5 (RCP45 and RCP85) from CMIP5. RCP45 represents emissions resulting in a global climate close to the target climate in the Paris Accord. RCP85 represents unconstrained greenhouse gas emissions. proprietary Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ALL STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ORNL_CLOUD STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary -Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ALL STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ORNL_CLOUD STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary +Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ALL STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary PhenoCam_V2_1674_2 PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018 ORNL_CLOUD STAC Catalog 1999-11-16 2018-12-31 -158.15, -22.97, 119.22, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2764826583-ORNL_CLOUD.umm_json This data set provides a time series of vegetation phenological observations for 393 sites across diverse ecosystems of the world (mostly North America) from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and means and other statistics calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals. proprietary Phenocam_Images_V2_1689_2 PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018 ORNL_CLOUD STAC Catalog 1999-11-16 2018-12-31 -158.15, -22.97, 119.22, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2764728896-ORNL_CLOUD.umm_json This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v2.0. proprietary Phenology_AmeriFlux_Neon_Sites_2033_1 Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021 ORNL_CLOUD STAC Catalog 2017-01-01 2021-12-31 -176.13, 14.34, -57.3, 70.98 https://cmr.earthdata.nasa.gov/search/concepts/C2764693210-ORNL_CLOUD.umm_json This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats. proprietary @@ -13494,20 +13494,20 @@ Pleiades.HiRI.archive.and.new_9.0 Pleiades full archive and tasking ESA STAC Cat Pleiades.Neo.full.archive.and.tasking_9.0 Pléiades Neo full archive and tasking ESA STAC Catalog 2021-04-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547572735-ESA.umm_json "Very High Resolution optical Pléiades Neo data at 30 cm PAN resolution (1.2 m 6-bands Multispectral) are available as part of the Airbus provision with twice daily revisit capability over the entire globe. The swath width is 14 km (footprint at nadir). Band combinations: • Panchromatic one band Black & White image at 0.3 m resolution • Pansharpened colour image at 0.3 m resolution: Natural colour (3 bands RGB), false colour (3 bands NIRRG), 4 bands (RGB+NIR), 6 bands • Multispectral colour image in 4 bands (RGB+NIR) or 6 bands (also Deep blue and Red Edge) at 1.2 m resolution • Bundle 0.3 m panchromatic image and 1.2 m multispectral image (4 or 6 bands) simultaneously acquired Geometric processing levels: • Primary: The Primary product is the processing level closest to the natural image acquired by the sensor. This product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is clear of all radiometric distortion. • Ortho: The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir effects. Acquisition modes: • Mono • Stereo • Tristereo • HD15: 15cm resolution for Panchromatic, 60cm resolution for Multispectral: Mono image resampled by using machine learning model which increase sharpness and fineness of details without introducing any fake data. To complement the traditional and fully customised ordering and download of selected SPOT, Pleiades or Pleiades Neo images in a variety of data formats, you can also subscribe to the OneAtlas Living Library package where the entire OneAtlas optical archive of ortho images is updated on a daily basis and made available for streaming or download. The Living Library consist of: • less-than-18-months-old Pansharpened and Bundle imagery • a curation of SPOT images with no cloud cover and less than 30° incidence angle • Pléiades images acquired worldwide with maximum 15% cloud cover and 30° Incidence Angle • Pléiades Neo premium imagery selection with 2% cloud cover and 30° incidence angle These are the available subscription packages (to be consumed withing one year from the activation) OneAtlas Living Library subscription package 1: up to 230 km2 Pleiades Neo or 430 km2 Pleiades or 1.500 km2 SPOT in download, up to 500 km2 Pleiades Neo or 2.000 km2 Pleiades or 7.500 km2 SPOT in streaming OneAtlas Living Library subscription package 2: up to 654 km2 Pleiades Neo or 1.214 km2 Pleiades or 4.250 km2 SPOT in download, up to 1417 km2 Pleiades Neo or 5.666 km2 Pleiades or 21.250 km2 SPOT in streaming OneAtlas Living Library subscription package 3: up to 1.161 km2 Pleiades Neo or 2.156 km2 Pleiades or 7.545 km2 SPOT in download, up to 2.515 km2 Pleiades Neo or 10.060 km2 Pleiades or 37.723 km2 SPOT in streaming All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPOT-Pleiades-data-terms-of-applicability.pdf available in the Resources section. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary Plot_Data_Noatak_Seward_AK_1919_1 Burned and Unburned Field Site Data, Noatak, Seward, and North Slope, AK, 2016-2018 ORNL_CLOUD STAC Catalog 2016-07-22 2018-08-27 -164.93, 65.02, -148.64, 69.66 https://cmr.earthdata.nasa.gov/search/concepts/C2240727642-ORNL_CLOUD.umm_json This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included. proprietary Plumes_and_Blooms_0 Plumes and Blooms OB_DAAC STAC Catalog 1996-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360616-OB_DAAC.umm_json The Plumes and Blooms program is a joint collaboration among UCSB faculty, student and staff researchers at the Institute of Computational Earth System Science (ICESS), NOAA researchers at the Coastal Services Center (Charleston, SC) and the NOAA sanctuary managers of the Channel Islands National Marine Sanctuary (CINMS). Since August, 1996, monthly research cruises have been conducted to collect measurements. These measurements include temperature and salinity, ocean color spectra, and water column profiles of red light transmission and chlorophyll fluorescence (indexes of suspended particulate load and phytoplankton abundance). The transect observations begin at the shelf waters north of Santa Rosa island and end at an area off Goleta Point. These repeat observations are combined with satellite imagery to build a time-series of the changing ocean color conditions in the Santa Barbara Channel. proprietary -PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ORNL_CLOUD STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ALL STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary +PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ORNL_CLOUD STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary Polar-VPRM_Alaskan-NEE_1314_1 CARVE Modeled Gross Ecosystem CO2 Exchange and Respiration, Alaska, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-12-31 -179, 55, -134, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2236236883-ORNL_CLOUD.umm_json This data set provides 3-hourly estimates of gross ecosystem CO2 exchange (GEE) and respiration (autotrophic and heterotrophic) for the state of Alaska from 2012 to 2014. The data were generated using the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) and are provided at ~ 1 km2 [1/4-degree (longitude) by 1/6-degree (latitude)] pixel resolution. The PolarVPRM produces high-frequency estimates of GEE of CO2 for North American biomes from remotely-sensed data sets. For Alaska, the model used meteorological inputs from the North American regional re-analysis (NARR) and inputs of fractional snow cover and land surface water index (LSWI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Land surface greenness was factored into the model from three sources: 1) Enhanced Vegetation Index (EVI) from MODIS; 2) Solar Induced Florescence (SIF) from the Orbiting Carbon Observatory 2 (OCO-2); and 3) SIF from the Global Ozone Monitoring Experiment 2 (GOME-2). Three independent estimates of GEE are included in the data set, one for each source of greenness observations. proprietary PolarWindsII_DAWN_DC8_1 Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 LARC_ASDC STAC Catalog 2015-05-11 2015-05-25 -59, 49, 15.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C1440079415-LARC_ASDC.umm_json PolarWindsII_DAWN_DC8_1 is the Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 data product. Data collection for this product is complete. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds. The focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA C-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. proprietary PolarWindsI_DAWN_KingAirUC-12B_1 Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B LARC_ASDC STAC Catalog 2014-10-29 2014-11-13 -58, 59, -42, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1457763994-LARC_ASDC.umm_json PolarWindsI_DAWN_KingAirUC-12B is the Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B data product. Data for this was collected using the DAWN instrument flown on the NASA Langley Beechcraft UC-12B Huron aircraft. Data collection for this product is complete. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds. The focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA UC-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap. proprietary -Polarimetric_CT_1601_1 AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools ALL STAC Catalog 2016-02-25 2016-03-08 9.17, -2.08, 11.86, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261393-ORNL_CLOUD.umm_json This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign. proprietary Polarimetric_CT_1601_1 AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools ORNL_CLOUD STAC Catalog 2016-02-25 2016-03-08 9.17, -2.08, 11.86, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261393-ORNL_CLOUD.umm_json This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign. proprietary -Polarimetric_height_profile_1577_1 AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016 ORNL_CLOUD STAC Catalog 2016-02-25 2016-02-28 9.67, -2.08, 11.86, 0.1 https://cmr.earthdata.nasa.gov/search/concepts/C2734257089-ORNL_CLOUD.umm_json This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI). proprietary +Polarimetric_CT_1601_1 AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools ALL STAC Catalog 2016-02-25 2016-03-08 9.17, -2.08, 11.86, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261393-ORNL_CLOUD.umm_json This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign. proprietary Polarimetric_height_profile_1577_1 AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016 ALL STAC Catalog 2016-02-25 2016-02-28 9.67, -2.08, 11.86, 0.1 https://cmr.earthdata.nasa.gov/search/concepts/C2734257089-ORNL_CLOUD.umm_json This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI). proprietary +Polarimetric_height_profile_1577_1 AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016 ORNL_CLOUD STAC Catalog 2016-02-25 2016-02-28 9.67, -2.08, 11.86, 0.1 https://cmr.earthdata.nasa.gov/search/concepts/C2734257089-ORNL_CLOUD.umm_json This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI). proprietary Poplar_Veg_Plots_1376_1 Arctic Vegetation Plots, Poplars, Arctic and Interior AK and YT, Canada, 2003-2005 ORNL_CLOUD STAC Catalog 2003-06-18 2005-08-17 -162.74, 61.08, -135.22, 69.47 https://cmr.earthdata.nasa.gov/search/concepts/C2170969941-ORNL_CLOUD.umm_json This data set provides vegetation cover and environmental plot data collected from 32 balsam poplar (Populus balsamifera L., Salicaceae) vegetation plots located on the Arctic Slope of Alaska and in the interior boreal forests of Alaska and the Yukon from 2003 to 2005. The estimated percent land cover by species per plot are according to the older Braun-Blanquet cover-abundance scale. Plot data includes moisture, topographic position, slope, aspect, shape, and soil data. proprietary -PostFire_Tree_Regeneration_1955_1.1 ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America ORNL_CLOUD STAC Catalog 1989-01-01 2018-12-31 -152.2, 49.12, -71.01, 66.96 https://cmr.earthdata.nasa.gov/search/concepts/C2539840222-ORNL_CLOUD.umm_json This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format. proprietary PostFire_Tree_Regeneration_1955_1.1 ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America ALL STAC Catalog 1989-01-01 2018-12-31 -152.2, 49.12, -71.01, 66.96 https://cmr.earthdata.nasa.gov/search/concepts/C2539840222-ORNL_CLOUD.umm_json This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format. proprietary -Post_Fire_C_Emissions_1787_1 ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015 ALL STAC Catalog 2015-04-06 2015-08-11 -116.06, 51.19, -100.17, 61.24 https://cmr.earthdata.nasa.gov/search/concepts/C2143401918-ORNL_CLOUD.umm_json This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis. proprietary +PostFire_Tree_Regeneration_1955_1.1 ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America ORNL_CLOUD STAC Catalog 1989-01-01 2018-12-31 -152.2, 49.12, -71.01, 66.96 https://cmr.earthdata.nasa.gov/search/concepts/C2539840222-ORNL_CLOUD.umm_json This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format. proprietary Post_Fire_C_Emissions_1787_1 ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015 ORNL_CLOUD STAC Catalog 2015-04-06 2015-08-11 -116.06, 51.19, -100.17, 61.24 https://cmr.earthdata.nasa.gov/search/concepts/C2143401918-ORNL_CLOUD.umm_json This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis. proprietary +Post_Fire_C_Emissions_1787_1 ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015 ALL STAC Catalog 2015-04-06 2015-08-11 -116.06, 51.19, -100.17, 61.24 https://cmr.earthdata.nasa.gov/search/concepts/C2143401918-ORNL_CLOUD.umm_json This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis. proprietary Post_Fire_SOC_NWT_2235_1 Post-fire Recovery of Soil Organic Layer Carbon in Canadian Boreal Forests, 2015-2018 ORNL_CLOUD STAC Catalog 2015-06-11 2018-08-24 -132.67, 59.79, -104.19, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2854211353-ORNL_CLOUD.umm_json This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format. proprietary PreABoVE_AirMOSS_L1_Alaska_1678_1 Pre-ABoVE: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Alaska, 2014-2015 ORNL_CLOUD STAC Catalog 2014-08-16 2015-10-01 -165.32, 57.22, -135.54, 71.48 https://cmr.earthdata.nasa.gov/search/concepts/C2143402734-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over 10 study sites across Northern Alaska, USA. Flight campaigns took place in August 2014, October 2014, April 2015, August 2015, September 2015, and October 2015. The acquired L1 P-band radar backscatter data will be used to derive estimates of soil water content and permafrost state at the study sites. proprietary PreDeltaX_ADCP_Measurements_1806_1 Pre-Delta-X: River Discharge Channel Surveys across Atchafalaya Basin, LA, USA, 2016 ORNL_CLOUD STAC Catalog 2016-10-15 2016-10-20 -91.44, 29.44, -91.21, 29.74 https://cmr.earthdata.nasa.gov/search/concepts/C2025124066-ORNL_CLOUD.umm_json This dataset provides river discharge measurements collected at selected locations across the Atchafalaya River Basin, within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign on October 15 to 20, 2016. Seventy-five channel surveys were conducted with a SonTek RiverSurveyor M9 acoustic doppler current profiler (ADCP) on selected wide channels (~100 m) and a few selected (~10 m) narrow channels. ADCP data provide near-instantaneous estimates of river discharge across the sampled channels. Sites coincided with AirSWOT swaths in the Atchafalaya River Basin and water level measurement locations. This in situ dataset was used to calibrate and validate Delta-X hydrodynamic models. proprietary @@ -13527,8 +13527,8 @@ PreDeltaX_Vegetation_Structure_1805_1 Pre-Delta-X: Vegetation Species, Structure PreDeltaX_Water_Level_Data_1801_1 Pre-Delta-X: Water Levels across Wax Lake Outlet, Atchafalaya Basin, LA, USA, 2016 ORNL_CLOUD STAC Catalog 2016-10-13 2016-10-20 -91.45, 29.51, -91.36, 29.74 https://cmr.earthdata.nasa.gov/search/concepts/C2025123345-ORNL_CLOUD.umm_json This dataset provides absolute water level elevations derived for 10 locations across the Wax Lake Delta, Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Field measurements were made during the Pre-Delta-X campaign on October 13-20, 2016. Relative water level measurements were recorded every five minutes during a one-week period using in situ pressure transducers (Solinst) to measure water surface elevation change with millimeter accuracy. The Solinst system combines a total pressure transducer (TPT) and a temperature detector. Once underwater, the TPT measures the sum of the atmosphere and water pressure above the sensor. Atmospheric pressure fluctuations must be accounted for to obtain the height of the water column above the TPT. An absolute elevation correction was applied to the water level data using an iterative approach with the USGS Calumet Station water level height and Airborne Snow Observatory (ASO) lidar water level profiles. These Pre-Delta-X water level measurements served to calibrate and validate the campaign's remote sensing observations and hydrodynamic models. proprietary Pre_LBA_ABRACOS_899_1.1 Pre-LBA Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) Data ORNL_CLOUD STAC Catalog 1991-01-01 1996-12-31 -75, -18, -46, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2762262185-ORNL_CLOUD.umm_json The data set presents the principal data from the Anglo-BRazilian Amazonian Climate Observation Study (ABRACOS) (Gash et al, 1996) and provides quality controlled information from five of the study topics considered by the project in five zipped files containing ASCII text data. The five study topics include Micrometeorology, Climate, Carbon Dioxide and Water Vapor, Plant Physiology, and Soil Moisture. The objectives of the ABRACOS were to monitor Amazonian climate and improve the understanding of the consequences of deforestation and to provide data for the calibration and validation of GCMs and GCM sub-models of Amazonian forest and post-deforestation pasture (Shuttleworth et al, 1991). Three areas were instrumented, each with different soils, dry season intensities and deforestation densities (Gash et al, 1996). In each area, an automatic weather station and soil moisture measurement equipment were installed: in a primary forest site and in nearby cattle pasture, for monitoring climate and soil status throughout the year. Additional intensive periods of study (or Missions), of varying duration, were operated at these sites: for calibration purposes, to understand the physical processes relevant to each site, and for detailed comparisons between sites. These data were collected under the ABRACOS project and made available by the UK Institute of Hydrology and the Instituto Nacional de Pesquisas Espaciais (Brazil). ABRACOS is a collaboration between the Agencia Brasileira de Cooperacao and the UK Overseas Development Administration. The processed, quality controlled and integrated data in the documented Pre-LBA data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. proprietary Proantar_0 Measurements off James Ross Island, Antarctica OB_DAAC STAC Catalog 2005-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360623-OB_DAAC.umm_json Measurements made off James Ross Island near Antarctica in 2005. proprietary -Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ALL STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary +Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1 Arctic Vegetation Plots, Prudhoe Bay ArcSEES Road Study, Lake Colleen, Alaska, 2014 ORNL_CLOUD STAC Catalog 2014-08-06 2014-08-13 -148.47, 70.22, -148.47, 70.22 https://cmr.earthdata.nasa.gov/search/concepts/C2162122325-ORNL_CLOUD.umm_json This dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance. proprietary Prudhoe_Bay_Veg_Maps_1387_1 Geobotanical and Impact Map Collection for Prudhoe Bay Oilfield, Alaska, 1972-2010 ORNL_CLOUD STAC Catalog 1949-01-01 2010-07-31 -150.17, 69.97, -146.97, 71.03 https://cmr.earthdata.nasa.gov/search/concepts/C2162616071-ORNL_CLOUD.umm_json This data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield. The land cover map of the Beechey Point quadrangle was derived from the Landsat multispectral scanner, aerial photography, and other field and cartographic methods. The cumulative impact maps of the Prudhoe Bay Oilfield show historical infrastructure and natural changes digitized from aerial photos taken in each successive analysis year (1968, 1970, 1972, 1973, 1977, 1979, 1983, 1990, 2001, and 2010). Nine geoecological attributes are included: dominant vegetation, secondary vegetation, tertiary vegetation, percentage open water, landform, dominant surface form, secondary surface form, dominant soil, and secondary soil. These data document environmental changes in an Arctic region that is affected by both climate change and rapid industrial development. proprietary Prudhoe_Bay_Veg_Plots_1360_1 Arctic Vegetation Plots at Prudhoe Bay, Alaska, 1973-1980 ORNL_CLOUD STAC Catalog 1973-01-01 1980-12-31 -148.95, 70.25, -148.29, 70.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170969598-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation, species, and cover; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for classification, mapping, and analysis of geobotanical factors in the Prudhoe Bay region and across Alaska. proprietary @@ -13600,15 +13600,15 @@ RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1 RapidScat Level 2B Ocean Wind Vectors in 12. RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2 RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.2 POCLOUD STAC Catalog 2015-08-19 2016-08-19 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2526576305-POCLOUD.umm_json "This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the ""Data Access"" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the ""wind_obj"" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the ""ambiguity_obj"" variable. The ""wind_obj"" variable contains DIRTH probabilities (which are derived form the ""ambiguity_obj"" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/." proprietary RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3 RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.3 POCLOUD STAC Catalog 2016-02-11 2016-08-19 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2526576326-POCLOUD.umm_json "This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data. The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the ""Data Access"" tab above. It is advised for users to avoid using the ""wind_obj"" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the ""ambiguity_obj"" variable. The ""wind_obj"" variable contains DIRTH probabilities (which are derived form the ""ambiguity_obj"" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/." proprietary RSES_PCM_1 Cosmogenic dating AU_AADC STAC Catalog 2001-12-20 63.6203, -75.2756, 73.7101, -69.7425 https://cmr.earthdata.nasa.gov/search/concepts/C1214313722-AU_AADC.umm_json The data set consists of cosmogenic exposure ages for samples collected by Research School of Earth Sciences in the Prince Charles Mountains and vicinity. Thus far work has been carried out in the 2001/2002, 2002/2003, 2003/2004 and 2004/2005 field seasons. Currently, the only data publicly available is an excel spreadsheet detailing sampling locations. The objectives of this project were: To develop a comprehensive understanding of the Lambert Glacier of East Antarctica, from the time of the last maximum glaciation to the present, through an integrated and interdisciplinary study combining new field evidence - ice retreat history from cosmogenic exposure dating, geodetic measurements of crustal rebound, satellite measurements of present ice heights and changes therein - with other geological and glaciological data and numerical geophysical modelling advances. The project contributes to the quantitative characterisation of the complex interactions between ice-sheets, oceans and solid earth within the climate system. Outcomes have implications for geophysics, glaciology, geomorphology, climate, and past and future sea-level change. This work was completed as part of ASAC projects 2502 and 2516 (ASAC_2502 and ASAC_2516). The fields in this dataset are: Sample Date Collector Type Lithology Location Elevation Latitude Longitude proprietary -RSFDCE_KLIM4 Absolute Minimum of Air Temperature. Year By Year Data ALL STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608674-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Sybiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSFDCE_KLIM4 Absolute Minimum of Air Temperature. Year By Year Data SCIOPS STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608674-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Sybiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary +RSFDCE_KLIM4 Absolute Minimum of Air Temperature. Year By Year Data ALL STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608674-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Sybiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSFDCE_KLIM5 Air Temperature 01.00 P.M. Year By Year Date SCIOPS STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608673-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Subiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSFDCE_KLIM5 Air Temperature 01.00 P.M. Year By Year Date ALL STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608673-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Subiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSS18_AVIRIS_L1B_449_1 BOREAS RSS-18 Level 1B AVIRIS At-Sensor Radiance Imagery ORNL_CLOUD STAC Catalog 1996-08-14 1996-08-14 -106.49, 53.45, -105.03, 54.32 https://cmr.earthdata.nasa.gov/search/concepts/C2929128157-ORNL_CLOUD.umm_json This dataset holds Level 1B (L1B) radiance data collected by the AVIRIS-Classic instrument near Prince Albert, Saskatchewan, Canada, on August 14, 1996. This imagery was acquired for the Boreal Ecosystem-Atmosphere Study (BOREAS) project in the boreal forests of central Canada. BOREAS focused on improving the understanding of exchanges of radiative energy, sensible heat, water, CO2 and trace gases between the boreal forest and the lower atmosphere. NASA's AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. For these data, AVIRIS-Classic was deployed on NASA's ER-2 high altitude aircraft. These spectra are acquired as images with 20-meter spatial resolution, 11 km swath width, and flight lines up to 800 km in length. The measurements are spectrally, radiometrically, and geometrically calibrated. There are seven flight lines subdivided into 66 scenes. The dataset includes the radiance imagery cube for each scene along with calibration and navigation information. The radiance data are in instrument coordinates, georeferenced by center of each scan line, and provided in a binary file. Metadata are included in a mixture of binary and text file formats. proprietary RSS_WindSat_L1C_TB_V08.0_8.0 RSS WindSat L1C Calibrated TB Version 8 POCLOUD STAC Catalog 2003-02-01 2020-10-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559430954-POCLOUD.umm_json The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run. proprietary Radarsat-2_8.0 RADARSAT-2 ESA Archive ESA STAC Catalog 2008-07-27 2021-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689631-ESA.umm_json The RADARSAT-2 ESA archive collection consists of RADARSAT-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Following Beam modes are available: Standard, Wide Swath, Fine Resolution, Extended Low Incidence, Extended High Incidence, ScanSAR Narrow and ScanSAR Wide. Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8, in single and dual polarisation . The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. BEAM MODE: Standard PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 or 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 9.0 or 13.5 x 7.7 (SLC), 26.8 - 17.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 100 x 100 Range of Angle of Incidence (deg): 20 - 52 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. Polarisation can be single and dual. BEAM MODE: Wide PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 10 x 10 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 40.0 - 19.2 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 150 x 150 Range of Angle of Incidence (deg): 20 - 45 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: Fine PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 4.7 x 5.1 (SLC), 3.13 x 3.13 (SGX), 6.25 x 6.25 (SSG, SPG) Resolution - Range x Azimuth (m): 5.2 x 7.7 (SLC), 10.4 - 6.8 x 7.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 50 x 50 Range of Angle of Incidence (deg): 30 - 50 No. of Looks - Range x Azimuth: 1 x 1 (SLC,SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. Only single polarisation is available. BEAM MODE: Extended Low PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 x 5.1 (SLC), 10.0 x 10.0 (SGX), 12.5 x 12.5 (SSG, SPG) Nominal Resolution - Range x Azimuth (m): 9.0 x 7.7 (SLC), 52.7 - 23.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 170 x 170 Range of Angle of Incidence (deg): 10 - 23 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. Only single polarisation available. BEAM MODE: Extended High PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 18.2 - 15.9 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 75 x 75 Range of Angle of Incidence (deg): 49 - 60 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH ScanSAR Narrow Beam Mode provides coverage of a ground swath approximately double the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCNA, which uses physical beams W1 and W2, and SCNB, which uses physical beams W2, S5, and S6. Both options provide coverage of swath widths of about 300 km. The SCNA combination provides coverage over the incidence angle range from 20 to 39 degrees. The SCNB combination provides coverage over the incidence angle range 31 to 47 degrees. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: ScanSAR Narrow PRODUCT: SCN, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 25 x 25 Nominal Resolution - Range x Azimuth (m):81-38 x 40-70 Nominal Scene Size - Range x Azimuth (km): 300 x 300 Range of Angle of Incidence (deg): 20 - 46 No. of Looks - Range x Azimuth: 2 x 2 Polarisations - Options: • Single Co or Cross: HH or VV or HV or VH • Dual: HH + HV or VV + VH ScanSAR Wide Beam Mode provides coverage of a ground swath approximately triple the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCWA, which uses physical beams W1, W2, W3, and S7, and SCWB, which uses physical beams W1, W2, S5 and S6. The SCWA combination allows imaging of a swath of more than 500 km covering an incidence angle range of 20 to 49 degrees. The SCWB combination allows imaging of a swath of more than 450 km covering the incidence angle. Polarisation can be single and dual. BEAM MODE: ScanSAR Wide PRODUCT: SCW, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 50 x 50 Resolution - Range x Azimuth (m): 163.0 - 73 x 78-106 Nominal Scene Size - Range x Azimuth (km): 500 x 500 Range of Angle of Incidence (deg): 20 - 49 No. of Looks - Range x Azimuth: 4 x 2 Polarisations - Options: • Single Co or Cross: HH or VV or HV or VH • Dual: HH + HV or VV + VH These are the different products : SLC (Single Look Complex): Amplitude and phase information is preserved. Data is in slant range. Georeferenced and aligned with the satellite track SGF (Path Image): Data is converted to ground range and may be multi-look processed. Scene is oriented in direction of orbit path. Georeferenced and aligned with the satellite track. SGX (Path Image Plus): Same as SGF except processed with refined pixel spacing as needed to fully encompass the image data bandwidths. Georeferenced and aligned with the satellite track SSG(Map Image): Image is geocorrected to a map projection. SPG (Precision Map Image): Image is geocorrected to a map projection. Ground control points (GCP) are used to improve positional accuracy. SCN(ScanSAR Narrow)/SCF(ScanSAR Wide) : ScanSAR Narrow/Wide beam mode product with original processing options and metadata fields (for backwards compatibility only). Georeferenced and aligned with the satellite track SCF (ScanSAR Fine): ScanSAR product equivalent to SGF with additional processing options and metadata fields. Georeferenced and aligned with the satellite track SCS(ScanSAR Sampled) : Same as SCF except with finer sampling. Georeferenced and aligned with the satellite track proprietary -Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ORNL_CLOUD STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ALL STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary +Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ORNL_CLOUD STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ALL STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ORNL_CLOUD STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary RapidEye.ESA.archive_7.0 RapidEye ESA archive ESA STAC Catalog 2009-02-22 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1965336937-ESA.umm_json The RapidEye ESA archive is a subset of the RapidEye Full archive that ESA collected over the years. The dataset regularly grows as ESA collects new RapidEye products. proprietary @@ -13625,8 +13625,8 @@ RemSensPOC_0 Remote-sensing-derived particulate organic carbon (POC) validation ResourceSat-1-IRS-P6.archive_6.0 ResourceSat-1/IRS-P6 full archive ESA STAC Catalog 2003-11-01 2013-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336942-ESA.umm_json ResourceSat-1 (also known as IRS-P6) archive products are available as below. • LISS-IV MN: Mono-Chromatic, Resolution 5 m, Coverage 70 km x 70 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2010, Global Archive 2003 - 2013 • LISS-III: Multi-spectral, Resolution 20 m, Coverage 140 km x 140 km, Radiometrically and Ortho (DN) corrected (ortho delivered without Band 5), Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 • AWiFS: Multi-spectral, Resolution 60 m, Coverage 370 km x 370 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 Note: • LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used. • For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘ResourceSat-1 archive’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf). proprietary ResourceSat-2.archive.and.tasking_6.0 ResourceSat-2 full archive and tasking ESA STAC Catalog 2011-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336944-ESA.umm_json ResourceSat-2 (also known as IRS-R2) archive and tasking products are available as below: Sensor: LISS-IV Type: Mono-Chromatic Resolution (m): 5 Coverage (km x km): 70 x 70 System or radiometrically corrected and Ortho corrected (DN) Neustralitz archive: 2014 Global archive: 2011 Sensor: LISS-III Type: Multi-spectral Resolution (m): 20 Coverage (km x km): 140 x 140 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Sensor: AWiFS Type: Multi-spectral Resolution (m): 60 Coverage (km x km): 370 x 370 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Note: • LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used.For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘ResourceSat-2 archive and tasking’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf). proprietary Respiration_622_1 Global Annual Soil Respiration Data (Raich and Schlesinger 1992) ORNL_CLOUD STAC Catalog 1963-01-01 1992-01-01 -156.4, -37.5, 146.5, 71.18 https://cmr.earthdata.nasa.gov/search/concepts/C2216863171-ORNL_CLOUD.umm_json This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates. proprietary -RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary +RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary River_Ice_Breakup_Freezeup_1697_1 ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016 ORNL_CLOUD STAC Catalog 1972-11-04 2016-11-30 -160.07, 62.9, -142.99, 66.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143403517-ORNL_CLOUD.umm_json This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue. proprietary @@ -13836,8 +13836,8 @@ SEAC4RS_Sondes_Data_1 SEAC4RS Radiosonde/Ozonesonde Data LARC_ASDC STAC Catalog SEAC4RS_TraceGas_AircraftInSitu_DC8_Data_1 SEAC4RS DC-8 Aircraft In-Situ Trace Gas Data LARC_ASDC STAC Catalog 2013-08-02 2013-09-24 -127, 19, -79, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2119341669-LARC_ASDC.umm_json SEAC4RS_TraceGas_AircraftInSitu_DC8_Data are in-situ trace gas data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete. Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest. proprietary SEAC4RS_TraceGas_AircraftInSitu_ER2_Data_1 SEAC4RS ER-2 Aircraft In-Situ Trace Gas Data LARC_ASDC STAC Catalog 2013-08-01 2013-09-23 -128, 15, -82, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2119341690-LARC_ASDC.umm_json SEAC4RS_TraceGas_AircraftInSitu_ER2_Data are in-situ trace gas data collected onboard the ER-2 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete. Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest. proprietary SEAC4RS_jValue_AircraftInSitu_DC8_Data_1 SEAC4RS DC-8 Aircraft In-Situ Photolysis Rate Data LARC_ASDC STAC Catalog 2013-08-02 2013-09-24 -127, 19, -80, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2119341667-LARC_ASDC.umm_json SEAC4RS_jValue_AircraftInSitu_DC8_Data are in-situ photolysis rate (j value) data collected onboard the DC8 aircraft during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. Data collection for this product is complete. Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest. proprietary -SEAGLIDER_GUAM_2019_V1 Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (Guam 2019-2020) POCLOUD STAC Catalog 2019-10-03 2020-01-15 143.63035, 13.39476, 144.613, 14.71229 https://cmr.earthdata.nasa.gov/search/concepts/C2151536874-POCLOUD.umm_json This dataset was produced by the Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (NASA grant NNX17AK07G) project, an investigation to develop tools and strategies to better measure the structure and variability of upper-ocean salinity in rain-dominated environments. From October 2019 to January 2020, three Seagliders were deployed near Guam (14°N 144°E). The Seaglider is an autonomous profiler measuring salinity and temperature in the upper ocean. The three gliders sampled in an adaptive formation to capture the patchiness of the rain and the corresponding oceanic response in real time. The location was chosen because of the likelihood of intense tropical rain events and the availability of a NEXRAD (S-band) rain radar at the Guam Airport. Spacing between gliders varies from 1 to 60 km. Data samples are gridded by profile and on regular depth bins from 0 to 1000 m. The time interval between profiles was about 3 hours, and they are typically about 1.5 km apart. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated). All Seaglider data files are in netCDF format with standards compliant metadata. The project was led by a team from the Applied Physics Laboratory at the University of Washington. proprietary SEAGLIDER_GUAM_2019_V1 Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (Guam 2019-2020) ALL STAC Catalog 2019-10-03 2020-01-15 143.63035, 13.39476, 144.613, 14.71229 https://cmr.earthdata.nasa.gov/search/concepts/C2151536874-POCLOUD.umm_json This dataset was produced by the Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (NASA grant NNX17AK07G) project, an investigation to develop tools and strategies to better measure the structure and variability of upper-ocean salinity in rain-dominated environments. From October 2019 to January 2020, three Seagliders were deployed near Guam (14°N 144°E). The Seaglider is an autonomous profiler measuring salinity and temperature in the upper ocean. The three gliders sampled in an adaptive formation to capture the patchiness of the rain and the corresponding oceanic response in real time. The location was chosen because of the likelihood of intense tropical rain events and the availability of a NEXRAD (S-band) rain radar at the Guam Airport. Spacing between gliders varies from 1 to 60 km. Data samples are gridded by profile and on regular depth bins from 0 to 1000 m. The time interval between profiles was about 3 hours, and they are typically about 1.5 km apart. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated). All Seaglider data files are in netCDF format with standards compliant metadata. The project was led by a team from the Applied Physics Laboratory at the University of Washington. proprietary +SEAGLIDER_GUAM_2019_V1 Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (Guam 2019-2020) POCLOUD STAC Catalog 2019-10-03 2020-01-15 143.63035, 13.39476, 144.613, 14.71229 https://cmr.earthdata.nasa.gov/search/concepts/C2151536874-POCLOUD.umm_json This dataset was produced by the Adaptive Sampling of Rain and Ocean Salinity from Autonomous Seagliders (NASA grant NNX17AK07G) project, an investigation to develop tools and strategies to better measure the structure and variability of upper-ocean salinity in rain-dominated environments. From October 2019 to January 2020, three Seagliders were deployed near Guam (14°N 144°E). The Seaglider is an autonomous profiler measuring salinity and temperature in the upper ocean. The three gliders sampled in an adaptive formation to capture the patchiness of the rain and the corresponding oceanic response in real time. The location was chosen because of the likelihood of intense tropical rain events and the availability of a NEXRAD (S-band) rain radar at the Guam Airport. Spacing between gliders varies from 1 to 60 km. Data samples are gridded by profile and on regular depth bins from 0 to 1000 m. The time interval between profiles was about 3 hours, and they are typically about 1.5 km apart. These profiles are available at Level 2 (basic gridding) and Level 3 (despiked and interpolated). All Seaglider data files are in netCDF format with standards compliant metadata. The project was led by a team from the Applied Physics Laboratory at the University of Washington. proprietary SEAHAWK_VALIDATION_0 Continuing the Mission: SeaHawk-1 Ocean Color CubeSat Nanosatellite OB_DAAC STAC Catalog 2022-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2639478178-OB_DAAC.umm_json Satellite validation work related to the SeaHawk Ocean Color CubeSat mission. This is a partnership between NASA, UNCW, UGA, ACC Clyde Space and Cloudland instruments. The project was funded by the Gordon and Betty Moore Foundation (grant number 11171) for years 2022-2025. proprietary SEASAT_SAR_L1_HDF5_1 SEASAT_SAR_LEVEL1_HDF5 ASF STAC Catalog 1978-07-04 1978-10-11 164.882812, 2.811371, 163.125, 77.235074 https://cmr.earthdata.nasa.gov/search/concepts/C1206500991-ASF.umm_json SEASAT Image Level 1 proprietary SEASAT_SAR_L1_TIFF_1 SEASAT_SAR_LEVEL1_GEOTIFF ASF STAC Catalog 1978-07-04 1978-10-11 164.882812, 2.811371, 163.125, 77.235074 https://cmr.earthdata.nasa.gov/search/concepts/C1206500826-ASF.umm_json SEASAT Image GeoTIFF proprietary @@ -13957,8 +13957,8 @@ SIR-C_PRECISION Spaceborne Imaging Radar-C Precision USGS_LTA STAC Catalog 1994- SIRSN3L1_001 SIRS/Nimbus-3 Level 1 Radiance Data V001 (SIRSN3L1) at GES DISC GES_DISC STAC Catalog 1969-04-14 1970-06-19 -180, -80.15, 180, 80.15 https://cmr.earthdata.nasa.gov/search/concepts/C1622768257-GES_DISC.umm_json SIRSN3L1 is the Nimbus-3 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day. The Nimbus-3 SIRS only viewed the nadir of the subsatellite track. Spatial coverage is near global from about latitude -80 to +80 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service. This product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A). proprietary SIRSN4L1_001 SIRS/Nimbus-4 Level 1 Radiance Data V001 (SIRSN4L1) at GES DISC GES_DISC STAC Catalog 1970-04-08 1971-04-08 -180, -85, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1622768259-GES_DISC.umm_json SIRSN4L1 is the Nimbus-4 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day. The Nimbus-4 SIRS used a scan mirror to observe 12.5 deg to either side of the subsatellite track. Spatial coverage is near global from latitude -85 to +85 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service. This product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A). proprietary SISTER_Workflow_V004_2335_4 SISTER: Experimental Workflows, Product Generation Environment, and Sample Data, V004 ORNL_CLOUD STAC Catalog 2011-05-13 2018-01-26 -158.05, 21.2, -107.96, 39.08 https://cmr.earthdata.nasa.gov/search/concepts/C3114843226-ORNL_CLOUD.umm_json The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission. proprietary -SIZEX-89-SAR Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea SCIOPS STAC Catalog 1989-02-15 1989-02-27 15, 74, 25, 77 https://cmr.earthdata.nasa.gov/search/concepts/C1214584391-SCIOPS.umm_json SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction. proprietary SIZEX-89-SAR Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea ALL STAC Catalog 1989-02-15 1989-02-27 15, 74, 25, 77 https://cmr.earthdata.nasa.gov/search/concepts/C1214584391-SCIOPS.umm_json SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction. proprietary +SIZEX-89-SAR Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea SCIOPS STAC Catalog 1989-02-15 1989-02-27 15, 74, 25, 77 https://cmr.earthdata.nasa.gov/search/concepts/C1214584391-SCIOPS.umm_json SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction. proprietary SLAR Side Looking Airborne Radar (SLAR) Imagery USGS_LTA STAC Catalog 1980-07-18 1993-11-30 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1220566112-USGS_LTA.umm_json Side-Looking Airborne Radar (SLAR) imagery is available from the U.S. Geological Survey (USGS) for selected project areas in the conterminous United States and Alaska. Data are X-band synthetic aperture radar (horizontally transmitted, horizontally received) with the exception of some test sites. Coverage was contracted on a yearly basis. The USGS SLAR images most often consist of contact strip images and 1:250,000-scale, map-controlled mosaics. Greater than half of the available SLAR image strips are distributed on 8-mm cassettes, while some image strips are distributed on CD-ROM. In addition, ancillary products such as indexes (on paper, film, or microfiche) and custom photographic products may also be available. Due to the geographically non-searchable nature of the SLAR inventory, customer assistance may be obtained to determine availability of SLAR data over the user's area of interest. Customer knowledge of USGS 1:250,000-scale map names is beneficial in expediting orders. A scale of 1:50,000 only applies to Alaska coverage. proprietary SLOPE_GPP_CONUS_1786_1 MODIS-based GPP, PAR, fC4, and SANIRv estimates from SLOPE for CONUS, 2000-2019 ORNL_CLOUD STAC Catalog 2000-01-01 2020-01-01 -155.57, 19.99, -52.22, 50.01 https://cmr.earthdata.nasa.gov/search/concepts/C2266194621-ORNL_CLOUD.umm_json This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1. proprietary SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0 JPL SMAP Level 2B Near Real-time CAP Sea Surface Salinity V5.0 Validated Dataset (2 hour latency) POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2681262364-POCLOUD.umm_json The SMAP-SSS V5.0, level 2B (NRT CAP) dataset produced by the Jet Propulsion Laboratory Combined Active-Passive (CAP) project , is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) and extreme winds, derived from the NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015. This mission, initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.

The dataset includes derived SMAP SSS, SSS uncertainty, wind speed and direction data for extreme winds, as well as brightness temperatures for each radiometer polarization. Furthermore, it contains ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. This broad range of parameters stems from the observatory's version 5.0 (V5) CAP retrieval algorithm, initially developed for the Aquarius/SAC-D mission and subsequently extended to SMAP. Datafrom April 1, 2015 to present, is available with a latency of about 6 hours. The observations are global, provided on a 25km swath grid with an approximate spatial resolution of 60 km. Each data file covers one 98-minute orbit, with 15 files generated per day. The data are based on the near-real-time SMAP V5 Level-1 Brightness Temperatures (TB) and benefits from an enhanced calibration methodology, which improves the absolute radiometric calibration and minimizes biases between ascending and descending passes. These improvements also enrich the applicability of SMAP Level-1 data for other uses, such as further sea surface salinity and wind assessments. Due to a malfunction of the SMAP scatterometer on July 7, 2015, collocated wind speed data has been utilized for the necessary surface roughness correction for salinity retrieval.

This JPL SMAP-SSS V5.0 dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature, it achieves global coverage in approximately three days , enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth’s hydrological cycle, ocean circulation, and climate change. proprietary @@ -13999,16 +13999,16 @@ SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3 RSS SMAP Level 3 Sea Surface Salinity Stand SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0 RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.0 Validated Dataset POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208416221-POCLOUD.umm_json The version 5.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. proprietary SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0 RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2832226365-POCLOUD.umm_json The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.

The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP L3 monthly product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format and is averaged over one-month time intervals with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km. proprietary SMERGE_RZSM0_40CM_2.0 Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 (SMERGE_RZSM0_40CM) at GES DISC GES_DISC STAC Catalog 1979-01-02 2019-05-10 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1569839798-GES_DISC.umm_json Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 is a multi-decadal root-zone soil moisture product. Smerge is developed by merging the North American Land Data Assimilation System (NLDAS) land surface model output with surface satellite retrievals from the European Space Agency Climate Change Initiative. The data have a 0.125 degree resolution at a daily time-step, covering the entire continental United States and spanning nearly four decades (January 1979 to May 2019). This data product contains root-zone soil moisture of 0 - 40 cm layer, Climate Change Initiative (CCI) derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag. This data product is the recommended replacement for the AMSR-E/Aqua root zone soil moisture L3 1 day 25 km x 25 km descending and 2-Layer Palmer Water Balance Model V001 product (LPRM_AMSRE_D_RZSM3), which will be removed from archive on June 27, 2022. Smerge provides a better root zone soil moisture estimation because it has higher data quality and longer temporal coverage. proprietary -SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK SCIOPS STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK ALL STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary +SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK SCIOPS STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic SCIOPS STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic ALL STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary -SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites SCIOPS STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites ALL STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary -SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track ALL STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary +SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites SCIOPS STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track SCIOPS STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary -SMHI_IPY_ALIS ALIS, Auroral Large Imaging System SCIOPS STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50×50 km. Each station is equipped with an imager having a high-resolution monochrome 1024×1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary +SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track ALL STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary SMHI_IPY_ALIS ALIS, Auroral Large Imaging System ALL STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50×50 km. Each station is equipped with an imager having a high-resolution monochrome 1024×1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary +SMHI_IPY_ALIS ALIS, Auroral Large Imaging System SCIOPS STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50×50 km. Each station is equipped with an imager having a high-resolution monochrome 1024×1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary SMMRN7IM_001 SMMR/Nimbus-7 Color Images V001 (SMMRN7IM) at GES DISC GES_DISC STAC Catalog 1978-10-30 1983-11-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1616514843-GES_DISC.umm_json "SMMRN7IM is the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Color Image data product scanned from 17"" x 15"" color prints and saved as JPEG-2000 files. Sea surface temperature, sea surface winds, total atmospheric water vapor over oceans, total atmospheric liquid water over oceans, including brightness temperature parameters are available as both 6-day composites and 1-month averages between 64 south and north latitudes in Mercator projection. Sea ice fraction, sea ice and ocean surface temperature, sea ice concentration, including brightness temperature parameters are available as both 3-day and 1-month averages in north and south polar stereographic projections. Images may contain between one and three measured parameters. These SMMR images are available from 30 October 1978 through 2 November 1983. The principal investigator for the SMMR experiment was Dr. Per Gloersen from NASA GSFC. These products were previously available from the NSSDC under the ids ESAD-00007, ESAD-00056, ESAD-00123, ESAD-00124, ESAD-00162, ESAD-00172, ESAD-00173, ESAD-00176 ESAD-00177, ESAD-00178, and ESAD-00241 (old ids 78-098A-08I-S)." proprietary SMMR_ALW_PRABHAKARA_1 Scanning Multichannel Microwave Radiometer (SMMR) Monthly Mean Atmospheric Liquid Water (ALW) By Prabhakara LARC_ASDC STAC Catalog 1979-02-01 1984-05-31 180, -48, -180, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1336972900-LARC_ASDC.umm_json SMMR_ALW_PRABHAKARA data are Special Multichannel Microwave Radiometer (SMMR) Monthly Mean Atmospheric Liquid Water (ALW) data by Prabhakara.The Prabhakara Scanning Multichannel Microwave Radiometer (SMMR) Atmospheric Liquid Water (ALW) files were generated by Dr. Prabhakara Cuddapah at the Goddard Space Flight Center (GSFC) using SMMR Antenna Temperatures. A discussion of the SMMR Antenna Temperatures is available from the Langley Distributed Active Archive Center (DAAC). Each ALW file contains one month of 3 degree by 5 degree gridded mean liquid water. Each element of data is in units of mg/cm2. The data spans the period from February 1979 to May 1984. proprietary SMMR_IWV_PRABHAKARA_1 Scanning Multichannel Microwave Radiometer (SMMR) Monthly Mean Integrated Water Vapor (IWV) By Prabhakara LARC_ASDC STAC Catalog 1979-01-01 1983-09-30 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1336972882-LARC_ASDC.umm_json SMMR_IWV_PRABHAKARA data are Special Multichannel Microwave Radiometer (SMMR) Monthly Mean Integrated Water Vapor (IWV) data by Prabhakara.The Scanning Multichannel Microwave Radiometer (SMMR) Prabhakara integrated atmospheric water vapor (IWV) files were generated by Dr. Prabhakara Cuddapah at the Goddard Space Flight Center (GSFC) using SMMR Antenna Temperatures. A discussion of the SMMR Antenna Temperatures is available from the Langley Research Center Distributed Active Archive Center (DAAC). Each IWV file contains one month of 3 degree by 5 degree gridded mean water vapor. A scale factor of 0.1 must be applied to convert the data into units of g/cm2. The data spans the period from October 1979 to September 1983. proprietary @@ -14281,8 +14281,8 @@ SOAR1999WMB Aerogeophysical survey of western Marie Byrd Land, Antarctica ALL ST SOAR1999WMB Aerogeophysical survey of western Marie Byrd Land, Antarctica SCIOPS STAC Catalog 1970-01-01 -158, -80.5, -136, -75.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214611929-SCIOPS.umm_json An aerogeophysical survey of the western Marie Byrd Land region of Antarctica was flown in Dec. 1998-Jan. 1999, measuring surface and base of ice elevation by radar and strength of magnetic and gravity fields. The coverage area measured about 460 by 360 km, long dimension oriented NE, and included the Shirase Coast of the eastern Ross Ice Shelf, much of the Edward VII Peninsula, the Sulzberger Ice Shelf, and the Ford Ranges. Track spacing was either 5.3 or 10.6 km over most of the area. The 60 Mhz radar system usually provided good images of the base of the ice for thicknesses less than 1 km but rarely imaged thicknesses greater than 1.5 km. Determination of gravity anomalies required corrections for acceleration of the aircraft as measured by differential carrier-phase GPS navigation, filtering to remove wavelengths less than 10 km, which are commonly contaminated by aircraft motion, and editing of occasional spikes. The gravity anomalies allow estimation of bed topography under floating ice and under ice too thick for radar imaging. Magnetic anomaly reduction includes a correction for daily variation as measured at the base camp. Data formats for all observations include files for original flight profiles and grids of edited data at 1.06 km node spacing. proprietary SOAR1_UTIG Airborne Geophysical Data acquired by the NSF Support Office for Aerogeophysical Research (SOAR), University of Texas Institute for Geophysics, 1994-2000. ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214611637-SCIOPS.umm_json This dataset consists of airborne geophysical data collected between 1994 and 2000 by the National Science Foundation's Support Office for Aerogeophysical Research (SOAR) at the University of Texas Institute for Geophysics. Meaurements were made using a laser altimeter, a radar echo sounder, a gravimeter, and a magnetometer. Positioning was accomplished with kinematic, differential carrier-phase GPS. Multiple areas within Antarctica were covered, including both grid and line surveys. Some areas have reduced data products (i.e., surface and bed elevations, ice thickness, gravity and magnetic field anomalies). proprietary SOAR1_UTIG Airborne Geophysical Data acquired by the NSF Support Office for Aerogeophysical Research (SOAR), University of Texas Institute for Geophysics, 1994-2000. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214611637-SCIOPS.umm_json This dataset consists of airborne geophysical data collected between 1994 and 2000 by the National Science Foundation's Support Office for Aerogeophysical Research (SOAR) at the University of Texas Institute for Geophysics. Meaurements were made using a laser altimeter, a radar echo sounder, a gravimeter, and a magnetometer. Positioning was accomplished with kinematic, differential carrier-phase GPS. Multiple areas within Antarctica were covered, including both grid and line surveys. Some areas have reduced data products (i.e., surface and bed elevations, ice thickness, gravity and magnetic field anomalies). proprietary -SOAR2_UTIG Airborne Geophysical Data acquired and reduced by The University of Texas Institute for Geophysics, 2000-2001. ALL STAC Catalog 1970-01-01 95, -82, 160, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214614557-SCIOPS.umm_json This dataset consists of airborne geophysical data collected during 2000/01 by researchers at The University of Texas Institute for Geophysics. Meaurements were made using a laser altimeter, a radar echo sounder, a gravimeter, and a magnetometer. Positioning was accomplished with kinematic, differential carrier-phase GPS. The data, reduced by UTIG, includes: surface and bed elevations, ice thickness, gravity and magnetic field anomalies. Two distinct surveys in East Antarctica are covered: a grid-based survey of subglacial Lake Vostok and its environs, and a 1200 km line-based transect extending from the Transantarctic Mountains (near 160E, 77S) toward Dome A (near 95E, 82S). proprietary SOAR2_UTIG Airborne Geophysical Data acquired and reduced by The University of Texas Institute for Geophysics, 2000-2001. SCIOPS STAC Catalog 1970-01-01 95, -82, 160, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214614557-SCIOPS.umm_json This dataset consists of airborne geophysical data collected during 2000/01 by researchers at The University of Texas Institute for Geophysics. Meaurements were made using a laser altimeter, a radar echo sounder, a gravimeter, and a magnetometer. Positioning was accomplished with kinematic, differential carrier-phase GPS. The data, reduced by UTIG, includes: surface and bed elevations, ice thickness, gravity and magnetic field anomalies. Two distinct surveys in East Antarctica are covered: a grid-based survey of subglacial Lake Vostok and its environs, and a 1200 km line-based transect extending from the Transantarctic Mountains (near 160E, 77S) toward Dome A (near 95E, 82S). proprietary +SOAR2_UTIG Airborne Geophysical Data acquired and reduced by The University of Texas Institute for Geophysics, 2000-2001. ALL STAC Catalog 1970-01-01 95, -82, 160, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214614557-SCIOPS.umm_json This dataset consists of airborne geophysical data collected during 2000/01 by researchers at The University of Texas Institute for Geophysics. Meaurements were made using a laser altimeter, a radar echo sounder, a gravimeter, and a magnetometer. Positioning was accomplished with kinematic, differential carrier-phase GPS. The data, reduced by UTIG, includes: surface and bed elevations, ice thickness, gravity and magnetic field anomalies. Two distinct surveys in East Antarctica are covered: a grid-based survey of subglacial Lake Vostok and its environs, and a 1200 km line-based transect extending from the Transantarctic Mountains (near 160E, 77S) toward Dome A (near 95E, 82S). proprietary SOCCOM_0 Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM) OB_DAAC STAC Catalog 2014-12-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360663-OB_DAAC.umm_json SOCCOM (Southern Ocean Carbon and Climate Observations and Modeling project) is a NSF project sampling the Southern Ocean and its influence on climate.Additional Data LinksCLIVAR P16S_2014 Pigment AnalysisCLIVAR P16S_2014 POC dataCLIVAR P16S_2014 Supporting Documentation proprietary SOC_3M_Maps_NE_TidalWetlands_1905_1 Soil Organic Carbon Distributions in Tidal Wetlands of the Northeastern USA ORNL_CLOUD STAC Catalog 1998-01-01 2018-12-31 -76.35, 37.08, -66.94, 45.26 https://cmr.earthdata.nasa.gov/search/concepts/C2515912673-ORNL_CLOUD.umm_json This dataset provides estimates of soil organic carbon (SOC) in tidal wetlands for the northeastern United States. The data cover the period 1998-2018. Northeastern U.S. tidal wetlands and bordering areas were harmonized from government agencies [U.S. Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS), National Cooperative Soil Survey (NCSS), USDA-NRCS - Rapid Carbon Assessment (RaCA), U.S. Environmental Protection Agency - National Wetland Condition and Assessment (EPA-NWCA)] and published studies. Point data for carbon stocks (in kg m-2) at four soil depths (0-5, 0-30, 0-100, and 0-200 cm) are included. SOC for the four depths was predicted for eight regional zones using regression models driven by environmental covariates. Two methods were used to estimate parameters for these models, a Random Forest (RF) Ranger method and a Quantile Regression Forest (QRF) model. The distribution of SOC was predicted for tidal wetland cover types mapped by Correll et al. (2019). Predictions and uncertainties are available at a 3 m resolution. proprietary SOC_Stocks_Great_Plains_1603_1 Stocks of Surface Soil Organic Carbon Fractions, Great Plains Region, USA, 2007-2010 ORNL_CLOUD STAC Catalog 2007-05-01 2010-10-01 -111.93, 31.22, -94.43, 45.83 https://cmr.earthdata.nasa.gov/search/concepts/C2517662316-ORNL_CLOUD.umm_json This dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included. The estimates were derived using a best random forest regression model and cover the period 2007-05-01 to 2010-10-01. proprietary @@ -14396,9 +14396,9 @@ SPL1A_RO_METADATA_003_3 SMAP_L1A_RADAR_RECEIVE_ONLY_METADATA_V003 ASF STAC Catal SPL1A_RO_QA_001_1 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243168733-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 1 proprietary SPL1A_RO_QA_002_2 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243168866-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 2 proprietary SPL1A_RO_QA_003_3 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243124139-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 3 proprietary -SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary -SPL1BTB_NRT_105 Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105 NSIDC_ECS STAC Catalog 2025-01-09 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20)." proprietary +SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary +SPL1BTB_NRT_105 Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105 NSIDC_ECS STAC Catalog 2025-01-10 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20)." proprietary SPL1B_SO_LoRes_001_1 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473308-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product proprietary SPL1B_SO_LoRes_002_2 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243253631-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product Version 2 proprietary SPL1B_SO_LoRes_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243133445-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product Version 3 proprietary @@ -14408,8 +14408,8 @@ SPL1B_SO_LoRes_METADATA_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_METADATA_V003 ASF ST SPL1B_SO_LoRes_QA_001_1 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214474243-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info proprietary SPL1B_SO_LoRes_QA_002_2 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243216659-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 2 proprietary SPL1B_SO_LoRes_QA_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243129847-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 3 proprietary -SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary +SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary SPL1C_S0_HiRes_001_1 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473367-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product proprietary @@ -14423,21 +14423,21 @@ SPL1C_S0_HiRes_QA_002_2 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V002 ASF STAC Catalog SPL1C_S0_HiRes_QA_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243140611-ASF.umm_json SMAP Level 1C Sigma Naught High Res Data Quality Info Version 3 proprietary SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary -SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary +SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary -SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary +SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary -SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2025-01-09 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary +SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary +SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2025-01-10 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary -SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary +SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary +SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary @@ -14450,10 +14450,10 @@ SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary -SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary -SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary +SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary +SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPOT-6.and.7.ESA.archive_9.0 SPOT-6 and 7 ESA archive ESA STAC Catalog 2012-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336951-ESA.umm_json The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products. SPOT 6 and 7 Primary and Ortho products can be available in the following modes: Panchromatic image at 1.5m resolution Pansharpened colour image at 1.5m resolution Multispectral image in 4 spectral bands at 6m resolution Bundle (1.5m panchromatic image + 6m multispectral image) Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/SPOT6-7 available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided. proprietary SPOT1-5_8.0 SPOT1-5 ESA archive ESA STAC Catalog 1986-04-01 2015-09-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1532648155-ESA.umm_json The ESA SPOT1-5 collection is a dataset of SPOT-1 to 5 Panchromatic and Multispectral products that ESA collected over the years. The HRV(IR) sensor onboard SPOT 1-4 provides data at 10 m spatial resolution Panchromatic mode (-1 band) and 20 m (Multispectral mode -3 or 4 bands). The HRG sensor on board of SPOT-5 provides spatial resolution of the imagery to < 3 m in the panchromatic band and to 10 m in the multispectral mode (3 bands). The SWIR band imagery remains at 20 m. The dataset mainly focuses on European and African sites but some American, Asian and Greenland areas are also covered. proprietary SPOT4-5_Take5.ESAarchive_7.0 SPOT 4-5 Take5 ESA archive ESA STAC Catalog 2013-01-31 2015-09-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336953-ESA.umm_json At the end of SPOT-4 life, the Take5 experiment was launched and the satellite was moved to a lower orbit to obtain a 5 day repeat cycle, same repetition of Sentinel-2. Thanks to this orbit, from 1st of Feb to 19th of June 2013 a time series of images acquired every 5 days with constant angle and over 45 different sites were observed. In analogy to the previous SPOT-4 Take-5 experiment, also SPOT-5 was placed in a 5 days cycle orbit and 145 selected sites were acquired every 5 days under constant angles from 8th of April to 31st of August 2015. With a resolution of 10 m, the following processing levels are available: Level 1A: reflectance at the top of atmosphere (TOA), not orthorectified products Level 1C: data orthorectified reflectance at the top of atmosphere (TOA) Level 2A: data orthorectified surface reflectance after atmospheric correction (BOA), along with clouds mask and their shadow, and mask of water and snow. proprietary @@ -14500,8 +14500,8 @@ SPURS2_WAVEGLIDER_1.0 SPURS-2 Waveglider data for the E. Tropical Pacific field SPURS2_XBAND_1.0 SPURS-2 shipboard X-band radar backscatter data for the E. Tropical Pacific field campaign POCLOUD STAC Catalog 2017-10-21 2017-11-13 -129.131, 8.927, -122.151, 10.355 https://cmr.earthdata.nasa.gov/search/concepts/C2781659132-POCLOUD.umm_json The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. proprietary SPURS2_XBAND_IMG_1.0 SPURS-2 shipboard X-band radar backscatter images for the 2016 E. Tropical Pacific field campaign POCLOUD STAC Catalog 2016-08-31 2016-09-22 -129.131, 8.927, -122.151, 10.355 https://cmr.earthdata.nasa.gov/search/concepts/C2931233351-POCLOUD.umm_json The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. proprietary SPURS2_XBT_1.0 SPURS-2 research vessel Expendable Bathythermograph (XBT) profile data for E. Tropical Pacific R/V Revelle cruises POCLOUD STAC Catalog 2016-08-14 2017-11-15 -157.88, 5.06, -118.32, 21.26 https://cmr.earthdata.nasa.gov/search/concepts/C2491772372-POCLOUD.umm_json The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Expendable bathythermograph (XBT) casts were undertaken at stations during both of the SPURS-2 R/V Revelle cruises. Launched off the side of the ship, XBT probes provide vertical profile measurements of the water column at fixed locations. There were a total of 25 and 11 XBT deployments made during the first and second R/V Revelle cruises respectively. There is one XBT data file per cruise, each containing the temperature profile data from all instrument deployments undertaken during that cruise. proprietary -SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ALL STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ORNL_CLOUD STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary +SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ALL STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary SRE4_SAB_gammaclones_1 Clone library using primers for gammaproteobacteria from an SAB treatment in the SRE4 experiment AU_AADC STAC Catalog 2002-12-01 2002-12-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313841-AU_AADC.umm_json A clone library was created from DNA extracted from an SAB-treated sample from the SRE4 in situ biodegradation experiment. The clone libary was created using one universal primer and one primer designed to be specific for the gammaproteobacteria. Sequences of approximately 600 bp were obtained. The samples used in this experiment were collected from O'Brien Bay, near Casey Station in the Windmill Islands. Gammaproteobacteria clone library Clone library created from SRE4 T2 SAB sample using primers 10F (GAG TTT GAT CCT GGC TCA G ) and GAMR (GGT AAG GTT CTT CGC GTT GCA T). Clones sequenced on a CEQ8000 Genetic Analysis system (Beckman-Coulter) and alignments were done in BioEdit v 5.0.9. Text file SRE4gammaclonesalign is a text version of BioEdit file SRE4gammaclones. This work was completed as part of ASAC project 2672 (ASAC_2672). proprietary SRE4_desulfobaculaDGGE_1 Band pattern data from Desulfobacula-group specific DGGE for the SRE4 experiment AU_AADC STAC Catalog 2001-10-25 2003-03-30 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313816-AU_AADC.umm_json Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65. Samples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Desulfobacula group. Samples A,B,C,D,E,F,G,H,I are all initial samples collected different days Samples beginning T0 are predeployment samples, the next number refers to the batch. Samples beginning T2 are 1 year samples with: C = control S = SAB L = lubricant U = used lubricant B = biodegradable lubricant PCR conditions were as follows: Primers: 764F: ACAATGGTAAATGAGGGCA 1392RC: CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCCACGGGCGG TGTGTAC 50 ul (micro litre) reactions with Advantage II taq (Clontech) following manufacturer's recommendations with 20 pmol (pico mol) each primer and 20 ng (nano gram) template DNA. Cycling: 94C 5 minutes 10 cycles of: 94C 1 minutes 65C 1 minutes (-1C per cycle) 72C 2 minutes 20 cycles of: 94C 1 minutes 55C 1 minutes 72C 2 minutes 72C 30 minutes DGGE carried out using the D-Code system (BioRad). Gel: 8% acrylamide 30 - 65% denaturant with 2 cm stacking gel (15% acrylamide) 1 x TAE, 60 degrees C, 70V 16 hours The gels were pre-run for 20 minutes then half reaction volume was loaded and the lanes flushed out after 15 minutes. Gels were stained with SYBRGold. Images were captured using Storm Phosphorimager and ImageQuant v5.2 software(.gel files). Samples were only compared within a gel. Band pattern results are in the file desulfodgge.xls. For each comparison made there is a separate sheet in this file (see below). The first column in each sheet is the band position (or band name) and the remaining columns are samples with the first row being the sample name. '0' '1' indicate the band was 'absent' or 'present'. Comparison Image files (.gel and .tif) results sheets Background variation 140704f; 140704b 140704f and 140704b predeployment batches 180604f; 180406b 180604f and 180604b effect of setup 150704 150704 immediate effect of oil 250604f; 250604b 250604f and 250604b 1 year samples (T2) 040804f; 040804b 040804f and 040804b This work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201). proprietary SRE4_gammaproteobacteriaDGGE_1 Band pattern data from Gammaproteobacteria-group specific DGGE AU_AADC STAC Catalog 2001-10-25 2003-03-30 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313817-AU_AADC.umm_json Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65. Samples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Gammaproteobacteria. Samples used were from Time2 (1 year) Initial: T-1C; T-1E Control: T2C SAB treatment: T2S PCR conditions: Primers: GAMFC: CGC CCG CCG CGC CCC GCG CCC GGC CCG CCG CCC CCG CCC GGG TTA ATC GGA ATT ACT GG GAMR: GGT AAG GTT CTT CGC GTT GCA T 50 ul (micro litre) reactions with HotStar (qiagen) mix, 5ul Q solution, 10 pmol (pico mol) each primer and 20 ng (nano gram) template DNA cycling: 94C 15 minutes 35 cycles of: 94C 1 minutes 55C 1 minutes 72C 1 minutes 72C 20 minutes DGGE was performed using D-Code system (BioRad). Gel: 8% acryloamide, 30 - 65% denaturant with 2 cm stacking gel 1 x TAE, 60 degrees C, 80V 16 hours Gel was pre-run for 20 minutes and lanes were flushed out after 15 minutes. Gel was stained with Sybrgold. Image captured using Storm Phosphorimager and ImageQuant v5.2 software (.gel files). The image files are called 151105#2.gel and 151105.tif Band pattern results are in gammadgge.xls. The first column is the band position (or band name) and the remaining columns are samples with the first row being the sample name. The numbers indicates how many times the band appeared for that sample out of 2 DGGE runs. This work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201). proprietary @@ -14525,8 +14525,8 @@ SRTMSWBD_003 NASA Shuttle Radar Topography Mission Water Body Data Shapefiles & SSBUVIRR_008 Shuttle SBUV (SSBUV) Solar Spectral Irradiance V008 (SSBUVIRR) at GES DISC GES_DISC STAC Catalog 1989-10-19 1996-01-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1273652226-GES_DISC.umm_json The Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites. Data are available in an ASCII text format. UV irradiance data are available for the following days from the eight missions: Flight #1: 1989 October 19, 20, 21 Flight #2: 1990 October 7, 8, 9 Flight #3: 1991 August 3, 4, 5, 6 Flight #4: 1992 March 29, 30 Flight #5: 1993 April 9, 11, 13, 15, 16 Flight #6: 1994 March 14, 15, 17 Flight #7: 1994 November 5, 7, 10, 13 Flight #8: 1996 January 12, 16, 18 The Shuttle SBUV (SSBUV) instrument measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. These data demonstrate the evolution of short-term solar UV activity during solar cycle 22. proprietary SSBUVO3_008 Shuttle SBUV (SSBUV) Level 2 Ozone Profile and Total Column, Aerosol Index, and UV-Reflectivity V008 (SSBUVO3) at GES DISC GES_DISC STAC Catalog 1989-10-19 1996-01-18 -180, -57, 180, 58 https://cmr.earthdata.nasa.gov/search/concepts/C1273652228-GES_DISC.umm_json The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods: Flight #1: 1989 October 19, 20, 21. Flight #2: 1990 October 7, 8, 9. Flight #3: 1991 August 3, 4, 5, 6. Flight #4: 1992 March 29, 31. Flight #5: 1993 April 9, 11, 13, 15, 16. Flight #6: 1994 March 14, 15, 17. Flight #7: 1994 November 5, 7, 10, 13. Flight #8: 1996 January 12, 16, 18. SSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb. proprietary SSDP_HAZARD_EARTHQUAKE Earthquakes and Planning for Ground Rupture Hazards CEOS_EXTRA STAC Catalog 1970-01-01 -116, 33, -115.5, 33.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553786-CEOS_EXTRA.umm_json Detailed maps bring a greater resolution to the number and locations of active faults. Preparing maps at a higher resolution requires extensive field study, and with a GIS, information, such as tract and parcel data, utility corridors, and flood hazard zones, can be incorporated to help decision makers in locating remediation facilities. After the Sylmar earthquake in 1972, building codes were strengthened, and the Alquist-Priolo Special Studies Zone Act was passed. Its purpose is to mitigate the hazard of fault rupture by prohibiting the location of most human occupancy structures across the traces of active faults. Earthquake fault zones are regulatory zones that encompass surface traces of active faults with a potential for future surface fault rupture. The zones are generally established about 500 feet on either side of the surface trace of active faults. Active faults and strips of state-mandated zoning along faults (Alquist-Priolo zones) riddle the Salton Sea Basin. The primary fault, the San Andreas, steps from the northeast side of the Salton Sea across the southern end, along a series of poorly understood faults, to the Brawley and Imperial fault systems. This stepover region has not had a historic ground-rupturing earthquake. Alquist-Priolo zones could not be defined because the faults are not well-located. Faults parallel to, and splaying from, the San Andreas are also capable of major earthquakes. Initial plans for remediation facilities take into account the generalized information (at 1:750,000 scale) on active faults, and the fault maps do not provide information on strong ground shaking. The shaking can damage facilities that lie far from an earthquake epicenter and far from active faults. Information on near-surface materials is required to estimate the ground-shaking hazards. proprietary -SSEC-AMRC-AIRCRAFT Aircraft meteorological reports over Antarctica SCIOPS STAC Catalog 2004-04-04 2015-08-31 -180, -90, 180, 0 https://cmr.earthdata.nasa.gov/search/concepts/C1214605495-SCIOPS.umm_json The AMRC has been archiving the Aircraft data since the 2000's in the ftp archive. Products used to be made in real-time, but data collection has ended starting 31 August, 2015. proprietary SSEC-AMRC-AIRCRAFT Aircraft meteorological reports over Antarctica ALL STAC Catalog 2004-04-04 2015-08-31 -180, -90, 180, 0 https://cmr.earthdata.nasa.gov/search/concepts/C1214605495-SCIOPS.umm_json The AMRC has been archiving the Aircraft data since the 2000's in the ftp archive. Products used to be made in real-time, but data collection has ended starting 31 August, 2015. proprietary +SSEC-AMRC-AIRCRAFT Aircraft meteorological reports over Antarctica SCIOPS STAC Catalog 2004-04-04 2015-08-31 -180, -90, 180, 0 https://cmr.earthdata.nasa.gov/search/concepts/C1214605495-SCIOPS.umm_json The AMRC has been archiving the Aircraft data since the 2000's in the ftp archive. Products used to be made in real-time, but data collection has ended starting 31 August, 2015. proprietary SSFR_irradiance_841_1 SAFARI 2000 Solar Spectral Flux Radiometer Data, Southern Africa, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-17 2000-09-16 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788411266-ORNL_CLOUD.umm_json The Solar Spectral Flux Radiometer (SSFR) was deployed on the University of Washington CV-580 during the dry season component of the Southern African Regional Science Initiative, August 1 - September 20, 2000. The SSFR made simultaneous measurements of both downwelling and upwelling net solar spectral irradiance at varying flight levels. Data have been provided for twenty flights in netcdf format for the period August 17 - September 16, 2000.For a complete detailed guide to the extensive measurements obtained aboard the UW Convair-580 aircraft in support of SAFARI 2000, see the UW Technical Report for the SAFARI 2000 Project. proprietary STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data_1 STAQS JSC GV GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator Data LARC_CLOUD STAC Catalog 2023-06-26 2023-08-17 -120.3, 33.36, -72, 44.56 https://cmr.earthdata.nasa.gov/search/concepts/C2862468660-LARC_CLOUD.umm_json STAQS_AircraftRemoteSensing_JSC-GV_GCAS_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models. proprietary STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data_1 STAQS JSC GV High Spectral Resolution Lidar-2 Data LARC_CLOUD STAC Catalog 2023-06-24 2023-08-16 -119.8, 29.25, -72.1, 44.22 https://cmr.earthdata.nasa.gov/search/concepts/C2862479332-LARC_CLOUD.umm_json STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the High Spectral Resolution Lidar-2 (HSRL-2) as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete. Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models. proprietary @@ -14714,8 +14714,8 @@ Saskatchewan_Soils_125m_SSA_1346_2 BOREAS Agriculture Canada Central Saskatchewa Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ORNL_CLOUD STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ALL STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary SatelliteDerived_Forest_Mexico_2320_1 Satellite-Derived Forest Extent Likelihood Map for Mexico ORNL_CLOUD STAC Catalog 2010-01-01 2020-12-31 -120.31, 12.48, -84.29, 34.51 https://cmr.earthdata.nasa.gov/search/concepts/C2905454214-ORNL_CLOUD.umm_json This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas. proprietary -Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions SCIOPS STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions ALL STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary +Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions SCIOPS STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary SciSat-1.Ace.FTS.and.Maestro_4.0 SciSat-1: ACE-FTS and MAESTRO ESA STAC Catalog 2003-08-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336954-ESA.umm_json SCISAT-1 data aim at monitoring and analysing the chemical processes that control the distribution of ozone in the upper troposphere and stratosphere. It provides acquisitions from the 2 instruments MAESTRO and ACE-FTS. • MAESTRO: Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation. Dual-channel optical spectrometer in the spectral region of 285-1030 nm. The objective is to measure ozone, nitrogen dioxide and aerosol/cloud extinction (solar occultation measurements of atmospheric attenuation during satellite sunrise and sunset with the primary objective of assessing the stratospheric ozone budget). Solar occultation spectra are being used for retrieving vertical profiles of temperature and pressure, aerosols, and trace gases (O3, NO2, H2O, OClO, and BrO) involved in middle atmosphere ozone distribution. The use of two overlapping spectrometers (280 - 550 nm, 500 - 1030 nm) improves the stray-light performance. The spectral resolution is about 1-2 nm. • ACE-FTS: Fourier Transform Spectrometer The objective is to measure the vertical distribution of atmospheric trace gases, in particular of the regional polar O3 budget, as well as pressure and temperature (derived from CO2 lines). The instrument is an adapted version of the classical sweeping Michelson interferometer, using an optimized optical layout. The ACE-FTS measurements are recorded every 2 s. This corresponds to a measurement spacing of 2-6 km which decreases at lower altitudes due to refraction. The typical altitude spacing changes with the orbital beta angle. For historical reasons, the retrieved results are interpolated onto a 1 km "grid" using a piecewise quadratic method. For ACE-FTS version 1.0, the results were reported only on the interpolated grid (every 1 km from 0.5 to 149.5 km). For versions 2.2, both the "retrieval" grid and the "1 km" grid profiles are available. SCISAT-1 collection provides ACE-FTS and MAESTRO Level 2 Data. As of today, ACE-FTS products are available in version 4.1, while MAESTRO products are available in version 3.13. proprietary Scotia_Prince_ferry_0 Scotia Prince ferry dataset OB_DAAC STAC Catalog 1998-06-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360640-OB_DAAC.umm_json Although the ferry that data were collected from no longer operates, longstanding data collection methods continue. The Scotia Prince ferry dataset has been reorganized and added to the GNATS experiment dataset (Gulf of Maine North Atlantic Time Series, 10.5067/SeaBASS/GNATS/DATA001). Please refer to that dataset to find data that were originally listed here. proprietary Scotts_Fuel_1 Composition and origin of fuel from the hut of explorer Robert Falcon Scott, Cape Evans, Antarctica AU_AADC STAC Catalog 1910-08-15 1912-03-29 166.4, -77.633, 166.4, -77.633 https://cmr.earthdata.nasa.gov/search/concepts/C1214311239-AU_AADC.umm_json As a direct result of the 1989-90 trip as part of ASAC 245, a sample of petrol used by Scott on his ill-fated expedition to the South Pole was obtained. This petrol sample was supplied by the late Garth Varcoe of the New Zealand Antarctic Division following a discussion ensuing from a lecture given whilst on the Icebird when stuck in the ice off Davis. This sample is of intense historical interest and the results of the studies are in the download file. The material in the file reports the studies on the composition of the petrol which was left by the remaining members of Scott's group when they departed their base at Evans Head. The aim of this work was to identify the source of the fuel. A later study will attempt to comment on its suitability as a fuel for use under Antarctic conditions. There are five files on the CD. a)a poster presented at the Australian Organic Geochemistry Conference held in Leura, NSW in February of this year, b)a brief description highlighting some salient points of the poster; presented orally, c)an abstract of this work included in the conference proceedings, d)the conference proceedings and e)manuscript of a full paper submitted for publication in the Journal of Organic Geochemistry, including a table of data Geochemical analyses of the fuel used for the motor driven sledges used by the explorer Robert Falcon Scott for his 1911/1912 quest to the South Pole indicates that it is a straight run gasoline. The presence of bicadinanes, oleanane and other oleanoid angiosperm markers indicate that the feedstock oil was likely to be sourced from terrestrial source rocks of Tertiary age in the South East Asian region. The overall chemical composition of the fuel in its present state indicates that it may have been too heavy for usage in polar regions. proprietary @@ -14792,15 +14792,15 @@ SiB4_Global_HalfDegree_Hourly_1847_1 SiB4 Modeled Global 0.5-Degree Hourly Carbo SiB4_Global_HalfDegree_Monthly_1848_1 SiB4 Modeled Global 0.5-Degree Monthly Carbon Fluxes and Pools, 2000-2018 ORNL_CLOUD STAC Catalog 2000-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2345882961-ORNL_CLOUD.umm_json "This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Monthly output includes carbon, carbonyl sulfide (COS), and energy fluxes; solar-induced fluorescence (SIF); carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the ""npft"" dimension (01-15) in each data file. The PFT three-character abbreviations (""pft_names"" variable) are listed in the same order as the ""npft"" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the ""pft_area"" variable for each cell." proprietary Siberian_Biomass_Wildfire_1321_1 Siberian Boreal Forest Aboveground Biomass and Fire Scar Maps, Russia, 1969-2007 ORNL_CLOUD STAC Catalog 1969-07-01 2007-07-26 156.61, 64.77, 166.47, 69.9 https://cmr.earthdata.nasa.gov/search/concepts/C2773255198-ORNL_CLOUD.umm_json This data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia. proprietary Siberian_Larch_Stand_Age_1364_1 Distribution of Estimated Stand Age Across Siberian Larch Forests, 1989-2012 ORNL_CLOUD STAC Catalog 1989-01-01 2012-12-31 90, 49, 143, 67 https://cmr.earthdata.nasa.gov/search/concepts/C2767498872-ORNL_CLOUD.umm_json This data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set. This approach is based on the assumption that the relationship between the spectral signature of a burned or unburned forest stand acquired by Landsat ETM+ and TM sensors and stand age before and after the year 2000 is similar, thus allowing for training an algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 and direct observations of forest loss after 2000 to deliver a 24-year stand age distribution map. proprietary -Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) ALL STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary +Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary SkySat.Full.Archive.and.New.Tasking_9.0 SkySat Full Archive and New Tasking ESA STAC Catalog 2013-11-13 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1965336955-ESA.umm_json "The SkySat Level 1 Basic Scene, Level 3B Ortho Scene and Level 3B Consolidated full archive and new tasking products are available as part of the Planet imagery offer. The SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent to the imaging process. Rational Polynomial Coefficients (RPCs) are provided to enable orthorectification by the user. • Basic Panchromatic Scene product – unorthorectified, radiometrically corrected, panchromatic (PAN) imagery. • Basic Panchromatic DN Scene product – unorthorectified, panchromatic (PAN) imagery. • Basic L1A Panchromatic DN Scene product – unorthorectified, pre-super resolution, panchromatic (PAN) imagery. • Basic Analytic Scene product – unorthorectified, radiometrically corrected, 4-band multispectral (BGR-NIR) imagery. • Basic Analytic DN Scene product – unorthorectified, 4-band multispectral (BGR-NIR) imagery. Basic Scene Product Components and Format Product Components and Format • Image File (GeoTIFF format) • Metadata File (JSON format) • Rational Polynomial Coefficients (Text File) • UDM File (GeoTIFF format) Image Configurations • 1-band Panchromatic/Panchromatic DN Image (PAN) • 4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR) Ground Sampling Distance (nadir) • SkySat-1 & -2: 0.86 m (PAN), 1.0 m (MS) • SkySat-3 to -15: 0.65 m (PAN), 0.8 m (MS). 0.72 m (PAN) and 1.0 m (MS) for data acquired prior to 30/06/2020 • SkySat-16 to -21: 0.57 m (PAN), 0.75 m (MS) Geolocation Accuracy <50 m RMSE The SkySat Ortho Scene product is sensor- and geometrically-corrected (using DEMs with a post spacing of 30 – 90 m) and is projected to a cartographic map projection; the accuracy of the product varies from region-to-region based on available GCPs. • Ortho Panchromatic Scene product – orthorectified, radiometrically corrected, panchromatic (PAN) imagery. • Ortho Panchromatic DN Scene product – orthorectified, panchromatic (PAN), uncalibrated digital number imagery. • Ortho Analytic Scene product – orthorectified, 4-band multispectral (BGR-NIR) imagery. Radiometric corrections are applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. • Ortho Analytic DN Scene product – orthorectified, 4-band multispectral (BGR-NIR), uncalibrated digital number imagery. Radiometric corrections are applied to correct for any sensor artifacts. • Ortho Pansharpened Multispectral Scene product – orthorectified, pansharpened, 4-band (BGR-NIR) imagery. • Ortho Visual Scene product – orthorectified, pansharpened, colour-corrected (using a colour curve) 3-band (RGB) imagery. Ortho Scene Product Components and Format Product Components and Format • Image File (GeoTIFF format) • Metadata File (JSON format) • Rational Polynomial Coefficients (Text File) • UDM File (GeoTIFF format) Image Configurations • 1-band Panchromatic/Panchromatic DN Image (PAN) • 4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR) • 4-band Pansharpened Multispectral Image (Blue, Green, Red, NIR) • 3-band Pansharpened (Visual) Image (Red, Green, Blue) Orthorectified Pixel Size 50 cm Projection UTM WGS84 Geolocation Accuracy <10 m RMSE The SkySat Ortho Collect product is created by composing SkySat Ortho Scene products along an imaging strip into segments typically unifying ~60 individual SkySat Ortho Scenes, resulting in an image with a footprint of approximately 20 km x 5.9 km. The products may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary SkySatESAarchive_8.0 Skysat ESA archive ESA STAC Catalog 2016-02-29 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2547572338-ESA.umm_json "The SkySat ESA archive collection consists of SkySat products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products. Two different product types are offered, Ground Sampling Distance at nadir up to 65 cm for panchromatic and up to 0.8m for multi-spectral. EO-SIP Product Type Product Description Content SSC_DEF_SC Basic and Ortho scene Level 1B 4-bands Analytic /DN Basic scene Level 1B 4-bands Panchromatic /DN Basic scene Level 1A 1-band Panchromatic DN Pre Sup resolution Basic scene Level 3B 3-bands Visual Ortho Scene Level 3B 4-bands Pansharpened Multispectral Ortho Scene Level 3B 4-bands Analytic/DN/SR Ortho Scene Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect Visual 3-band Pansharpened Image Multispectral 4-band Pansharpened Image Multispectral 4-band Analytic/DN/SR Image (B, G, R, N) 1-band Panchromatic Image The Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process: Analytic - unorthorectified, radiometrically corrected, multispectral BGRN Analytic DN - unorthorectified, multispectral BGRN Panchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN) Panchromatic DN - unorthorectified, panchromatic (PAN) L1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection: Visual - orthorectified, pansharpened, and colour-corrected (using a colour curve) 3-band RGB Imagery Pansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery Analytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product. Analytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. Analytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts Panchromatic - orthorectified, radiometrically correct, panchromatic (PAN) Panchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product The Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/SkySat/ available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary Smallholder Cashew Plantations in Benin_1 Smallholder Cashew Plantations in Benin MLHUB STAC Catalog 2020-01-01 2023-01-01 2.4636579, 9.0570625, 2.5618896, 9.1603783 https://cmr.earthdata.nasa.gov/search/concepts/C2781412245-MLHUB.umm_json This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pléiades imagery. proprietary -SnowMeltDuration_PMicrowave_1843_1.1 ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-07-20 -180, 51.6, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.umm_json This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary SnowMeltDuration_PMicrowave_1843_1.1 ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-02-09 2018-07-20 -180, 51.6, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.umm_json This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary -Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017 ALL STAC Catalog 2001-01-01 2017-12-30 -179.18, 55.57, -132.58, 71.42 https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.umm_json This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter. proprietary +SnowMeltDuration_PMicrowave_1843_1.1 ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-07-20 -180, 51.6, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.umm_json This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017 ORNL_CLOUD STAC Catalog 2001-01-01 2017-12-30 -179.18, 55.57, -132.58, 71.42 https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.umm_json This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter. proprietary +Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017 ALL STAC Catalog 2001-01-01 2017-12-30 -179.18, 55.57, -132.58, 71.42 https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.umm_json This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter. proprietary Snow_Depth_Data_Images_1656_1 Snow Depth, Stratigraphy, and Temperature in Wrangell St Elias NP, Alaska, 2016-2018 ORNL_CLOUD STAC Catalog 2016-09-01 2018-03-20 -143.32, 62.26, -143, 62.39 https://cmr.earthdata.nasa.gov/search/concepts/C2170971586-ORNL_CLOUD.umm_json This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek. proprietary Snow_Wildlife_Tracks_AK_WA_2188_1 Snow Properties and Wildlife Tracks in Washington and Alaska ORNL_CLOUD STAC Catalog 2021-01-09 2023-03-13 -150.01, 48.05, -117.17, 63.97 https://cmr.earthdata.nasa.gov/search/concepts/C2772851281-ORNL_CLOUD.umm_json This dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions. The track sink depth and dimensions (width and length) of three consecutive footprints were measured from one individual. Age class was recorded for moose based either on visual confirmation of an individual creating snow tracks or based on track dimensions. The ability to differentiate age classes for smaller ungulates was more uncertain, so age classes for deer, caribou, or sheep were not specified. Animal gait was identified using a simple classification scheme. Data also include animal species, snow density, hardness, total ice, surface temperature, and vegetation type. To best capture snow hardness, surface penetrability and hand-hardness were measured throughout the snowpack. The data are provided in comma-separated values (CSV) format. proprietary Snowmelt_timing_maps_V2_1712_2 Snowmelt Timing Maps Derived from MODIS for North America, Version 2, 2001-2018 ORNL_CLOUD STAC Catalog 2001-01-01 2018-12-31 -180, 10, 0, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2764725108-ORNL_CLOUD.umm_json This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31. The STMs were created by conducting a time-series analysis of the MOD10A2 snow maps to identify the DOY of snowmelt on a per-pixel basis. Snowmelt timing (no-snow) was defined as a snow-free reading following two consecutive snow-present readings for a given 500-m pixel. The count of STM values is also reported, which represents the number of years on record in the STMs from 2001-2018. proprietary @@ -14809,8 +14809,8 @@ Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow De SoilResp_HeterotrophicResp_1928_1 Global Gridded 1-km Soil and Soil Heterotrophic Respiration Derived from SRDB v5 ORNL_CLOUD STAC Catalog 1961-01-01 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2345796019-ORNL_CLOUD.umm_json This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016. proprietary SoilSCAPE_1339_1 Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA ORNL_CLOUD STAC Catalog 2011-08-03 2019-12-14 -120.99, 31.74, -83.66, 42.3 https://cmr.earthdata.nasa.gov/search/concepts/C2736724942-ORNL_CLOUD.umm_json This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales. proprietary SoilSCAPE_V2_2049_2 Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, Version 2 ORNL_CLOUD STAC Catalog 2021-12-03 2023-02-03 -110.05, -36.72, 174.62, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2736725173-ORNL_CLOUD.umm_json This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format. proprietary -Soil_ActiveLayer_Properties_AK_2315_1 ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska ALL STAC Catalog 2016-08-09 2018-07-07 -149.53, 63.88, -146.56, 68.56 https://cmr.earthdata.nasa.gov/search/concepts/C2849255421-ORNL_CLOUD.umm_json This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary Soil_ActiveLayer_Properties_AK_2315_1 ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska ORNL_CLOUD STAC Catalog 2016-08-09 2018-07-07 -149.53, 63.88, -146.56, 68.56 https://cmr.earthdata.nasa.gov/search/concepts/C2849255421-ORNL_CLOUD.umm_json This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary +Soil_ActiveLayer_Properties_AK_2315_1 ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska ALL STAC Catalog 2016-08-09 2018-07-07 -149.53, 63.88, -146.56, 68.56 https://cmr.earthdata.nasa.gov/search/concepts/C2849255421-ORNL_CLOUD.umm_json This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary Soil_Carbon_Flux_Maps_1683_1 Gridded Winter Soil CO2 Flux Estimates for pan-Arctic and Boreal Regions, 2003-2100 ORNL_CLOUD STAC Catalog 1993-01-01 2100-11-30 -180, -84.69, 179.9, 89.98 https://cmr.earthdata.nasa.gov/search/concepts/C2143812328-ORNL_CLOUD.umm_json This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100. proprietary Soil_Moisture_Alaska_Alberta_2123_1 Hourly Soil Moisture Logger Data, Alberta and Alaska, 2017-2021 ORNL_CLOUD STAC Catalog 2017-07-24 2021-07-29 -148.81, 56.66, -115.11, 69.63 https://cmr.earthdata.nasa.gov/search/concepts/C2633820284-ORNL_CLOUD.umm_json This dataset includes hourly in-situ soil moisture measurements from data loggers in predominantly organic soils (very low bulk density) at two locations: 1) along the Sag River in Alaska, U.S., and 2) near Red Earth Creek in Alberta, Canada. The dataset also provides soil moisture probe periods, temperature probe readings, as well as calibration coefficients and soil profile measurements used to create per probe calibrations for derived volumetric moisture content. The Campbell Scientific CR200 data loggers used CS625 water content reflectometers and temperature probe 109. Further details to the derivation of the calibrations are provided in a supplementary document. The purpose of the dataset is to provide field measurements that can be used for calibration/validation for satellite-based soil moisture retrieval algorithms. With some interruptions, the dataset exists from July 2017 to July 2021. The data are provided in comma-separated values (CSV) format. proprietary Soil_Sensors_1 Data collected from in-situ soil sensors placed at Macquarie Island and Casey Station AU_AADC STAC Catalog 2005-01-01 110.52394, -66.28192, 158.9392, -54.498737 https://cmr.earthdata.nasa.gov/search/concepts/C1214313810-AU_AADC.umm_json "Data are collected for the purposes of monitoring on-ground works at Australian Antarctic stations associated with the remediation of petroleum hydrocarbon contaminated soil. Output datasets consist of soil oxygen (%), soil temperature (C), soil moisture content (VWC - Volumetric Water Content %), and aeration manifold pressure as measured by buried sensors (O2, T C, VWC) or manifold instruments (pressure). Sensor types are either: AD590 (temperature C) AD592 (temperature C) Figaro KE25 (% oxygen) Vegetronix VH400 (Volumetric Water Content %) 26PCD (Pressure, kPa) Sensors are attached via instrument cables to Datataker dt80 series loggers, which are housed in waterproof containers mounted on buildings, or inside buildings at Australian Antarctic stations. At the Macquarie Island isthmus, oxygen sensors are attached to buried groundwater monitoring wells (screened PVC tubes, known as mini-piezometers). Pressure sensors are attached to air distribution manifolds (part of an in-situ aeration distribution network), and temperature sensors are buried in the soil profile. Sensor nomenclature is as follows: FF0807/1/O2 (Fuel Farm, 2008 installation, mini-piezometer number 07, Sensor 1, Oxygen sensor) MPH_PS_3 (Main Power House, pressure sensor number 03) Biopiles consist of excavated soil placed in temporary, geo-engineered liner cells. Soil oxygen, soil temperature, and soil moisture content are typically measured at 50 cm height intervals from within the soil piles. Temperature and moisture are also typically measured from within the subgrade and liner materials - common nomenclature for sensor names are as follows: BP1/0.5SS_G11/O2 (Biopile 1, buried 0.5 m in soil profile, location G11, Oxygen sensor) BP1/AGM_G1/T(Biopile 1, Above GeoMembrane, Location G1, Temperature sensor) BP6/AGCL_N1/M (Biopile 6, Above Geosynthetic Clay Liner, Location N1, Moisture sensor) BP6/IGCL_N9/M (Biopile 6, Inside Geosynthetic Clay Liner, Location N9, Moisture sensor) EXT/-30SS_E1/M (External soil location, 30 cm below sediment surface, Sensor 1, Moisture sensor) Permeable Reactive Barrier (PRB's) are permeable gates emplaced within the regolith to treat hydrocarbon contaminated groundwater/meltwater and prevent offsite migration of contaminants (primarily hydrocarbons). The barriers have undergone several design iterations, but have consisted of staged (3 sections) permeable reactive or non-reactive filter media (Granular Activated Carbon, Silica sand, Zeolite, MaxBac (TM), Zeopro (TM), Zero Valent Iron), which are placed in buried galvanised shipping cages. The original PRB (installed 2005/06) is named ""PRB"", the second smaller PRB (named the Upper PRB or ""UPRB"" due to its higher elevation in the ) was installed in 2010/11 to treat contaminated groundwater around the MPH settling tank bund and protected the area cleaned as part of the MPH excavation. From this date, the original PRB has also been referred to as the ""lower PRB"". Sensor nomenclature is as follows: C_MP9/700/T (MiniPiezometer 9, 700 mm below ground surface, Temperature sensor) C_CG3_3/600/02 (Cage 3,Section 3, 600 mm below ground surface, Oxygen sensor) These data are downloaded from the sensors to the Australian Antarctic Division on a daily basis. Data are collected by the sensors every 5-20 minutes. As of 2013-03-04, the following personnel have been involved in the project: Greg Hince (AAD) - Project Manager, Field Remediation (11/12-ongoing). Principle Contact Ian Snape (AAD) - Project Principal (Macquarie Island and Casey Station), Macquarie Island 2008 field team. Geoff Stevens (University of Melbourne) - Project Principal - Casey Lower PRB installation Ben Raymond (AAD) - Calibration and Installation of sensors for Macquarie Island 08/09 field season, maintenance of database and remote troubleshooting of dataloggers. Tim Spedding (ex AAD) - Field Project Manager (08/09-10/11), Macquarie Island 2008 field team Dan Wilkins (AAD) - Datalogger management and system design (2009 onwards), Casey station sensor installation 10/11 and 11/12. John Rayner (ex AAD) - System design - Oxygen sensors. Macquarie Island 2008 field team. Installation of lower PRB (Casey) in 05/06. Lauren Wise (AAD) - Field maintenance and system operation (Macquarie Island, 10/11 and 12/13) Rebecca McWatters (AAD)- Casey Station sensors installation 10/11, 11/12, 12/13 Susan Ferguson (ex AAD) - Macquarie Island 2008 field team, Macquarie Island system maintenance 2009. Brett Quinton (ex AAD) - Macquarie Island system maintenance 2009 Charles Sutherland (AAD contractor/expeditioner) - Macquarie Island system maintenance 12/13 field season Robby Kilpatrick (AAD contractor/expeditioner) - Calibration and Installation of sensors for Macquarie Island 11/12 field season Kathryn Mumford (AAS Project Co-investigator, University of Melbourne) - Installation of lower PRB (Casey) in 05/06. Tom Statham (University of Melbourne, PhD student) - System installation, Casey 10/11 Warren Nichols - Oxygen sensor modifications (resin encasement) Rebecca Miller (AAD contractor/expeditioner) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season Dan Jones (Queens University, Canada) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season Various members of AAD Telecommunications Team (on ground troubleshooting and maintenance)" proprietary @@ -14820,8 +14820,8 @@ Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Se Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary Sonoma_County_Forest_AGB_1764_1 CMS: LiDAR Biomass Improved for High Biomass Forests, Sonoma County, CA, USA, 2013 ORNL_CLOUD STAC Catalog 2013-09-01 2013-09-01 -123.54, 38.11, -122.34, 38.85 https://cmr.earthdata.nasa.gov/search/concepts/C2389021440-ORNL_CLOUD.umm_json This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated. proprietary South Africa Crop Type Competition_1 South Africa Crop Type Competition MLHUB STAC Catalog 2020-01-01 2023-01-01 17.818514, -34.1538276, 19.7650866, -30.7480751 https://cmr.earthdata.nasa.gov/search/concepts/C2781412651-MLHUB.umm_json This dataset was produced as part of the [Radiant Earth Spot the Crop Challenge](https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites. proprietary -Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ALL STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ORNL_CLOUD STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary +Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ALL STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary Southern_Ocean_Drifter_0 Southern Pacific Ocean drifter measurements in 1996 OB_DAAC STAC Catalog 1996-09-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360666-OB_DAAC.umm_json Measurements taken by a drifter in the Southern Pacific Ocean in 1996. proprietary Spire.live.and.historical.data_8.0 Spire live and historical data ESA STAC Catalog 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689697-ESA.umm_json "The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided. GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present. Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information. polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation. patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs) Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites. gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground. gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties. Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification. grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides). Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016 The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 ." proprietary Stream_GIS_USGS Digital Line Graphs of U.S. Streams for the EPA Clean Air Mapping and Analysis Program (C-MAP) CEOS_EXTRA STAC Catalog 1970-01-01 -127.77, 23.25, -65.71, 48.15 https://cmr.earthdata.nasa.gov/search/concepts/C2231553171-CEOS_EXTRA.umm_json This is a 1:2,000,000 coverage of streams for the conterminous United States. This coverage was intended for use as a background display for the National Water Summary program. The stream layer was extracted from the 1:2,000,000 Digital Line Graph files. Originally, each state was stored as a separate coverage. In this version, the individual state coverages all have been appended. [Summary provided by EPA] proprietary @@ -14899,8 +14899,8 @@ TEMPO_O3TOT_L3_V03 TEMPO gridded ozone total column V03 (PROVISIONAL) LARC_CLOUD TEMPO_RADT_L1_V03 TEMPO geolocated Earth radiances twilight V03 (PROVISIONAL) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2930766795-LARC_CLOUD.umm_json Level 1 twilight radiance files provide radiance measured during twilight hours to capture city lights at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically calibrated and geolocated radiances for the UV and visible bands, corresponding noise, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes image processing steps to produce radiometrically calibrated radiances with nominal navigation. These data reached provisional validation on December 9, 2024. proprietary TEMPO_RAD_L1_V02 TEMPO geolocated Earth radiances V02 (BETA) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2842845562-LARC_CLOUD.umm_json Level 1 radiance files provide radiance information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated and geolocated radiances for the UV and visible bands, corresponding noise, parameterized wavelength grid, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, (2) Additional wavelength calibration to improve wavelength registration, (3) Image Navigation and Registration (INR) using GOES-R data, and (4) post INR processing geolocation tagging and polarization correction. Please refer to the ATBD for details. These data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters. Because the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status. The TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov. proprietary TEMPO_RAD_L1_V03 TEMPO geolocated Earth radiances V03 (PROVISIONAL) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2930759336-LARC_CLOUD.umm_json Level 1 radiance files provide radiance information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated and geolocated radiances for the UV and visible bands, corresponding noise, parameterized wavelength grid, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, (2) Additional wavelength calibration to improve wavelength registration, (3) Image Navigation and Registration (INR) using GOES-R data, and (4) post INR processing geolocation tagging. These data reached provisional validation on December 9, 2024. proprietary -TEMR_RSFCE Air Temperature Time Series ALL STAC Catalog 1883-01-01 1987-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608675-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by Computer Centre North Administration for hydrometeorology in 1990 and containes air temperature from 68 stations in Arhangelsk, Vologda regions and Komi ASSR in Russia. Data is currently stored on magnetic tape (800 bit/inch). proprietary TEMR_RSFCE Air Temperature Time Series SCIOPS STAC Catalog 1883-01-01 1987-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608675-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by Computer Centre North Administration for hydrometeorology in 1990 and containes air temperature from 68 stations in Arhangelsk, Vologda regions and Komi ASSR in Russia. Data is currently stored on magnetic tape (800 bit/inch). proprietary +TEMR_RSFCE Air Temperature Time Series ALL STAC Catalog 1883-01-01 1987-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608675-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by Computer Centre North Administration for hydrometeorology in 1990 and containes air temperature from 68 stations in Arhangelsk, Vologda regions and Komi ASSR in Russia. Data is currently stored on magnetic tape (800 bit/inch). proprietary TG02_Balloon_VOC_1110_1 LBA-ECO TG-02 Biogenic VOC Emissions from Brazilian Amazon Forest and Pasture Sites ORNL_CLOUD STAC Catalog 1998-03-22 2000-02-16 -62.2, -10.08, -54.97, -0.86 https://cmr.earthdata.nasa.gov/search/concepts/C2768941787-ORNL_CLOUD.umm_json This data set reports concentrations of biogenic volatile organic compounds (BVOCs) collected from tethered balloon-sampling platforms above selected forest and pasture sites in the Brazilian Amazon in March 1998, February 1999, and February 2000. The air samples were collected from forested sites in Brazil: the Tapajos forest (Para) in the Tapajos/Xingu moist forest; Balbina (Amazonas) in the Uatuma moist forest; and Jaru (Rondonia) in the Purus/Madeira moist forest. Two other sites were also located in Rondonia: at a forest reserve (Rebio Jaru) and a pasture (Fazenda Nossa Senhora Aparecida). The BVOCs measured included isoprene, alpha and beta pinene, camphene, sabinene, myrcene, limonene, and other monoterpenes. Approximately 24 to 40 soundings, including as many as four VOC samples collected simultaneously at various altitudes, were made at each site. There is one comma-delimited data file with this data set. proprietary TG03_AERONET_AOT_1128_1 LBA-ECO TG-03 Aeronet Aerosol Optical Thickness Measurements, Brazil: 1993-2005 ORNL_CLOUD STAC Catalog 1993-01-01 2005-01-01 -70.31, -20.45, -48.28, -1.2 https://cmr.earthdata.nasa.gov/search/concepts/C2768942874-ORNL_CLOUD.umm_json This data set includes aerosol optical thickness measurements from the CIMEL sunphotometer for 22 sites in Brazil during the period from 1993-2005. The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (cloud-screened and quality-assured). Data provided here are Level 2.0. There are 22 comma-delimited data files with this data set and one companion text file which contains the latitude, longitude, and elevation of the 22 sites. proprietary TG03_Aeronet_Solar_Flux_1137_1 LBA-ECO TG-03 Solar Surface Irradiance and PAR, Brazilian Amazon: 1999-2004 ORNL_CLOUD STAC Catalog 1999-01-01 2004-12-31 -67.87, -15.73, -54.95, -1.92 https://cmr.earthdata.nasa.gov/search/concepts/C2781384398-ORNL_CLOUD.umm_json This data set includes solar surface irradiance from Kipp and Zonen CM-21 pyranometers, both total unfiltered and filtered (RG695), and photosynthetically active radiation (PAR) from Skye-Probetech SKE-510 PAR sensors. Measurements were made at six sites acrosss the Brazilian Amazon during the period from 1999 to 2004. These sites were co-located with AERONET (AErosol RObotic NETwork) program sites. There are 17 comma-delimited data files (.csv) with this data set. The AERONET program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of those properties. The network imposes standardization of instruments, calibration, and processing. proprietary @@ -15581,13 +15581,13 @@ UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical ALL STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary UM0506_26_aerosol_optical Aerosol optical thickness ALL STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary UM0506_26_aerosol_optical Aerosol optical thickness SCIOPS STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary -UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system SCIOPS STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system ALL STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary +UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system SCIOPS STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton ALL STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml).　　　　　　　　　　　　　　　　　　　　　http://biows.ac.jp/~plankton/um0809-1a.png proprietary UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton SCIOPS STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml).　　　　　　　　　　　　　　　　　　　　　http://biows.ac.jp/~plankton/um0809-1a.png proprietary UMD_GEOL388_0 Measurements from the Atlantic Ocean made by the University of Maryland (UMD) OB_DAAC STAC Catalog 2003-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360691-OB_DAAC.umm_json Measurements from the Atlantic Ocean made by the University of Maryland between New England, Bermuda, and Brazil in 2003. proprietary -UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls ALL STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary +UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls ALL STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary UNEP_GRID_SF_ASIA Asia Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 26, -12, 155, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2232847540-CEOS_EXTRA.umm_json The Asian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This project (which has been carried out as a cooperative activity between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has pooled available data sets, many of which had been assembled for the global demography project. All data were checked, international boundaries and coastlines were replaced with a standard template, the attribute database was redesigned, and new, more reliable population estimates for subnational units were produced for all countries. From the resulting data sets, raster surfaces representing population distribution and population density were created in collaboration between NCGIA and GRID-Geneva. proprietary UNEP_GRID_SF_GLOBAL Global Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849256-CEOS_EXTRA.umm_json Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions. This project has provided a population database depicting the worldwide distribution of population in a 1X1 latitude/longitude grid system. The database is unique, firstly, in that it makes use of the most recent data available (1990). Secondly, it offers true apportionment for each grid cell that is, if a cell contains populations from two different countries, each is assigned a percentage of the grid cell area, rather than artificially assigning the whole cell to one or the other country (this is especially important for European countries). Thirdly, the database gives the percentage of a country's total population accounted for in each cell. So if a country's total in a given year around 1990 (1989 or 1991, for example) is known, then population in each cell can be calculated by using the percentage given in the database with the assumption that the growth rate in each cell of the country is the same. And lastly, this dataset is easy to be updated for each country as new national population figures become available. proprietary UNEP_GRID_SF_LATINAMERICA_1.0 Latin America and Caribbean Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -120, -60, -31, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2232848778-CEOS_EXTRA.umm_json The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This documentation describes the Latin American Population Database, a collaborative effort between the International Center for Tropical Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID, Sioux Falls) and the World Resources Institute (WRI). This work is intended to provide a population database that compliments previous work carried out for Asia and Africa. This data set is more detailed than the Africa and Asia data sets. Population estimates for 1960, 1970, 1980, 1990 and 2000 are also provided. The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). proprietary @@ -15599,8 +15599,8 @@ USAP-0944266 Climate, Ice Dynamics and Biology using a Deep Ice Core from the We USAP-0944348 Climate, Ice Dynamics and Biology using a Deep Ice Core from the West Antarctic Ice Sheet Ice Divide AMD_USAPDC STAC Catalog 2010-08-01 2015-07-31 -112.1115, -79.481, -112.1115, -79.481 https://cmr.earthdata.nasa.gov/search/concepts/C2532070599-AMD_USAPDC.umm_json This award supports renewal of funding of the WAIS Divide Science Coordination Office (SCO). The Science Coordination Office (SCO) was established to represent the research community and facilitates the project by working with support organizations responsible for logistics, drilling, and core curation. During the last five years, 26 projects have been individually funded to work on this effort and 1,511 m of the total 3,470 m of ice at the site has been collected. This proposal seeks funding to continue the SCO and related field operations needed to complete the WAIS Divide ice core project. Tasks for the SCO during the second five years include planning and oversight of logistics, drilling, and core curation; coordinating research activities in the field; assisting in curation of the core in the field; allocating samples to individual projects; coordinating the sampling effort; collecting, archiving, and distributing data and other information about the project; hosting an annual science meeting; and facilitating collaborative efforts among the research groups. The intellectual merit of the WAIS Divide project is to better predict how human-caused increases in greenhouse gases will alter climate requires an improved understanding of how previous natural changes in greenhouse gases influenced climate in the past. Information on previous climate changes is used to validate the physics and results of climate models that are used to predict future climate. Antarctic ice cores are the only source of samples of the paleo-atmosphere that can be used to determine previous concentrations of carbon dioxide. Ice cores also contain records of other components of the climate system such as the paleo air and ocean temperature, atmospheric loading of aerosols, and indicators of atmospheric transport. The WAIS Divide ice core project has been designed to obtain the best possible record of greenhouse gases during the last glacial cycle (last ~100,000 years). The site was selected because it has the best balance of high annual snowfall (23 cm of ice equivalent/year), low dust Antarctic ice that does not compromise the carbon dioxide record, and favorable glaciology. The main science objectives of the project are to investigate climate forcing by greenhouse gases, initiation of climate changes, stability of the West Antarctic Ice Sheet, and cryobiology in the ice core. The project has numerous broader impacts. An established provider of educational material (Teachers' Domain) will develop and distribute web-based resources related to the project and climate change for use in K-12 classrooms. These resources will consist of video and interactive graphics that explain how and why ice cores are collected, and what they tell us about future climate change. Members of the national media will be included in the field team and the SCO will assist in presenting information to the general public. Video of the project will be collected and made available for general use. Finally, an opportunity will be created for cryosphere students and early career scientists to participate in field activities and core analysis. An ice core archive will be available for future projects and scientific discoveries from the project can be used by policy makers to make informed decisions. proprietary USAP-1043471 A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes ALL STAC Catalog 2011-08-01 2015-07-31 -112.5, -79.5, -112.086, -79.468 https://cmr.earthdata.nasa.gov/search/concepts/C2532071870-AMD_USAPDC.umm_json This award supports a project to obtain the first set of isotopic-based provenance data from the WAIS divide ice core. A lack of data from the WAIS prevents even a basic knowledge of whether different sources of dust blew around the Pacific and Atlantic sectors of the southern latitudes. Precise isotopic measurements on dust in the new WAIS ice divide core are specifically warranted because the data will be synergistically integrated with other high frequency proxies, such as dust concentration and flux, and carbon dioxide, for example. Higher resolution proxies will bridge gaps between our observations on the same well-dated, well-preserved core. The intellectual merit of the project is that the proposed analyses will contribute to the WAIS Divide Project science themes. Whether an active driver or passive recorder, dust is one of the most important but least understood components of regional and global climate. Collaborative and expert discussion with dust-climate modelers will lead to an important progression in understanding of dust and past atmospheric circulation patterns and climate around the southern latitudes, and help to exclude unlikely air trajectories to the ice sheets. The project will provide data to help evaluate models that simulate the dust patterns and cycle and the relative importance of changes in the sources, air trajectories and transport processes, and deposition to the ice sheet under different climate states. The results will be of broad interest to a range of disciplines beyond those directly associated with the WAIS ice core project, including the paleoceanography and dust- paleoclimatology communities. The broader impacts of the project include infrastructure and professional development, as the proposed research will initiate collaborations between LDEO and other WAIS scientists and modelers with expertise in climate and dust. Most of the researchers are still in the early phase of their careers and hence the project will facilitate long-term relationships. This includes a graduate student from UMaine, an undergraduate student from Columbia University who will be involved in lab work, in addition to a LDEO Postdoctoral scientist, and possibly an additional student involved in the international project PIRE-ICETRICS. The proposed research will broaden the scientific outlooks of three PIs, who come to Antarctic ice core science from a variety of other terrestrial and marine geology perspectives. Outreach activities include interaction with the science writers of the Columbia's Earth Institute for news releases and associated blog websites, public speaking, and involvement in an arts/science initiative between New York City's arts and science communities to bridge the gap between scientific knowledge and public perception. proprietary USAP-1043471 A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes AMD_USAPDC STAC Catalog 2011-08-01 2015-07-31 -112.5, -79.5, -112.086, -79.468 https://cmr.earthdata.nasa.gov/search/concepts/C2532071870-AMD_USAPDC.umm_json This award supports a project to obtain the first set of isotopic-based provenance data from the WAIS divide ice core. A lack of data from the WAIS prevents even a basic knowledge of whether different sources of dust blew around the Pacific and Atlantic sectors of the southern latitudes. Precise isotopic measurements on dust in the new WAIS ice divide core are specifically warranted because the data will be synergistically integrated with other high frequency proxies, such as dust concentration and flux, and carbon dioxide, for example. Higher resolution proxies will bridge gaps between our observations on the same well-dated, well-preserved core. The intellectual merit of the project is that the proposed analyses will contribute to the WAIS Divide Project science themes. Whether an active driver or passive recorder, dust is one of the most important but least understood components of regional and global climate. Collaborative and expert discussion with dust-climate modelers will lead to an important progression in understanding of dust and past atmospheric circulation patterns and climate around the southern latitudes, and help to exclude unlikely air trajectories to the ice sheets. The project will provide data to help evaluate models that simulate the dust patterns and cycle and the relative importance of changes in the sources, air trajectories and transport processes, and deposition to the ice sheet under different climate states. The results will be of broad interest to a range of disciplines beyond those directly associated with the WAIS ice core project, including the paleoceanography and dust- paleoclimatology communities. The broader impacts of the project include infrastructure and professional development, as the proposed research will initiate collaborations between LDEO and other WAIS scientists and modelers with expertise in climate and dust. Most of the researchers are still in the early phase of their careers and hence the project will facilitate long-term relationships. This includes a graduate student from UMaine, an undergraduate student from Columbia University who will be involved in lab work, in addition to a LDEO Postdoctoral scientist, and possibly an additional student involved in the international project PIRE-ICETRICS. The proposed research will broaden the scientific outlooks of three PIs, who come to Antarctic ice core science from a variety of other terrestrial and marine geology perspectives. Outreach activities include interaction with the science writers of the Columbia's Earth Institute for news releases and associated blog websites, public speaking, and involvement in an arts/science initiative between New York City's arts and science communities to bridge the gap between scientific knowledge and public perception. proprietary -USAP-1043623_1 Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean AMD_USAPDC STAC Catalog 2011-06-15 2015-05-31 117.5, -67.4, 146, -47 https://cmr.earthdata.nasa.gov/search/concepts/C2532072248-AMD_USAPDC.umm_json Accurate parameterizations of the air-sea fluxes of CO2 into the Southern Ocean, in particular at high wind velocity, are needed to better assess how projections of global climate warming in a windier world could affect the ocean carbon uptake, and alter the ocean heat budget at high latitudes. Air-sea fluxes of momentum, sensible and latent heat (water vapor) and carbon dioxide (CO2) are to be measured continuously underway on cruises using micrometeorological eddy covariance techniques adapted to ship-board use. The measured gas transfer velocity (K) is then to be related to other parameters known to affect air-sea-fluxes. A stated goal of this work is the collection of a set of direct air-sea flux measurements at high wind speeds, conditions where parameterization of the relationship of gas exchange to wind-speed remains contentious. The studies will be carried out at sites in the Southern Ocean using the USAP RV Nathaniel B Palmer as measurment platform. Co-located pCO2 data, to be used in the overall analysis and enabling internal consistency checks, are being collected from existing underway systems aboard the USAP research vessel under other NSF awards. proprietary USAP-1043623_1 Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean ALL STAC Catalog 2011-06-15 2015-05-31 117.5, -67.4, 146, -47 https://cmr.earthdata.nasa.gov/search/concepts/C2532072248-AMD_USAPDC.umm_json Accurate parameterizations of the air-sea fluxes of CO2 into the Southern Ocean, in particular at high wind velocity, are needed to better assess how projections of global climate warming in a windier world could affect the ocean carbon uptake, and alter the ocean heat budget at high latitudes. Air-sea fluxes of momentum, sensible and latent heat (water vapor) and carbon dioxide (CO2) are to be measured continuously underway on cruises using micrometeorological eddy covariance techniques adapted to ship-board use. The measured gas transfer velocity (K) is then to be related to other parameters known to affect air-sea-fluxes. A stated goal of this work is the collection of a set of direct air-sea flux measurements at high wind speeds, conditions where parameterization of the relationship of gas exchange to wind-speed remains contentious. The studies will be carried out at sites in the Southern Ocean using the USAP RV Nathaniel B Palmer as measurment platform. Co-located pCO2 data, to be used in the overall analysis and enabling internal consistency checks, are being collected from existing underway systems aboard the USAP research vessel under other NSF awards. proprietary +USAP-1043623_1 Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean AMD_USAPDC STAC Catalog 2011-06-15 2015-05-31 117.5, -67.4, 146, -47 https://cmr.earthdata.nasa.gov/search/concepts/C2532072248-AMD_USAPDC.umm_json Accurate parameterizations of the air-sea fluxes of CO2 into the Southern Ocean, in particular at high wind velocity, are needed to better assess how projections of global climate warming in a windier world could affect the ocean carbon uptake, and alter the ocean heat budget at high latitudes. Air-sea fluxes of momentum, sensible and latent heat (water vapor) and carbon dioxide (CO2) are to be measured continuously underway on cruises using micrometeorological eddy covariance techniques adapted to ship-board use. The measured gas transfer velocity (K) is then to be related to other parameters known to affect air-sea-fluxes. A stated goal of this work is the collection of a set of direct air-sea flux measurements at high wind speeds, conditions where parameterization of the relationship of gas exchange to wind-speed remains contentious. The studies will be carried out at sites in the Southern Ocean using the USAP RV Nathaniel B Palmer as measurment platform. Co-located pCO2 data, to be used in the overall analysis and enabling internal consistency checks, are being collected from existing underway systems aboard the USAP research vessel under other NSF awards. proprietary USAP-1056396_1 CAREER: Protist Nutritional Strategies in Permanently Stratified Antarctic Lakes AMD_USAPDC STAC Catalog 2011-05-01 2016-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532071892-AMD_USAPDC.umm_json This project supported an integrated research and education program in the fields of polar biology and environmental microbiology, focusing on single-celled eukaryotes (protists) in high latitude ice-covered Antarctic lakes systems. Protists play important roles in energy flow and material cycling, and act as both primary producers (fixing inorganic carbon by photosynthesis) and consumers (preying on bacteria by phagotrophic digestion). The McMurdo Dry Valleys (MDV) located in Victoria Land, Antarctica, harbor microbial communities which are isolated in the unique aquatic ecosystem of perennially ice-capped lakes. The project studied: (1) the impact of permanent biogeochemical gradients on protist trophic strategy, (2) the effect of major abiotic drivers (light and nutrients) on the distribution of two key mixotrophic and photoautotrophic protist species, and (3) the effect of episodic nutrient pulses on mixotroph communities in high latitude (ultraoligotrophic) MDV lakes versus low latitude (eutrophic) watersheds. Sampling dates: February 4 – April 10, 2008; November 11- 28, 2012; December 12, 2012 Sampling locations/depths: East Lobe Lake Bonney/5m, 10m, 13m, 15m, 20m, 25m, 30m West Lobe Lake Bonney/5m, 10m, 13m, 15m, 20m, 25m, 30m Lake Fryxell/5m, 7m, 9m, 11m, 12m, 15m Lake Vanda/10m, 20m, 30m, 40m, 50m, 60m, 70m, 75m, 80m Two kinds of metadata from this project are available: 1) DNA sequence data – DNA was extracted from filtered lake water (1-2L) collected from sampling locations and dates reported above. Environmental DNA was PCR-amplified using primers specific for the following genes: 16S rRNA, 18S rRNA, rbcL, cbbM, nifJ, psbA. Genes were sequenced on an Applied Biosystems DNA analyzer or an Illumina MiSeq or HiSeq instruments. All DNA sequences from this project are available via GenBank. 2) Limnological metadata - Limnological data was collected from sampling locations and dates reported above. Data includes PAR, conductivity, temperature, Chlorophyll a, and macronutrients and is available via the McMurdo Dry Valleys LTER Data Center. proprietary USAP-1141939 Antarctic Cloud Physics: Fundamental Observations from Ross Island AMD_USAPDC STAC Catalog 2012-08-15 2015-07-31 -167.0365, -77.57, -166.31, -77.5203 https://cmr.earthdata.nasa.gov/search/concepts/C2532071883-AMD_USAPDC.umm_json Antarctic clouds constitute an important parameter of the surface radiation budget and thus play a significant role in Antarctic climate and climate change. The variability in, and long term trends of, cloud optical and microphysical properties are therefore fundamental in parameterizing the mixed phase (water-snow-ice) coastal Antarctic stratiform clouds experienced around the continent. Using a spectoradiometer that covers the wavelength range of 350 to 2200nm, the downwelled spectral irradiance at the earth surface (Ross Island) will be used to retrieve the optical depth, thermodynamic phase, liquid water droplet effective radius, and ice-cloud effective particle size of overhead clouds, at hourly intervals and for an austral summer season (Oct-March). Based on the very limited data sets that exist for the maritime Antarctic, expectations are that Ross Island (Lat 78 S) should exhibit clouds with: a) An abundance of supercooled liquid water, and related mixed-phase cloud processes b) Cloud nucleation from year round biogenic and oceanic sources, in an otherwise pristine environment c) Simple cloud geometries of predominantly stratiform cloud decks Increased understanding of the cloud properties in the region of the main USAP base, McMurdo station is also relevant to operational weather forecasting relevant to aviation. A range of educational and outreach activities are associate with the project, including provision of workshops for high school teachers will be carried out. proprietary USAP-1142084_1 Applying High-resolution GPS Tracking to Characterize Sensory Foraging Strategies of the Black-browed Albatross, a Top Predator of the Southern Ocean Ecosystem AMD_USAPDC STAC Catalog 2012-08-15 2015-07-31 40, -60, 100, -25 https://cmr.earthdata.nasa.gov/search/concepts/C2532071897-AMD_USAPDC.umm_json "We collected GPS tracks and stomach temperature records from Blackbrowed Albatross from a breeding colony at ""Canon des Sourcils Noirs"" on Kerguelen Island for the purpose of analyzing their flight patterns with regard to foraging events. We found that most birds regurgitated their stomach temperature pill transmitters early on in their trip. The GPS tracks do show their overall foraging flight patterns and include events that are characteristic of olfactory foraging such as upwind turns and zigzagging flight." proprietary @@ -15618,10 +15618,10 @@ USAP-1443637_1 Analysis of Voltage-gated Ion Channels in Antarctic Fish AMD_USAP USAP-1444167_1 Antarctic Notothenioid Fishes: Sentinel Taxa for Southern Ocean Warming AMD_USAPDC STAC Catalog 2015-07-01 2020-06-30 -70, -76, -55, -58 https://cmr.earthdata.nasa.gov/search/concepts/C2532072217-AMD_USAPDC.umm_json "Antarctic fish and their early developmental stages are an important component of the food web that sustains life in the cold Southern Ocean (SO) that surrounds Antarctica. They feed on smaller organisms and in turn are eaten by larger animals, including seals and killer whales. Little is known about how rising ocean temperatures will impact the development of Antarctic fish embryos and their growth after hatching. This project will address this gap by assessing the effects of elevated temperatures on embryo viability, on the rate of embryo development, and on the gene ""toolkits"" that respond to temperature stress. One of the two species to be studied does not produce red blood cells, a defect that may make its embryos particularly vulnerable to heat. The outcomes of this research will provide the public and policymakers with ""real world"" data that are necessary to inform decisions and design strategies to cope with changes in the Earth's climate, particularly with respect to protecting life in the SO. The project will also further the NSF goals of training new generations of scientists, including providing scientific training for undergraduate and graduate students, and of making scientific discoveries available to the general public. This includes the unique educational opportunity for undergraduates to participate in research in Antarctica and engaging the public in several ways, including the development of professionally-produced educational videos with bi-lingual closed captioning. Since the onset of cooling of the SO about 40 million years ago, evolution of Antarctic marine organisms has been driven by the development of cold temperatures. Because body temperatures of Antarctic fishes fall in a narrow range determined by their habitat (-1.9 to +2.0 C), they are particularly attractive models for understanding how organismal physiology and biochemistry have been shaped to maintain life in a cooling environment. Yet these fishes are now threatened by rapid warming of the SO. The long-term objective of this project is to understand the capacities of Antarctic fishes to acclimatize and/or adapt to oceanic warming through analysis of their underlying genetic ""toolkits."" This objective will be accomplished through three Specific Aims: 1) assessing the effects of elevated temperatures on gene expression during development of embryos; 2) examining the effects of elevated temperatures on embryonic morphology and on the temporal and spatial patterns of gene expression; and 3) evaluating the evolutionary mechanisms that have led to the loss of the red blood cell genetic program by the white-blooded fishes. Aims 1 and 2 will be investigated by acclimating experimental embryos of both red-blooded and white-blooded fish to elevated temperatures. Differential gene expression will be examined through the use of high throughput RNA sequencing. The temporal and spatial patterns of gene expression in the context of embryonic morphology (Aim 2) will be determined by microscopic analysis of embryos ""stained"" with (hybridized to) differentially expressed gene probes revealed by Aim 1; other key developmental marker genes will also be used. The genetic lesions resulting from loss of red blood cells by the white-blooded fishes (Aim 3) will be examined by comparing genes and genomes in the two fish groups." proprietary USAP-1542778 Climate History and Flow Processes from Physical Analyses of the SPICECORE South Pole Ice Core AMD_USAPDC STAC Catalog 2016-06-01 2019-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532071857-AMD_USAPDC.umm_json This award supports a three-year effort to study physical properties of the South Pole ice core to help provide a high-time-resolution history of trace gases and other paleoclimatic indicators from an especially cold site with high preservation potential for important signals. The physical-properties studies include visual inspection to identify any flow disturbances and for identifying annual layers and other features, and combined bubble, grain and ice crystal orientation studies to better understand the processes occurring in the ice that affect the climate record and the ice-sheet behavior. Success of these efforts will provide necessary support for dating and quality control to others studying the ice core, as well as determining the climate history of the site, flow state, and key physical processes in ice. The intellectual merits of the project include better understanding of physical processes, paleoclimatic reconstruction, dating of the ice, and quality assurance. Visual inspection of the core will help identify evidence of flow disturbances that would disrupt the integrity of the climate record and will reveal volcanic horizons and other features of interest. Annual layer counting will be conducted to help estimate accumulation rate over time as recorded in the ice core. Measurements of C-axis fabric, grain size and shapes, and bubble characteristics will provide information about processes occurring in the ice sheet as well as the history of ice flow, current flow state and how the ice is flowing and how easily it will flow in the future. Analysis of this data in conjunction with microCT data will help to reveal grain-scale processes. The broader impacts of the project include support for an early-career, post-doctoral researcher, and improved paleoclimatic data of societal relevance. The results will be incorporated into the active program of education and outreach which have educated many students, members of the public and policy makers through the sharing of information and educational materials about all aspects of ice core science and paleoclimate. proprietary USAP-1543383_1 Antarctic Fish and MicroRNA Control of Development and Physiology AMD_USAPDC STAC Catalog 2016-09-01 2019-08-31 -66, -66, -58, -62 https://cmr.earthdata.nasa.gov/search/concepts/C2532072220-AMD_USAPDC.umm_json microRNAs (miRNAs) are key post-transcriptional regulators of gene expression that modulate development and physiology in temperate animals. Although miRNAs act by binding to messenger RNAs (mRNAs), a process that is strongly sensitive to temperature, miRNAs have yet not been studied in Antarctic animals, including Notothenioid fish, which dominate the Southern Ocean. This project will compare miRNA regulation in 1) Antarctic vs. temperate fish to learn the roles of miRNA regulation in adaptation to constant cold; and in 2) bottom-dwelling, dense-boned, red-blooded Nototheniods vs. high buoyancy, osteopenic, white-blooded icefish to understand miRNA regulation in specialized organs after the evolution of the loss of hemoglobin genes and red blood cells, the origin of enlarged heart and vasculature, and the evolution of increased buoyancy, which arose by decreased bone mineralization and increased lipid deposition. Aim 1 is to test the hypothesis that Antarctic fish evolved miRNA-related genome specializations in response to constant cold. The project will compare four Antarctic Notothenioid species to two temperate Notothenioids and two temperate laboratory species to test the hypotheses that (a) Antarctic fish evolved miRNA genome repertoires by loss of ancestral genes and/or gain of new genes, (b) express miRNAs that are involved in cold tolerance, and (c) respond to temperature change by changing miRNA gene expression. Aim 2 is to test the hypothesis that the evolution of icefish from red-blooded bottom-dwelling ancestors was accompanied by an altered miRNA genomic repertoire, sequence, and/or expression. The project will test the hypotheses that (a) miRNAs in icefish evolved in sequence and/or in expression in icefish specializations, including head kidney (origin of red blood cells); heart (changes in vascular system), cranium and pectoral girdle (reduced bone mineral density); and skeletal muscle (lipid deposition), and (b) miRNAs that evolved in icefish specializations had ancestral functions related to their derived roles in icefish, as determined by functional tests of zebrafish orthologs of icefish miRNAs in developing zebrafish. The program will isolate, sequence, and determine the expression of miRNAs and mRNAs using high-throughput transcriptomics and novel software. Results will show how the microRNA system evolves in vertebrate animals pushed to physiological extremes and provide insights into the prospects of key species in the most rapidly warming part of the globe. proprietary -USAP-1543498_1 A Full Lifecycle Approach to Understanding Adélie Penguin Response to Changing Pack Ice Conditions in the Ross Sea AMD_USAPDC STAC Catalog 2016-06-01 165, -78, -150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532074621-AMD_USAPDC.umm_json "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Adélie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and ""NestCheck"" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Adélie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region." proprietary USAP-1543498_1 A Full Lifecycle Approach to Understanding Adélie Penguin Response to Changing Pack Ice Conditions in the Ross Sea ALL STAC Catalog 2016-06-01 165, -78, -150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532074621-AMD_USAPDC.umm_json "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Adélie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and ""NestCheck"" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Adélie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region." proprietary -USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica AMD_USAPDC STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary +USAP-1543498_1 A Full Lifecycle Approach to Understanding Adélie Penguin Response to Changing Pack Ice Conditions in the Ross Sea AMD_USAPDC STAC Catalog 2016-06-01 165, -78, -150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532074621-AMD_USAPDC.umm_json "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Adélie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and ""NestCheck"" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Adélie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region." proprietary USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica ALL STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary +USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica AMD_USAPDC STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary USAP-1643534_1 Biological and Physical Drivers of Oxygen Saturation and Net Community Production Variability along the Western Antarctic Peninsula AMD_USAPDC STAC Catalog 2016-06-15 2023-07-15 -83, -73, -56, -62 https://cmr.earthdata.nasa.gov/search/concepts/C2532075509-AMD_USAPDC.umm_json "This project seeks to make detailed measurements of the oxygen content of the surface ocean along the Western Antarctic Peninsula. Detailed maps of changes in net oxygen content will be combined with measurements of the surface water chemistry and phytoplankton distributions. The project will determine the extent to which on-shore or offshore phytoplankton blooms along the peninsula are likely to lead to different amounts of carbon being exported to the deeper ocean. The project will analyze oxygen in relation to argon that will allow determination of the physical and biological contributions to surface ocean oxygen dynamics. These assessments will be combined with spatial and temporal distributions of nutrients (iron and macronutrients) and irradiances. This will allow the investigators to unravel the complex interplay between ice dynamics, iron and physical mixing dynamics as they relate to Net Community Production (NCP) in the region. NCP measurements will be normalized to Particulate Organic Carbon (POC) and be used to help identify area of ""High Biomass and Low NCP"" and those with ""Low Biomass and High NCP"" as a function of microbial plankton community composition. The team will use machine learning methods- including decision tree assemblages and genetic programming- to identify plankton groups key to facilitating biological carbon fluxes. Decomposing the oxygen signal along the West Antarctic Peninsula will also help elucidate biotic and abiotic drivers of the O2 saturation to further contextualize the growing inventory of oxygen measurements (e.g. by Argo floats) throughout the global oceans." proprietary USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core AMD_USAPDC STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core ALL STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary @@ -15631,10 +15631,10 @@ USAP-1644073_1 Collaborative Research: Cobalamin and Iron Co-Limitation Of Phyt USAP-1644197_1 Collaborative Research: New Constraints on Post-Glacial Rebound and Holocene Environmental History along the Northern Antarctic Peninsula from Raised Beaches AMD_USAPDC STAC Catalog 2017-08-08 2021-08-31 -65, -65, -55, -61 https://cmr.earthdata.nasa.gov/search/concepts/C2605088269-AMD_USAPDC.umm_json Glacier ice loss from Antarctica has the potential to lead to a significant rise in global sea level. One line of evidence for accelerated glacier ice loss has been an increase in the rate at which the land has been rising across the Antarctic Peninsula as measured by GPS receivers. However, GPS observations of uplift are limited to the last two decades. One goal of this study is to determine how these newly observed rates of uplift compare to average rates of uplift across the Antarctic Peninsula over a longer time interval. Researchers reconstructed past sea levels using the age and elevation of ancient beaches now stranded above sea level on the low-lying coastal hills of the Antarctica Peninsula and determined the rate of uplift over the last 5,000 years. The researchers analyzed the structure of the beaches using ground-penetrating radar and the characteristics of beach sediments to understand how sea-level rise and past climate changes are recorded in beach deposits. We found that unlike most views of how sea level changed across Antarctica over the last 5,000 years, its history is complex with periods of increasing rates of sea-level fall as well as short periods of potential sea-level rise. We attribute these oscillations in the nature of sea-level change across the Antarctic Peninsula to changes in the ice sheet over the last 5,000 years. These changes in sea level also suggest our understanding of the Earth structure beneath the Antarctic Peninsula need to be revised. The beach deposits themselves also record periods of climate change as reflected in the size and shape of their cobbles. This project has lead to the training of five graduate students, three undergraduate students, and outreach talks to k-12 schools in three communities. proprietary USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus ALL STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus AMD_USAPDC STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary -USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition AMD_USAPDC STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition ALL STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary -USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean AMD_USAPDC STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary +USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition AMD_USAPDC STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean ALL STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary +USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean AMD_USAPDC STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary USAP-1744828_1 Collaborative Proposal: A High-Latitude Conjugate Area Array Experiment to Investigate Solar Wind - Magnetosphere - Ionosphere Coupling AMD_USAPDC STAC Catalog 2018-08-15 2022-07-31 6, -85, 89, -69 https://cmr.earthdata.nasa.gov/search/concepts/C2532075157-AMD_USAPDC.umm_json This proposal is directed toward an investigation of the coupling phenomena between the solar wind and the Earth's magnetosphere and ionosphere, particularly on the day side of the Earth and observed simultaneously at high latitudes in both northern and southern hemispheres. Through past NSF support, several magnetometers have been deployed in Antarctica, Greenland, and Svalbard, while new collaborations have been developed with the Polar Research Institute of China (PRIC) to further increase coverage through data sharing. This project will expand the existing Virginia Tech-PRIC partnership to include New Jersey Institute of Technology, University of New Hampshire, and the Technical University of Denmark and (1) construct two new stations to be deployed by PRIC along a chain from Zhongshan station to Dome A to complete a conjugate area array, (2) integrate data from all stations into a common format, and (3) address two focused science questions. Both instrument deployment and data processing efforts are motivated by a large number of solar wind-magnetosphere-ionosphere (SWMI) coupling science questions; this project will address two questions pertaining to Ultra Low Frequency (ULF) waves: (1) What is the global ULF response to Hot Flow Anomalies (HFA) and how is it affected by asymmetries in the SWMI system? (2) How do dawn-dusk and north-south asymmetries in the coupled SWMI system affect global ULF wave properties during periods with large, steady east-west Interplanetary Magnetic field (IMF By)? This proposal requires fieldwork in the Antarctic, but all fieldwork will be conducted by PRIC. proprietary USAP-1744961_1 Atmospheric Mineral Nanoparticles in Antarctic Ice during the last Climatic Cycle AMD_USAPDC STAC Catalog 2018-08-15 2024-07-31 161.711586, -77.7602, 161.728, -77.75758 https://cmr.earthdata.nasa.gov/search/concepts/C3386889612-AMD_USAPDC.umm_json "The main goal of this project is to identify and geochemically characterize atmospheric mineral nanoparticles and fine microparticles in pre-industrial Antarctic ice during the last climatic cycle. Recent technological and industrial development is introducing a large number of natural and engineered nanoparticles and fine microparticles into Earth's atmosphere. These constitute a concern for human health, mainly due to their high chemical reactivity. While many atmospheric nanoparticle studies have been performed in modern urban environments, there is essentially no information about their occurrence in a pristine pre-industrial atmosphere. This information is critical, as it constitutes an important benchmark for comparison to the modern atmosphere. Information on nanoparticles from the pre-industrial atmosphere can be obtained from atmospheric mineral nanoparticles that are entrapped in remote pre-industrial Antarctic ice covering the last climatic cycles. Mineral nanoparticles and fine microparticles can also affect several climatic processes. First, they directly influence the global energy balance by reflecting solar radiation and indirectly influence through changes in cloud formation (and clouds also reflect solar radiation). Second, atmospheric mineral nanoparticles such as iron oxides could have fertilized the oceans, causing blooms of marine phytoplankton that may have drawn part of the atmospheric carbon dioxide into the oceans during glacial ages (the ""biological pump""). Third, a significant amount of extraterrestrial material entering the Earth atmosphere is thought to be transported to the poles as nanoparticles called ""meteoric smoke"" that form polar stratospheric clouds implicated in changes of the ozone hole. This project aims to establish the natural background of unknown classes of glacial particles whose size is below the detection limit of the conventional dust analyzers. The team will take advantage of ice samples from the ""horizontal ice core"", already extracted from the remote Taylor Glacier (coastal East Antarctica) covering the last ~44,000 years. These ancient samples are particularly suited to project scope because i) a large ice volume is available ii) the team expects to find a markedly different geochemistry between nanoparticles deposited during the last glacial age and during the current interglacial. A set of advanced techniques including Transmission Electron Microscopy, Single Particle Inductively Coupled Plasma Mass Spectrometry (spICP-MS), and spICP-Time of Flight MS will be employed to determine mineral nanoparticle and fine microparticle sizes, number/volume, and chemical composition. So far, the elemental composition of dust entrapped in polar ice has been mainly determined by Inductively Coupled Plasma Sector Field Mass Spectrometry and it is generally assumed to be descriptive of the coarse aeolian dust fraction. However, project will test whether or not the determined elemental composition is instead mainly linked to the previously unobserved smaller mineral nanoparticle content. Results on nanoparticles will be compared with a set of new experiments of total dust composition measured by Inductively Coupled Plasma Sector Field Mass Spectrometry, using the same ice samples from Taylor Glacier. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria." proprietary USAP-1744989_1 A Multi-scale Approach to Understanding Spatial and Population Variability in Emperor Penguins AMD_USAPDC STAC Catalog 2018-07-15 2022-06-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.umm_json This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public. proprietary @@ -15651,8 +15651,8 @@ USAP-1933764_1 Antarctic Submarine Melt Variability from Remote Sensing of Icebe USAP-1935635_1 ANT LIA Collaborative Research: Interrogating Molecular and Physiological Adaptations in Antarctic Marine Animals. AMD_USAPDC STAC Catalog 2020-06-01 2023-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075069-AMD_USAPDC.umm_json Understanding the genomic changes underlying adaptations to polar environments is critical for predicting how ecological changes will affect life in these fragile environments. Accomplishing these goals requires looking in detail at genome-scale data across a wide array of organisms in a phylogenetic framework. This study combines multifaceted computational and functional approaches that involves analyzing in the genic evolution of invertebrate organisms, known as the bryozoans or ectoprocts. In addition, the commonality of our results in other taxa will be tested by comparing the results to those produced from the previous and newly proposed workshops. Specific aims of this study include: 1) identifying genes involved in adaptation to Antarctic marine environments using transcriptomic and genomic data from bryozoans to test for positively selected genes in a phylogenetic framework, 2) experimentally testing identified candidate enzymes (especially those involved in calcium signaling, glycolysis, the citric acid cycle, and the cytoskeleton) for evidence of cold adaption, and 3) conducting computational workshops aimed at training scientists in techniques for the identification of genetic adaptations to polar and other disparate environments. The proposed work provides critical insights into the molecular rules of life in rapidly changing Antarctic environments, and provides important information for understanding how Antarctic taxa will respond to future environmental conditions. proprietary USAP-1937546_1 ANT LIA: Collaborative Research: Genetic Underpinnings of Microbial Interactions in Chemically Stratified Antarctic Lakes AMD_USAPDC STAC Catalog 2020-09-15 2023-08-31 162, -77.733333, 163, -77.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2544479199-AMD_USAPDC.umm_json Microbial communities are of more than just a scientific curiosity. Microbes represent the single largest source of evolutionary and biochemical diversity on the planet. They are the major agents for cycling carbon, nitrogen, phosphorus, and other elements through the ecosystem. Despite their importance in ecosystem function, microbes are still generally overlooked in food web models and nutrient cycles. Moreover, microbes do not live in isolation: their growth and metabolism are influenced by complex interactions with other microorganisms. This project will focus on the ecology, activity and roles of microbial communities in Antarctic Lake ecosystems. The team will characterize the genetic underpinnings of microbial interactions and the influence of environmental gradients (e.g. light, nutrients, oxygen, sulfur) and seasons (e.g. summer vs. winter) on microbial networks in Lake Fryxell and Lake Bonney in the Taylor Valley within the McMurdo Dry Valley region. Finally, the project furthers the NSF goals of training new generations of scientists by including undergraduate and graduate students, a postdoctoral researcher and a middle school teacher in both lab and field research activities. This partnership will involve a number of other outreach training activities, including visits to classrooms and community events, participation in social media platforms, and webinars. Part II: Technical description: Ecosystem function in the extreme Antarctic Dry Valleys ecosystem is dependent on complex biogeochemical interactions between physiochemical environmental factors (e.g. light, nutrients, oxygen, sulfur), time of year (e.g. summer vs. winter) and microbes. Microbial network complexity can vary in relation to specific abiotic factors, which has important implications on the fragility and resilience of ecosystems under threat of environmental change. This project will evaluate the influence of biogeochemical factors on microbial interactions and network complexity in two Antarctic ice-covered lakes. The study will be structured by three main objectives: 1) infer positive and negative interactions from rich spatial and temporal datasets and investigate the influence of biogeochemical gradients on microbial network complexity using a variety of molecular approaches; 2) directly observe interactions among microbial eukaryotes and their partners using flow cytometry, single-cell sorting and microscopy; and 3) develop metabolic models of specific interactions using metagenomics. Outcomes from amplicon sequencing, meta-omics, and single-cell genomic approaches will be integrated to map specific microbial network complexity and define the role of interactions and metabolic activity onto trends in limnological biogeochemistry in different seasons. These studies will be essential to determine the relationship between network complexity and future climate conditions. To further increase polar literacy training and educational impacts, the field team will include a teacher as part of a collaboration with the successful NSF-funded PolarTREC program and participation in activities designed for public outreach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. proprietary USAP-1945127_1 CAREER: The Transformation, Cross-shore Export, and along-shore Transport of Freshwater on Antarctic Shelves AMD_USAPDC STAC Catalog 2020-06-01 2025-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075621-AMD_USAPDC.umm_json Freshwater discharges from melting high-latitude continental ice glacial reserves strongly control salt budgets, circulation and associated ocean water mass formation arising from polar ice shelves. These are different in nature than freshwater inputs associated with riverine coastal inputs. The PI proposes an observational deployment to measure a specific, previously-identified example of a coastal freshwater-driven current, the Antarctic Peninsula Coastal Current (APCC). The research component of this CAREER project aims to improve understanding of the dynamics of freshwater discharge around the Antarctic continent. Associated research questions pertain to the i) controls on the cross- and along-shelf spreading of fresh, buoyant coastal currents, ii) the role of distributed coastal freshwater sources (as opposed to 'point' source river outflow sources typical of lower latitudes), and iii) the contribution of these coastal currents to water mass transformation and heat transfer on the continental shelf. An educational CAREER program component leverages a series of field experiences and research outputs including data, model outputs, and theory, to bring polar science to the classroom and the general public, as well as training a new polar scientist. This combined strategy will allow the investigator to lay the foundation for a successful academic career as a researcher and teacher at the University of Delaware. The project will also provide the opportunity to train a PhD student. Informal outreach efforts will include giving public lectures at University of Deleware's sponsored events, including Coast Day, a summer event that attracts 8000-10000 people, and remote lectures from the field using an existing outreach network. This proposal requires fieldwork in the Antarctic. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. proprietary -USAP-1947094_1 A non-amniote perspective on the recovery from the end-Permian extinction at high latitudes: paleobiology of Early Triassic temnospondyls from Antarctica ALL STAC Catalog 2020-05-01 2022-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075035-AMD_USAPDC.umm_json The research supported by this grant centers on the evolution of fossil amphibians (temnospondyls) from the Early Triassic, a crucial time interval in the evolution of life on Earth following the end-Permian mass extinction, specifically based on fossil material from Antarctica, a high-latitude paleoenvironment that may have served as a refuge for tetrapods across the extinction event. Previous records of temnospondyls, mostly reported several decades ago, are highly fragmentary, and their original interpretations are considered dubious or demonstrably erroneous by contemporary workers. The Antarctic record of temnospondyls is of great import in understanding the biotic recovery in terrestrial environments for several reasons. Firstly, temnospondyls, like amphibians today, were highly speciose in the Triassic but were also some of the most susceptible to environmental perturbations and instability. Therefore, temnospondyls provide key insights into the paleoenvironmental conditions, either in place of or alongside other lines of data. Secondly, the record of temnospondyls from the Early Triassic is quite rich, but it is also restricted to a few densely sampled regions, such as the Karoo Basin of South Africa. In order to ascertain whether observed patterns such as an unusual abundance of small-bodied taxa or a lack of faunal overlap between different depositional basins (endemism) are real or merely artifactual, study of additional, less sampled regions takes on great import. Recent collection of substantial new temnospondyl material from several horizons in the Triassic exposure of Antarctica provides the requisite data to begin to address these questions. Finally, correlating the Triassic rocks of Antarctica with those of adjacent regions is largely reliant on comparisons of faunal assemblages. In particular, the middle Fremouw Formation, one of the horizons from which new temnospondyl material was collected, remains of uncertain relation and age due to the paucity of described material. proprietary USAP-1947094_1 A non-amniote perspective on the recovery from the end-Permian extinction at high latitudes: paleobiology of Early Triassic temnospondyls from Antarctica AMD_USAPDC STAC Catalog 2020-05-01 2022-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075035-AMD_USAPDC.umm_json The research supported by this grant centers on the evolution of fossil amphibians (temnospondyls) from the Early Triassic, a crucial time interval in the evolution of life on Earth following the end-Permian mass extinction, specifically based on fossil material from Antarctica, a high-latitude paleoenvironment that may have served as a refuge for tetrapods across the extinction event. Previous records of temnospondyls, mostly reported several decades ago, are highly fragmentary, and their original interpretations are considered dubious or demonstrably erroneous by contemporary workers. The Antarctic record of temnospondyls is of great import in understanding the biotic recovery in terrestrial environments for several reasons. Firstly, temnospondyls, like amphibians today, were highly speciose in the Triassic but were also some of the most susceptible to environmental perturbations and instability. Therefore, temnospondyls provide key insights into the paleoenvironmental conditions, either in place of or alongside other lines of data. Secondly, the record of temnospondyls from the Early Triassic is quite rich, but it is also restricted to a few densely sampled regions, such as the Karoo Basin of South Africa. In order to ascertain whether observed patterns such as an unusual abundance of small-bodied taxa or a lack of faunal overlap between different depositional basins (endemism) are real or merely artifactual, study of additional, less sampled regions takes on great import. Recent collection of substantial new temnospondyl material from several horizons in the Triassic exposure of Antarctica provides the requisite data to begin to address these questions. Finally, correlating the Triassic rocks of Antarctica with those of adjacent regions is largely reliant on comparisons of faunal assemblages. In particular, the middle Fremouw Formation, one of the horizons from which new temnospondyl material was collected, remains of uncertain relation and age due to the paucity of described material. proprietary +USAP-1947094_1 A non-amniote perspective on the recovery from the end-Permian extinction at high latitudes: paleobiology of Early Triassic temnospondyls from Antarctica ALL STAC Catalog 2020-05-01 2022-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075035-AMD_USAPDC.umm_json The research supported by this grant centers on the evolution of fossil amphibians (temnospondyls) from the Early Triassic, a crucial time interval in the evolution of life on Earth following the end-Permian mass extinction, specifically based on fossil material from Antarctica, a high-latitude paleoenvironment that may have served as a refuge for tetrapods across the extinction event. Previous records of temnospondyls, mostly reported several decades ago, are highly fragmentary, and their original interpretations are considered dubious or demonstrably erroneous by contemporary workers. The Antarctic record of temnospondyls is of great import in understanding the biotic recovery in terrestrial environments for several reasons. Firstly, temnospondyls, like amphibians today, were highly speciose in the Triassic but were also some of the most susceptible to environmental perturbations and instability. Therefore, temnospondyls provide key insights into the paleoenvironmental conditions, either in place of or alongside other lines of data. Secondly, the record of temnospondyls from the Early Triassic is quite rich, but it is also restricted to a few densely sampled regions, such as the Karoo Basin of South Africa. In order to ascertain whether observed patterns such as an unusual abundance of small-bodied taxa or a lack of faunal overlap between different depositional basins (endemism) are real or merely artifactual, study of additional, less sampled regions takes on great import. Recent collection of substantial new temnospondyl material from several horizons in the Triassic exposure of Antarctica provides the requisite data to begin to address these questions. Finally, correlating the Triassic rocks of Antarctica with those of adjacent regions is largely reliant on comparisons of faunal assemblages. In particular, the middle Fremouw Formation, one of the horizons from which new temnospondyl material was collected, remains of uncertain relation and age due to the paucity of described material. proprietary USAP-1947562_1 Antarctica as a Model System for Responses of Terrestrial Carbon Balance to Warming AMD_USAPDC STAC Catalog 2022-01-01 2026-12-31 -65, -65, -63, -64.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532075152-AMD_USAPDC.umm_json Responses of the carbon balance of terrestrial ecosystems to warming will feed back to the pace of climate change, but the size and direction of this feedback are poorly constrained. Least known are the effects of warming on carbon losses from soil, and clarifying the major microbial controls is an important research frontier. This study uses a series of experiments and observations to investigate microbial, including autotrophic taxa, and plant controls of net ecosystem productivity in response to warming in intact ecosystems. Field warming is achieved using open-top chambers paired with control plots, arrayed along a productivity gradient. Along this gradient incoming and outgoing carbon fluxes will be measured at the ecosystem-level. The goal is to tie warming-induced shifts in net ecosystem carbon balance to warming effects on soil microbes and plants. The field study will be supplemented with lab temperature incubations. Because soil microbes dominate biogeochemical cycles in Antarctica, a major focus of this study is to determine warming responses of bacteria, fungi and archaea. This is achieved using a cutting-edge stable isotope technique, quantitative stable isotope probing (qSIP) developed by the proposing research team, that can identify the taxa that are active and involved in processing new carbon. This technique can identify individual microbial taxa that are actively participating in biogeochemical cycling of nutrients (through combined use of 18O-water and 13C-bicarbonate) and thus can be distinguished from those that are simply present (cold-preserved). The study further assesses photosynthetic uptake of carbon by the vegetation and their sensitivity to warming. Results will advance research in climate change, plant and soil microbial ecology, and ecosystem modeling. proprietary USAP-1947646_1 Collaborative Proposal: Miocene Climate Extremes: A Ross Sea Perspective from IODP Expedition 374 and DSDP Leg 28 Marine Sediments AMD_USAPDC STAC Catalog 2020-05-01 2023-04-30 164, -79, -156, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532075622-AMD_USAPDC.umm_json Presently, Antarctica's glaciers are melting as Earth's atmosphere and the Southern Ocean warm. Not much is known about how Antarctica's ice sheets might respond to ongoing and future warming, but such knowledge is important because Antarctica's ice sheets might raise global sea levels significantly with continued melting. Over time, mud accumulates on the sea floor around Antarctica that is composed of the skeletons and debris of microscopic marine organisms and sediment from the adjacent continent. As this mud is deposited, it creates a record of past environmental and ecological changes, including ocean depth, glacier advance and retreat, ocean temperature, ocean circulation, marine ecosystems, ocean chemistry, and continental weathering. Scientists interested in understanding how Antarctica's glaciers and ice sheets might respond to ongoing warming can use a variety of physical, biological, and chemical analyses of these mud archives to determine how long ago the mud was deposited and how the ice sheets, oceans, and marine ecosystems responded during intervals in the past when Earth's climate was warmer. In this project, researchers from the University of South Florida, University of Massachusetts, and Northern Illinois University will reconstruct the depth, ocean temperature, weathering and nutrient input, and marine ecosystems in the central Ross Sea from ~17 to 13 million years ago, when the warm Miocene Climate Optimum transitioned to a cooler interval with more extensive ice sheets. Record will be generated from new sediments recovered during the International Ocean Discovery Program (IODP) Expedition 374 and legacy sequences recovered in the 1970s during the Deep Sea Drilling Program. Results will be integrated into ice sheet and climate models to improve the accuracy of predictions. proprietary USAP-1951603_1 Antarctic Meteorological Research and Data Center AMD_USAPDC STAC Catalog 2020-07-01 2025-07-01 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075146-AMD_USAPDC.umm_json The Antarctic Meteorological Research and Data Center (AMRDC) project will create an Antarctic meteorological observational data repository and archive system based on an open source platform to manage data from submission to end-user retrieval. The new archival system will host both currently available datasets and campaign meteorological datasets deposited by other Antarctic investigators. Both real-time meteorological data and archive data from the repository (e.g. Antarctic composite satellite imagery, AWS observations, etc.) will be accessible on a newly constructed website. The project will engage undergraduate and graduate students in order to provide them with meaningful experiences that can translate to any science, technology, engineering, and mathematics (STEM) career path. Project participants and students will be involved in case studies, climatology reporting and development of whitepapers on related topics. The outcomes of this project revolve around data, and the students, researchers, and decision makers who all use and rely on Antarctic meteorological data. The AMRDC will not only be a resource for users, but it will also provide investigators a repository to place campaign datasets and meet NSF standards and requirements. This project also aims to give students Antarctic field experiences who are considering a career in science, technology, engineering and mathematics (STEM). proprietary @@ -15662,8 +15662,8 @@ USAP-2046240_1 CAREER: Coastal Antarctic Snow Algae and Light Absorbing Particle USAP-2046437_1 CAREER: Development of Unmanned Ground Vehicles for Assessing the Health of Secluded Ecosystems (ECHO) AMD_USAPDC STAC Catalog 2021-09-01 2026-08-31 -60, -80, 10, -55 https://cmr.earthdata.nasa.gov/search/concepts/C2532075144-AMD_USAPDC.umm_json Polar ecosystems currently experience significant impacts due to global changes. Measurable negative effects on polar wildlife have already occurred, such as population decreases of numerous seabird species, including the complete loss of colonies of one of the most emblematic species of the Antarctic, the emperor penguin. These existing impacts on polar species are alarming, especially because many polar species still remain poorly studied due to technical and logistical challenges imposed by the harsh environment and extreme remoteness. Developing technologies and tools for monitoring such wildlife populations is, therefore, a matter of urgency. This project aims to help close major knowledge gaps about the emperor penguin, in particular about their adaptive capability to a changing environment, by the development of next-generation tools to remotely study entire colonies. Specifically, the main goal of this project is to implement and test an autonomous unmanned ground vehicle equipped with Radio-frequency identification (RFID) antennas and wireless mesh communication data-loggers to: 1) identify RFID-tagged emperor penguins during breeding to studying population dynamics without human presence; and 2) receive GPS-TDR datasets from VHF-GPS-TDR data-loggers without human presence to study animal behavior and distribution at sea. The autonomous vehicles navigation through the colony will be aided by an existing remote penguin observatory (SPOT). Properly implemented, this technology can be used to study of the life history of individual penguins, and therefore gather data for behavioral and population dynamic studies. The education objectives of this CAREER project are designed to increase the interest in a STEM education for the next generation of scientists by combining the charisma of the emperor penguin with robotics research. Within this project, a new class on ecosystem robotics will be developed and taught, Robotics boot-camps will allow undergraduate students to remotely participate in Antarctic field trips, and an annual curriculum will be developed that allows K-12 students to follow the life of the emperor penguin during the breeding cycle, powered by real-time data obtained using the unmanned ground vehicle as well as the existing emperor penguin observatory. proprietary USAP-2046800_1 CAREER: Ecosystem Impacts of Microbial Succession and Production at Antarctic Methane Seeps AMD_USAPDC STAC Catalog 2022-01-01 2026-12-31 162, -78, 168, -77 https://cmr.earthdata.nasa.gov/search/concepts/C2532075149-AMD_USAPDC.umm_json Due to persistent cold temperatures, geographical isolation, and resulting evolutionary distinctness of Southern Ocean fauna, the study of Antarctic reducing habitats has the potential to fundamentally alter our understanding of the biologic processes that inhibit greenhouse gas emissions from our oceans. Marine methane, a greenhouse gas 25x as potent as carbon dioxide for warming our atmosphere, is currently a minor component of atmospheric forcing due to the microbial oxidation of methane within the oceans. Based on studies of persistent deep-sea seeps at mid- and northern latitudes we have learned that bacteria and archaea create a ‘sediment filter’ that oxidizes methane prior to its release. As increasing global temperatures have and will continue to alter the rate and variance of methane release, the ability of the microbial filter to respond to fluctuations in methane cycles is a critical yet unexplored avenue of research. Antarctica contains vast reservoirs of methane, equivalent to all of the permafrost in the Arctic, and yet we know almost nothing about the fauna that may mitigate its release, as until recently, we had not discovered an active methane seep. In 2012, a methane seep was discovered in the Ross Sea, Antarctica that formed in 2011 providing the first opportunity to study an active Antarctic methane-fueled habitat and simultaneously the impact of microbial succession on the oxidation of methane, a critical ecosystem service. Previous work has shown that after 5 years of seepage, the community was at an early stage of succession and unable to mitigate the release of methane from the seafloor. In addition, additional areas of seepage had begun nearby. This research aims to quantify the community trajectory of these seeps in relation to their role in the Antarctic Ecosystem, from greenhouse gas mitigation through supporting the food web. Through the application of genomic and transcriptomic approaches, taxa involved in methane cycling and genes activated by the addition of methane will be identified and contrasted with those from other geographical locations. These comparisons will elucidate how taxa have evolved and adapted to the polar environment. This research uses a ‘genome to ecosystem’ approach to advance our understanding of organismal and systems ecology in Antarctica. By quantifying the trajectory of community succession following the onset of methane emission, the research will decipher temporal shifts in biodiversity/ecosystem function relationships. Phylogenomic approaches focusing on taxa involved in methane cycling will advance the burgeoning field of microbial biogeography on a continent where earth’s history may have had a profound yet unquantified impact on microbial evolution. Further, the research will empirically quantify the role of chemosynthesis as a form of export production from seeps and in non-seep habitats in the nearshore Ross Sea benthos, informing our understanding of Antarctic carbon cycling. proprietary USAP-2055455_1 ANT LIA - Viral Ecogenomics of the Southern Ocean: Unifying Omics and Ecological Networks to Advance our Understanding of Antarctic Microbial Ecosystem Function AMD_USAPDC STAC Catalog 2021-05-01 2024-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532075626-AMD_USAPDC.umm_json "Part 1: Non-technical description: It is well known that the Southern Ocean plays an important role in global carbon cycling and also receives a disproportionately large influence of climate change. The role of marine viruses on ocean productivity is largely understudied, especially in this global region. This team proposes to use combination of genomics, flow cytometry, and network modeling to test the hypothesis that viral biogeography, infection networks, and viral impacts on microbial metabolism can explain variations in net community production (NCP) and carbon cycling in the Southern Ocean. The project includes the training of a postdoctoral scholar, graduate students and undergraduate students. It also includes the development of a new Polar Sci ReachOut program in partnership with the University of Michigan Museum of Natural History especially targeted to middle-school students and teachers and the general public. The team will also produce a Science for Tomorrow (SFT) program for use in middle schools in metro-Detroit communities and lead a summer Research Experience for Teachers (RET) fellows. Part 2: Technical description: The study will leverage hundreds of existing samples collected for microbes and viruses from the Antarctic Circumpolar Expedition (ACE). These samples provide the first contiguous survey of viral diversity and microbial communities around Antarctica. Viral networks are being studied in the context of biogeochemical data to model community networks and predict net community production (NCP), which will provide a way to evaluate the role of viruses in Southern Ocean carbon cycling. Using cutting edge molecular and flow cytometry approaches, this project addresses the following questions: 1) How/why are Southern Ocean viral populations distributed across environmental gradients? 2a) Do viruses interfere with ""keystone"" metabolic pathways and biogeochemical processes of microbial communities in the Southern Ocean? 2b) Does nutrient availability or other environmental variables drive changes in virus-microbe infection networks in the Southern Ocean? Results will be used to develop and evaluate generative models of NCP predictions that incorporate the importance of viral traits and virus-host interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria." proprietary -USAP-2130663_1 2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science ALL STAC Catalog 2021-06-01 2023-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2556670196-AMD_USAPDC.umm_json Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation. This workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. proprietary USAP-2130663_1 2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science AMD_USAPDC STAC Catalog 2021-06-01 2023-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2556670196-AMD_USAPDC.umm_json Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation. This workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. proprietary +USAP-2130663_1 2021 Antarctic Subsea Cable Workshop: High-Speed Connectivity Needs to Advance US Antarctic Science ALL STAC Catalog 2021-06-01 2023-05-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2556670196-AMD_USAPDC.umm_json Current networking capacity at McMurdo Station is insufficient to even be considered broadband, with a summer population of up to 1000 people sharing what is equivalent to the connection enjoyed by a typical family of three in the United States. The changing Antarctic ice sheets and Southern Ocean are large, complex systems that require cutting edge technology to do cutting edge research, with remote technology becoming increasingly useful and even necessary to monitor changes at sufficient spatial and temporal scales. Antarctic science also often involves large data-transfer needs not currently met by existing satellite communication infrastructure. This workshop will gather representatives from across Antarctic science disciplinesfrom astronomy to zoologyas well as research and education networking experts to explore the scientific advances that would be enabled through dramatically increased real-time network connectivity, and also consider opportunities for subsea cable instrumentation. This workshop will assess the importance of a subsea fiber optic cable for high-capacity real-time connectivity in the US Antarctic Program, which is at the forefront of some of the greatest climate-related challenges facing our planet. The workshop will: (1) document unmet or poorly met current scientific and logistic needs for connectivity; (2) explore connectivity needs for planned future research and note the scientific advances that would be possible if the full value of modern cyberinfrastructure-empowered research could be brought to the Antarctic research community; and (3) identify scientific opportunities in planning a fully instrumented communication cable as a scientific observatory. Due to the ongoing COVID-19 pandemic, the workshop will be hosted and streamed online. While the workshop will be limited to invited personnel in order to facilitate a collaborative working environment, broad community input will be sought via survey and via comment on draft outputs. A workshop summary document and report will be delivered to NSF. Increasing US Antarctic connectivity by orders of magnitude will be transformative for science and logistics, and it may well usher in a new era of Antarctic science that is more accessible, efficient and sustainable. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. proprietary USAP-2132641_1 ANT LIA: Do Molecular Data Support High Endemism and Divergent Evolution of Antarctic Marine Nematodes and their Host-associated Microbiomes? AMD_USAPDC STAC Catalog 2022-07-15 2026-06-30 -180, -80, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2544555474-AMD_USAPDC.umm_json Nematode worms are abundant and ubiquitous in marine sediment habitats worldwide, performing key functions such as nutrient cycling and sediment stability. However, study of this phylum suffers from a perpetual and severe taxonomic deficit, with less than 5,000 formally described marine species. Fauna from the Southern Ocean are especially poorly studied due to limited sampling and the general inaccessibility of the Antarctic benthos. This study is providing the first large-scale molecular-based investigation from marine nematodes in the Eastern Antarctic continental shelf, providing an important comparative dataset for the existing body of historical (morphological) taxonomic studies. This project uses a combination of classical taxonomy (microscopy) and modern -omics tools to achieve three overarching aims: 1) determine if molecular data supports high biodiversity and endemism of benthic meiofauna in Antarctic benthic ecosystems; 2) determine the proportion of marine nematode species that have a deep-sea versus shallow-water evolutionary origin on the Antarctic shelf, and assess patterns of cryptic speciation in the Southern Ocean; and 3) determine the most important drivers of the host-associated microbiome in Antarctic marine nematodes. This project is designed to rapidly advance knowledge of the evolutionary origins of Antarctic meiofauna, provide insight on population-level patterns within key indicator genera, and elucidate the potential ecological and environmental factors which may influence microbiome patterns. Broader Impacts activities include an intensive cruise- and land-based outreach program focusing on social media engagement and digital outreach products, raising awareness of Antarctic marine ecosystems and understudied microbial-animal relationships. The diverse research team includes female scientists, first-generation college students, and Latinx trainees. proprietary USAP-2133684_1 Collaborative Research: ANT LIA Integrating Genomic and Phenotypic Analyses to understand Microbial Life in Antarctic Soils AMD_USAPDC STAC Catalog 2022-04-01 2025-03-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2660035273-AMD_USAPDC.umm_json Not all of Antarctica is covered in ice. In fact, soils are common to many parts of Antarctica, and these soils are often unlike any others found on Earth. Antarctic soils harbor unique microorganisms able to cope with the extremely cold and dry conditions common to much of the continent. For decades, microbiologists have been drawn to the unique soils in Antarctica, yet critical knowledge gaps remain. Most notably, it is unclear what properties allow certain microbes to thrive in Antarctic soils. By using a range of methods, this project is developing comprehensive model that discovers the unique genomic features of soils diversity, distributions, and adaptations that allow Antarctic soil microbes to thrive in extreme environments. The proposed work will be relevant to researchers in many fields, including engineers seeking to develop new biotechnologies, ecologists studying the contributions of these microbial communities to the functioning of Antarctic ecosystems, microbiologists studying novel microbial adaptations to extreme environmental conditions, and even astrobiologists studying the potential for life on Mars. More generally, the proposed research presents an opportunity to advance our current understanding of the microbial life found in one of the more distinctive microbial habitats on Earth, a habitat that is inaccessible to many scientists and a habitat that is increasingly under threat from climate change. The research project explores the microbial diversity in Antarctic soils and links specific features to different soil types and environmental conditions. The overarching questions include: What microbial taxa are found in a variety of Antarctic environments? What are the environmental preferences of specific taxa or lineages? What are the genomic and phenotypic traits of microorganisms that allow them to persist in extreme environments and determine biogeographical differneces? This project will analyze archived soils collected from across Antarctica by a network of international collaborators, with samples selected to span broad gradients in soil and site conditions. The project uses cultivation-independent, high-throughput genomic analysis methods and cultivation-dependent approaches to analyze bacterial and fungal communities in soil samples. The results will be used to predict the distributions of specific taxa and lineages, obtain genomic information for the more ubiquitous and abundant taxa, and quantify growth responses in vitro across gradients in temperature, moisture, and salinity. This integration of ecological, environmental, genomic, and trait-based information will provide a comprehensive understanding of microbial life in Antarctic soils. This project will also help facilitate new collaborations between scientists across the globe while providing undergraduate students with ''hands-on'' research experiences that introduce the next generation of scientists to the field of Antarctic biology. This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria. proprietary USAP-2141555_1 CAREER: Using Otolith Chemistry to Reveal the Life History of Antarctic Toothfish in the Ross Sea, Antarctica: Testing Fisheries and Climate Change Impacts on a Top Fish Predator AMD_USAPDC STAC Catalog 2022-05-01 2027-04-30 161, -79, -151, -71.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532075614-AMD_USAPDC.umm_json The Ross Sea, Antarctica, is one of the last large intact marine ecosystems left in the world, yet is facing increasing pressure from commercial fisheries and environmental change. It is the most productive stretch of the Southern Ocean, supporting an array of marine life, including Antarctic toothfish the regions top fish predator. While a commercial fishery for toothfish continues to grow in the Ross Sea, fundamental knowledge gaps remain regarding toothfish ecology and the impacts of toothfish fishing on the broader Ross Sea ecosystem. Recognizing the global value of the Ross Sea, a large (>2 million km2) marine protected area was adopted by the multi-national Commission for the Conservation of Antarctic Marine Living Resources in 2016. This research will fill a critical gap in the knowledge of Antarctic toothfish and deepen understanding of biological-physical interactions for fish ecology, while contributing to knowledge of impacts of fishing and environmental change on the Ross Sea system. This work will further provide innovative tools for studying connectivity among geographically distinct fish populations and for synthesizing and assessing the efficacy of a large-scale marine protected area. In developing an integrated research and education program in engaged scholarship, this project seeks to train the next generation of scholars to engage across the science-policy-public interface, engage with Southern Ocean stakeholders throughout the research process, and to deepen the publics appreciation of the Antarctic. A major research priority among Ross Sea scientists is to better understand the life history of the Antarctic toothfish and test the efficacy of the Ross Sea Marine Protected Area (MPA) in protecting against the impacts of overfishing and climate change. Like growth rings of a tree, fish ear bones, called otoliths, develop annual layers of calcium carbonate that incorporates elements from their environment. Otoliths offer information on the fishs growth and the surrounding ocean conditions. Hypothesizing that much of the Antarctic toothfish life cycle is structured by ocean circulation, this research employs a multi-disciplinary approach combining age and growth work with otolith chemistry testing, while also utilizing GIS mapping. The project will measure life history parameters as well as trace elements and stable isotopes in otoliths in three distinct sets collected over the last four decades in the Ross Sea. The information will be used to quantify the transport pathways Antarctic toothfish use across their life history, and across time, in the Ross Sea. The project will assess if toothfish populations from the Ross Sea are connected more widely across the Antarctic. By comparing life history and otolith chemistry data across time, the researchers will assess change in life history parameters and spatial dynamics and seek to infer if these changes are driven by fishing or climate change. Spatially mapping of these data will allow an assessment of the efficacy of the Ross Sea MPA in protecting toothfish and where further protections might be needed. This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria. proprietary @@ -15671,13 +15671,13 @@ USAP-2149070_1 ANT LIA: Collaborative Research: Adaptations of Southern Ocean Di USAP-2232891_1 ANT LIA: The Role of Sex Determination in the Radiation of Antarctic Notothenioid Fish AMD_USAPDC STAC Catalog 2023-08-15 2027-07-31 -180, -90, 180, -37 https://cmr.earthdata.nasa.gov/search/concepts/C2759058324-AMD_USAPDC.umm_json Antarctic animals face tremendous threats as Antarctic ice sheets melt and temperatures rise. About 34 million years ago, when Antarctica began to cool, most species of fish became locally extinct. A group called the notothenioids, however, survived due to the evolution of antifreeze. The group eventually split into over 120 species. Why did this group of Antarctic fishes evolve into so many species? One possible reason why a single population splits into two species relates to sex genes and sex chromosomes. Diverging species often have either different sex determining genes (genes that specify whether an individual’s gonads become ovaries or testes) or have different sex chromosomes (chromosomes that differ between males and females within a species, like the human X and Y chromosomes). We know the sex chromosomes of only a few notothenioid species and know the genetic basis for sex determination in none of them. The aims of this research are to: 1) identify sex chromosomes in species representing every major group of Antarctic notothenioid fish; 2) discover possible sex determining genes in every major group of Antarctic notothenioid fish; and 3) find sex chromosomes and possible sex determining genes in two groups of temperate, warmer water, notothenioid fish. These warmer water fish include groups that never experienced the frigid Southern Ocean and groups that had ancestors inhabiting Antarctic oceans that later adjusted to warmer waters. This project will help explain the mechanisms that led to the division of a group of species threatened by climate change. This information is critical to conserve declining populations of Antarctic notothenioids, which are major food sources for other Antarctic species such as bird and seals. The project will offer a diverse group of undergraduates the opportunity to develop a permanent exhibit at the Eugene Science Center Museum. The exhibit will describe the Antarctic environment and explain its rapid climate change. It will also introduce the continent’s bizarre fishes that live below the freezing point of water. The project will collaborate with the university’s Science and Comics Initiative and students in the English Department’s Comics Studies Minor to prepare short graphic novels explaining Antarctic biogeography, icefish specialties, and the science of this project as it develops. proprietary USAP-2240780_1 ANT LIA: Collaborative Research: Mixotrophic Grazing as a Strategy to meet Nutritional Requirements in the Iron and Manganese Deficient Southern Ocean AMD_USAPDC STAC Catalog 2023-02-15 2026-01-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2639396983-AMD_USAPDC.umm_json Mixotrophic microorganisms that are capable of both photosynthetic and heterotrophic forms of metabolism are key contributors to primary productivity and organic carbon remineralization in the Southern Ocean. However, uncertainties in their grazing behavior and physiology prevent an accurate understanding of microbial food web dynamics and biogeochemical cycling in the Antarctic ecosystem. Polar mixotrophs have evolved to withstand extreme seasonality, including variable light, sea ice, temperature, and micronutrient concentrations. In particular, the Southern Ocean appears to be the only region of the world’s ocean where the bioessential trace metals iron (Fe) and manganese (Mn) are low enough to inhibit photosynthetic growth. The molecular physiology of mixotrophs experiencing Fe and Mn growth limitation has not yet been examined, and we lack an understanding of how seasonal changes in the availability of these micronutrients influence mixotrophic growth dynamics. We aim to examine whether grazing affords mixotrophs an ecological advantage in the Fe and Mn-deficient Southern Ocean, and to characterize the shifts in metabolic processes that occur during transitions in micronutrient conditions. Culture studies will directly measure growth responses, grazing behavior, and changes in elemental stoichiometry in response to Fe and Mn limitation. Transcriptomic analyses will reveal the metabolic underpinnings of trophic behavior and micronutrient stress responses, with implications for key biogeochemical processes such as carbon fixation, remineralization, and nutrient cycling. proprietary USAP-2324998_1 ANT LIA: Collaborative Research: Evolutionary Patterns and Mechanisms of Trait Diversification in the Antarctic Notothenioid Radiation AMD_USAPDC STAC Catalog 2022-10-01 2025-01-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C3333666817-AMD_USAPDC.umm_json Part I: Nontechnical description The ecologically important notothenioid fish of the Southern Ocean surrounding Antarctica will be studied to address questions central to polar, evolutionary, and adaptational biology. The rapid diversification of the notothenioids into >120 species following a period of Antarctic glaciation and cooling of the Southern Ocean is thought to have been facilitated by key evolutionary innovations, including antifreeze glycoproteins to prevent freezing and bone reduction to increase buoyancy. In this project, a large dataset of genomic sequences will be used to evaluate the genetic mechanisms that underlie the broad pattern of novel trait evolution in these fish, including traits relevant to human diseases (e.g., bone density, renal function, and anemia). The team will develop new STEM-based research and teaching modules for undergraduate education at Northeastern University. The work will provide specific research training to scholars at all levels, including a post-doctoral researcher, a graduate student, undergraduate students, and high school students. The team will also contribute to public outreach, including, in part, the develop of teaching videos in molecular evolutionary biology and accompanying educational supplements. Part II: Technical description The researchers will leverage their comprehensive notothenioid phylogenomic dataset comprising >250,000 protein-coding exons and conserved non-coding elements across 44 ingroup and 2 outgroup species to analyze the genetic origins of three iconic notothenioid traits: (1) loss of erythrocytes by the icefish clade in a cold, stable and highly-oxygenated marine environment. (2) reduction in bone mass and retention of juvenile skeletal characteristics as buoyancy mechanisms to facilitate foraging. And (3) loss of kidney glomeruli to retain energetically expensive antifreeze glycoproteins. The team will first track patterns of change in erythroid-related genes throughout the notothenioid phylogeny. They will then examine whether repetitive evolution of a pedomorphic skeleton in notothenioids is based on parallel or divergent evolution of genetic regulators of heterochrony. Third, they will determine whether there is mutational bias in the mechanisms of loss and re-emergence of kidney glomeruli. Finally, identified genetic mechanisms of evolutionary change will be validated by experimental testing using functional genomic strategies in the zebrafish model system. proprietary -USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure AMD_USAPDC STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure ALL STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary +USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure AMD_USAPDC STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary USAP-9725024_1 Circumpolar Deep Water and the West Antarctic Ice Sheet AMD_USAPDC STAC Catalog 1988-03-01 2002-02-28 140, -68, 150, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532072042-AMD_USAPDC.umm_json This project will study the dynamics of Circumpolar Deep Water intruding on the continental shelf of the West Antarctic coast, and the effect of this intrusion on the production of cold, dense bottom water, and melting at the base of floating glaciers and ice tongues. It will concentrate on the Amundsen Sea shelf, specifically in the region of the Pine Island Glacier, the Thwaites Glacier, and the Getz Ice Shelf. Circumpolar Deep Water (CDW) is a relatively warm water mass (warmer than +1.0 deg Celsius) which is normally confined to the outer edge of the continental shelf by an oceanic front separating this water mass from colder and saltier shelf waters. In the Amundsen Sea however, the deeper parts of the continental shelf are filled with nearly undiluted CDW, which is mixed upward, delivering significant amounts of heat to the base of the floating glacier tongues and the ice shelf. The melt rate beneath the Pine Island Glacier averages ten meters of ice per year with local annual rates reaching twenty meters. By comparison, melt rates beneath the Ross Ice Shelf are typically twenty to forty centimeters of ice per year. In addition, both the Pine Island and the Thwaites Glacier are extremely fast-moving, and have a significant effect on the regional ice mass balance of West Antarctica. This project therefore has an important connection to antarctic glaciology, particularly in assessing the combined effect of global change on the antarctic environment. The particular objectives of the project are (1) to delineate the frontal structure on the continental shelf sufficiently to define quantitatively the major routes of CDW inflow, meltwater outflow, and the westward evolution of CDW influence; (2) to use the obtained data set to validate a three-dimensional model of sub-ice ocean circulation that is currently under construction, and (3) to refine the estiamtes of in situ melting on the mass balance of the antarctic ice sheet. The observational program will be carried out from the research vessel Nathaniel B. Palmer in February and March, 1999. proprietary -USARC_AERIAL_PHOTOS Aerial Photography of Antarctica CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary USARC_AERIAL_PHOTOS Aerial Photography of Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary -USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ORNL_CLOUD STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary +USARC_AERIAL_PHOTOS Aerial Photography of Antarctica CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ALL STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary +USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ORNL_CLOUD STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary USDA0113 Groundwater Quality in Beaver Creek Watershed, Tennessee CEOS_EXTRA STAC Catalog 1992-07-01 1992-08-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411621-CEOS_EXTRA.umm_json Analysis for 400 domestic wells for selected constituents. Reconnaissance of Ground Water Quality in Beaver Creek Watershed, Shelby, Tipton, Fayette, and Haywood counties, Tennessee. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 400 wells; 20 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Dissemination Media: USGS Data Base Access Instructions: Contact the data center. proprietary USDA0114 Groundwater Quality in Bedford and Coffee Counties, Tennessee CEOS_EXTRA STAC Catalog 1991-06-01 1991-07-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411616-CEOS_EXTRA.umm_json Analysis for 200 domestic wells and springs for selected constituents. Reconnaissance of Ground Water Quality in Bedford and Coffee Counties, TN. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 200 wells/springs; 7 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base Access Instructions: Contact the data center. proprietary USDA0115 Groundwater Quality in Tennessee CEOS_EXTRA STAC Catalog 1984-01-01 1990-12-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411608-CEOS_EXTRA.umm_json Analysis of 150 wells for selected constituents, reconnaissance of Ground Water Quality in Tennessee. Collection Organization: USDA-CSREES - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by USGS staff. USGS conducted field and laboratory analysis at their national lab. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 150 wells on farmsteads across Tennessee; 7 parameters per well. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base. Access Instructions: Contact the data center. proprietary @@ -15688,16 +15688,16 @@ USGS-DDS-11 Geology of the Conterminous United States at 1:2,500,000 Scale -- A USGS-DDS-18-A_1.0 National Geochemical Database: National Uranium Resource Evaluation Data for the Conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -162, 24, -66, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231552333-CEOS_EXTRA.umm_json This is an online version of a CD-ROM publication. It is intended for use only on DOS-based computer systems. The files must be downloaded onto your computer before they can be used. The files are presented here in two forms: as the original folders that were published on the CD-ROM and as a large zip file that you can use to download the entire product in one step. This publication contains National Uranium Resource Evaluation (NURE) data for the conterminous United States. The data has been compressed and requires GSSEARCH software for access. GSSEARCH, supplied below, runs only under DOS. [Summary provided by the USGS.] proprietary USGS-DDS-19 Geology and Resource Assessment of Costa Rica at 1:500,000 Scale CEOS_EXTRA STAC Catalog 1970-01-01 -86, 8, -82, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2231554233-CEOS_EXTRA.umm_json PROJECT OVERVIEW Conversion of the information from the original folio to a computerized digital format was undertaken to facilitate the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, most of the maps on this disc are not intended to be used at any scale more detailed than 1:500,000. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. Digital information on geology, geophysics, and geochemistry can be combined to create useful derivative products. HISTORY OF THE MAPS In 1986 and 1987, the U.S. Geological Survey (USGS), the Dirección General de Geología, Minas e Hidrocarburos, and the Universidad de Costa Rica conducted a mineral-resource assessment of the Republic of Costa Rica. The results were published as a large 80- by 50-cm color folio (U.S. Geological Survey and others, 1987). The 75-page document consists of maps as well as descriptive and interpretive text in English and Spanish covering physiographic, geologic, geochemical, geophysical, and mineral site themes as well as a mineral-resource assessment. The following maps are present in the original folio: 1) Physiographic base map at a scale of 1:500,000 with hypsography, place names, and drainage. 2) Geologic map at a scale of 1:500,000. 3) Regional geophysical maps, including gravity, aeromagnetic, and seismicity maps at various scales. 4) Mineral sites map at a scale of 1:500,000 showing mines, prospects, and occurrences. 5) Volcanological framework of the Tilarán region important for epithermal gold deposits at a scale of 1:100,000. 6) Rock sample locations, mining areas, and vein locations for several parts of the country. 7) Permissive areas delineated for selected mineral deposit types. 8) Digital elevation model. This CD-ROM contains most of the above maps; it lacks items 1 and 8 and the seismicity and aeromagnetic maps of item 3. The linework was digitized from preliminary and printed maps with GSMAP (Selner and Taylor, 1987), a USGS-authored program for map editing and publishing. Conversion from GSMAP to ARC/INFO was accomplished through the use of the GSMARC program (Green and Selner, 1988). The arcs and polygons were tagged using Alacarte (Wentworth and Fitzgibbon, 1991). [Summary provided by the USGS.] proprietary USGS-DDS-27_1 Monthly average polar sea-ice concentration - USGS-DDS-27 CEOS_EXTRA STAC Catalog 1978-10-25 1991-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553834-CEOS_EXTRA.umm_json The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the modern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change. This data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original data were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. The objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing the variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each pole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of grid cells that lack valid data; the composite averages are designed to suppress interannual variability. Also included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical data retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems. The data contained in this data set are derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data produced by the National Snow and Ice Data Center (NSIDC) at the University of Colorado in cooperation with the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanic and Atmospheric Administration (NOAA). The basic data come from satellites of the U.S. Air Force Defense Meteorological Satellite Program. NSIDC distributes three collections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7 SMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the SMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two separate volumes, covering the periods from July 9 of 1987 to December 31 of 1989, and from January 1 of 1990 through December 31 of 1991, respectively. The NASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the present data set. SMMR images were collected every 2 to 3 days, whereas SSM/I data are provided in daily ice-concentration grids. Apart from a number of small gaps (5 or fewer days) in the record, the only long period for which no data are available is December 3 of 1987 through January 12 of 1988, inclusive. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the NSIDC data; the interpolated topographic data are included. The images are provided in three formats: Hierarchical Data Format (HDF), a flexible scientific data format developed at the National Center for Supercomputing Applications; Graphics Interchange Format (GIF); and Macintosh PICT format. The ice- concentration grids are distributed by NSIDC in HDF format. proprietary -USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples CEOS_EXTRA STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples ALL STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary -USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 ALL STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary +USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples CEOS_EXTRA STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 CEOS_EXTRA STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary +USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 ALL STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary USGS-DDS-74_2.0 Long-term Oceanographic Observations in Western Massachusetts Bay Offshore of Boston, Massachusetts: Data Report for 1989-2002 CEOS_EXTRA STAC Catalog 1989-12-01 2002-12-01 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231551840-CEOS_EXTRA.umm_json Long-term oceanographic observations have been made at two locations in western Massachusetts Bay: (1) Site A (42ý 22.6' N, 70ý 47.0' W, 33 m water depth) from from 1989 to 2002, and (2) Site B (42ý 9.8' N, 70ý 38.4' W, 21 m deter depth) from 1997 to 2002. Site A is approximately 1 km south of the new ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. These long-term oceanographic observations have been collected by the U.S. Geological Survey (USGS) in partnership with the Massachusetts Water Resources Authority (MWRA) and with logistical support from the U. S. Coast Guard (USCG). This report presents time series data collected through December 2002, updating a similar report that presented data through December 2000 (Butman and others, 2002). The long-term observations at these two stations are part of a USGS study designed to understand the transport and long-term fate of sediments and associated contaminants in the Massachusetts Bays (see //woodshole.er.usgs.gov/project-pages/bostonharbor / and Butman and Bothner, 1997). The long-term observations document seasonal and inter-annual changes in currents, hydrography, and suspended-matter concentration in western Massachusetts Bay, and the importance of infrequent catastrophic events, such as major storms or hurricanes, in sediment resuspension and transport. They also provide observations for testing numerical models of circulation. This data report presents a description of the field program and instrumentation, an overview of the data through summary plots and statistics, and the data in NetCDF and ASCII format for the period December 1989 through December 2002. The objective of this report is to make the data available in digital form, and to provide summary plots and statistics to facilitate browsing of the long-term data set . [Summary provided by the USGS.] proprietary USGS-DDS-79 Coastal Erosion and Wetland Change in Louisiana: Selected USGS Products CEOS_EXTRA STAC Catalog 1970-01-01 -94.3, 28.67, -88.54, 33.29 https://cmr.earthdata.nasa.gov/search/concepts/C2231552152-CEOS_EXTRA.umm_json Louisiana contains 25 percent of the vegetated wetlands and 40 percent of the tidal wetlands in the 48 conterminous States. These critical natural systems are being lost. Louisiana leads the Nation in coastal erosion and wetland loss as a result of a complex combination of natural processes (e.g. storms, sea-level rise, subsidence) and manmade alterations to the Mississippi River and the wetlands over the past 200 years. Erosion of several of the barrier islands, which lie offshore of the estuaries and wetlands and buffer and protect these important ecosystems from the open marine environment, exceeds 20 meters/year. The average rate of shoreline erosion is over 10 meters/year. Within the past 100 years, Louisiana's barrier islands have decreased in area by more than 40 percent, and some islands have lost more than 75 percent of their land area. If these loss rates continue, several of the barriers are expected to erode completely within the next three decades. Their disappearance will contribute to further loss and deterioration of wetlands and back-barrier estuaries and increase the risk to infrastructure. Coastal wetland environments, which include associated bays and estuaries, support a rich harvest of renewable natural resources with an estimated annual value of over $1 billion. More than 30 percent of the Nation's fisheries come from these wetlands, as well as 25 percent of oil and gas coming through the wetlands. Louisiana also has the highest rate of wetland loss: 80 percent of the Nation's total loss of wetlands has occurred in this State. The rate of wetland loss in the Mississippi River delta plain is estimated to be about 70 square kilometers/year -- the equivalent of a football field every 20 minutes. If these rates continue, an estimated 4,000 square kilometers of wetlands will be lost in the next 50 years. Losses of this magnitude have direct implications on the Nation's energy supplies, economic security, and environmental integrity. Over the past two decades, the USGS, working in partnership with other scientists in universities and State agencies, has led the research effort to document barrier erosion and wetland loss and understand the natural and manmade causes responsible. Some products resulting from this research, included in this DVD, are providing the baseline data and information being used for Federal-State wetlands restoration programs underway and being planned. [Summary provided by the USGS.] proprietary USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary -USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province ALL STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary +USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary USGS-DS-91_1.1 Depth to the Juan De Fuca Slab Beneath the Cascadia Subduction Margin: A 3-D Model for Sorting Earthquakes CEOS_EXTRA STAC Catalog 1970-01-01 -130, 40, -120, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2231552778-CEOS_EXTRA.umm_json The USGS presents an updated model of the Juan de Fuca slab beneath southern British Columbia, Washington, Oregon, and northern California, and use this model to separate earthquakes occurring above and below the slab surface. The model is based on depth contours previously published by Flück and others (1997). Our model attempts to rectify a number of shortcomings in the original model and to update it with new work. The most significant improvements include (1) a gridded slab surface in geo-referenced (ArcGIS) format, (2) continuation of the slab surface to its full northern and southern edges, (3) extension of the slab surface from 50-km depth down to 110-km beneath the Cascade arc volcanoes, and (4) revision of the slab shape based on new seismic-reflection and seismic-refraction studies. We have used this surface to sort earthquakes and present some general observations and interpretations of seismicity patterns revealed by our analysis. In addition, we provide files of earthquakes above and below the slab surface and a 3-D animation or fly-through showing a shaded-relief map with plate boundaries, the slab surface, and hypocenters for use as a visualization tool. [Summary provided by the USGS.] proprietary USGS-OFR-92-299_1.0 Molecular and Isotopic Analyses of the Hydrocarbon Gases within Gas Hydrate-Bearing Rock Units of the Prudhoe Bay-Kuparuk River Area in Northern Alaska CEOS_EXTRA STAC Catalog 1979-05-01 1990-09-01 -150, 70, -148, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2231550014-CEOS_EXTRA.umm_json "Information about and data from the USGS Open-File Report 92-299 (Molecular and isotopic analyses of the hydrocarbon gases within gas hydrate-bearing rock units of the Prudhoe Bay-Kuparuk River area in northern Alaska) are available On-line via the World Wide Web: ""http://pubs.usgs.gov/of/of92-299//"" or ""http://pubs.usgs.gov/of/1992/of92-299/"" The following information about the data set was provided by the data center contact: The objective of this study was to document the molecular and isotopic composition of the gas trapped within the gas hydrate-bearing stratigraphic intervals overlying the Prudhoe Bay and Kuparuk River oil fields. To reach this objective, we have analyzed cuttings gas and free gas samples collected from 10 drilling-production wells in the Prudhoe Bay and Kuparuk River fields. The dataset includes a report documenting the materials, the procedures used to analyze them, and the results. Results are given in tabular form as spreadsheets showing headspace, headspace/free gas, and blended headspace analyses. Gas characteristics analyzed include nitrogen, carbon dioxide, methane, ethane, ethene, propane, propene, isobutane, n-butane, isopentane, n-pentane, stable carbon isotope composition of the methane, ethane, and carbon dioxide fractions, and deuterium isotope composition of the methane fraction. Methane is the most abundant hydrocarbon gas within the gas hydrate- bearing rock units of the Prudhoe Bay-Kuparuk River area in the North Slope of Alaska. Isotopic analysis indicates that both microbial and thermogenic processes have contributed to the formation of this methane. The thermogenic component probably migrated into the rock units from greater depths, since vitrinite reflectance measurements show that the units never endured temperatures within the thermogenic range. Approximately 50 to 70 percent of the methane within the gas hydrate units is thermogenic in origin. This is U.S. Geological Survey Open-File Report 92-299 This report is preliminary and has not been reviewed for conformity with U.S. Geological Survey editorial standards or with the North American Stratigraphic Code. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government." proprietary USGS-PRISM-PACIFIC-OSTRACODES Modern and fossil ostracode census data from the Western Pacific Ocean and seas around Japan CEOS_EXTRA STAC Catalog 1990-01-01 1993-12-31 122, 25, 165, 63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551101-CEOS_EXTRA.umm_json "This data set is part of the Pliocene Research, Interpretation, and Synoptic Mapping (PRISM) Project. This data set describes marine ostracode species and related sample and stratigraphic information produced as part of the USGS PRISM Project (Pliocene Research, Interpretation, and Synoptic Mapping). The general goals of PRISM are to reconstruct global climate during a period of extreme warmth about 3 million years ago and to determine the causes of the warmth and the subsequent climatic change towards colder climates about 2.5 million years ago. To do this, PRISM has been studying Pliocene deposits and their microfaunas and, by comparison with modern assemblages, estimating past boundary conditions such as ocean temperatures. To obtain more reliable estimates of past environments in paleoclimate studies, the use of ecologically sensitive species requires extensive modern datasets on living species with limited environmental tolerances. Thus, much of the data generated by PRISM consists of species counts from modern samples that form a ""coretop"" dataset applicable not only to PRISM Pliocene assemblages but also to Quaternary assemblages as well. This situation was especially true for ostracodes, a group of Crustacea that includes many species that have limited range of water temperatures required for survival, reproduction, or both. Fossil assemblages of ostracodes can therefore yield information on past bottom water conditions on continental shelves in the mixed ocean layer above the thermocline and they are especially useful where planktic foraminifers are rare or absent. However comprehensive datasets with quantitative ostracode data were not available for application to regional paleoceanographic studies. Further, because of the endemic nature of ostracodes living on continental shelves, separate modern datasets needed to be developed for regions of the Pacific, Atlantic and Arctic Oceans. The data contained in the files in this folder come from the western North Pacific Ocean, mainly the seas around Japan. These regions encompass subtropical to cold temperate and subfrigid marine climate zones and include faunas from the major Western North Pacific water masses such as the Oyashio and Kuroshio current systems. The ostracode data sets were developed in collaboration with Prof. Noriyuki Ikeya, Institute of Geosciences, Shizuoka University, Shizuoka, Japan, Prof. Ikeya's students, and other Japanese colleagues, with support from the USGS Global Change and Climate History Program and grants from the National Science Foundation (NSF grant INT: LTV-9013402) and the Japanese Society for the Promotion of Science (JSPS grant EPAR- 093). Most of the faunal slides are housed at Shizuoka University. Separate PRISM ostracode data sets contain modern and Pliocene species data from continental shelves of the Arctic and Atlantic Oceans and from deep sea environments. Among the various types of quantitative analyses used to evaluate the ostracode data, the Squared Chord Distance (SCD) coefficient of dissimilarity was found to be useful in identifying modern analog assemblages for fossil assemblages on the basis of the proportions of shared species between two samples. The ostracode data and analyses of them are discussed in detail in the following published scientific papers: Ikeya, Noriyuki and Cronin, Thomas. M., 1993, Quantitative analysis of Ostracoda and water masses around Japan: Application to Pliocene and Pleistocene paleoceanography: Micropaleontology, v. 39, p. 263-281. Cronin, T.M., Kitamura, A., Ikeya, N., Watanabe, M., and Kamiya, T., in press. Late Pliocene climate change 3.4-2.3 Ma: Paleoceanographic record from the Yabuta Formation, Sea of Japan: Palaeogeography, Palaeoclimatology, Palaeoecology." proprietary @@ -15725,44 +15725,44 @@ USGS_DDS-66_1.0 Assessment of the Alluvial Sediments in the Big Thompson River V USGS_DDS-68 Coastal Vulnerability to Sea-Level Rise: A Preliminary Database for the U.S. Atlantic, Pacific, and Gulf of Mexico Coasts CEOS_EXTRA STAC Catalog 1970-01-01 -124.7608, 24.5485, -66.9578, 48.388 https://cmr.earthdata.nasa.gov/search/concepts/C2231553183-CEOS_EXTRA.umm_json "Coastal Changes Due to Sea-Level Rise: One of the most important applied problems in coastal geology today is determining the physical response of the coastline to sea-level rise. Predicting shoreline retreat, beach loss, cliff retreat, and land loss rates is critical to planning coastal zone management strategies and assessing biological impacts due to habitat change or destruction. Presently, long-term (>50 years) coastal planning and decision-making has been done piecemeal, if at all, for the nation's shoreline (National Research Council, 1990; 1995). Consequently, facilities are being located and entire communities are being developed without adequate consideration of the potential costs of protecting or relocating them from sea-level rise related erosion, flooding and storm damage. Recent estimates of future sea-level rise based on climate modeling (Wigley and Raper, 1992) suggest an increase in global eustatic sea-level of between 15 and 95 cm by 2100, with a ""best estimate"" of 50 cm (IPCC, 1995). This is more than double the rate of eustatic rise for the past century (Douglas, 1997; Peltier and Jiang, 1997). The prediction of coastal evolution is not straightforward. There is no standard methodology, and even the kinds of data required to make such predictions are the subject of much scientific debate. A number of predictive approaches have been used (National Research Council, 1990), including: 1. extrapolation of historical data (for example, coastal erosion rates); 2. static inundation modeling; 3. application of a simple geometric model (for example, the Bruun Rule); 4. application of a sediment dynamics/budget model; or 5. Monte Carlo (probabilistic) simulation based on parameterized physical forcing variables. Each of these approaches, however, has its shortcomings or can be shown to be invalid for certain applications (National Research Council, 1990). Similarly, the types of input data required vary widely, and for a given approach (for example, sediment budget), existing data may be indeterminate or may simply not exist (Klein and Nicholls, 1999). Furthermore, human manipulation of the coast in the form of beach nourishment, construction of seawalls, groins, and jetties, as well as coastal development itself, may dictate Federal, State and local priorities for coastal management without proper regard for geologic processes. Thus, the long-term decision to renourish or otherwise engineer a coastline may be the primary determining factor in how that coastal segment evolves. Variables Affecting Coastal Vulnerability: We use here a fairly simple classification of the relative vulnerability of different U.S. coastal environments to future rises in sea-level. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, and yields a relative measure of the system's natural vulnerability to the effects of sea-level rise (Klein and Nicholls, 1999). The vulnerability classification is based upon the relative contributions and interactions of six variables: 1. Tidal range, which contributes to inundation hazards. 2. Wave height, which is linked to inundation hazards. 3. Coastal slope (steepness or flatness of the coastal region), which is linked to the susceptibility of a coast to inundation by flooding and to the rapidity of shoreline retreat. 4. Shoreline erosion rates, which indicate how the given section of shoreline has been eroding. 5. Geomorphology, which indicates the relative erodibility of a given section of shoreline. 6. Historical rates of relative sea-level rise, which correspond to how the global (eustatic) sea-level rise and local tectonic processes (land motion such as uplift or subsidence) have affected a section of shoreline. The input data for this database of coastal vulnerability have been assembled using the original, and sometimes variable, horizontal resolution, which then was resampled to a 3-minute grid cell. A data set for each risk variable is then linked to each grid point. For mapping purposes, data stored in the 3-minute grid is transferred to a 1:2,000,000 vector shoreline with each segment of shoreline lying within a single grid cell. [Summary provided by the USGS.]" proprietary USGS_DDS-72 Bathymetry and Acoustic Backscatter of Crater Lake, Oregon from Field Activity: S-1-00-OR CEOS_EXTRA STAC Catalog 2000-07-28 2000-08-03 -122.16555, 42.904907, -122.049835, 42.978516 https://cmr.earthdata.nasa.gov/search/concepts/C2231551066-CEOS_EXTRA.umm_json "These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) or other software to display bathymetry and backscatter data of Crater Lake, Oregon. These data include high-resolution bathymetry and calibrated acoustic backscatter in XYZ ASCII and ArcInfo GRID format generated from the 2000 multibeam sonar survey of Crater Lake, Oregon. Information for USGS Coastal and Marine Geology related activities are online at ""http://walrus.wr.usgs.gov/infobank/s/s100or/html/s-1-00-or.meta.html"" These data not intended for navigational purposes. Please recognize the U.S. Geological Survey (USGS) as the source of this information. USGS-authored or produced data and information are in the public domain. Although these data have been used by the U.S. Geological Survey, U.S. Department of the Interior, these data and information are provided with the understanding that they are not guaranteed to be usable, timely, accurate, or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Neither the U.S. Government nor any agency thereof, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any data, software, information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. Trade, firm, or product names and other references to non-USGS products and services are provided for information only and do not constitute endorsement or warranty, express or implied, by the USGS, USDOI, or U.S. Government, as to their suitability, content, usefulness, functioning, completeness, or accuracy." proprietary USGS_DDS_10_1 Modern Average Global Sea-Surface Temperature CEOS_EXTRA STAC Catalog 1981-10-01 1989-12-31 -180, -66, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231552931-CEOS_EXTRA.umm_json The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-surface temperature (SST) throughout the modern world ocean and for estimating the modern monthly and weekly sea-surface temperature at any given oceanic location. It is expected that these data will be compared with temperature estimates derived from geological proxy measures such as faunal census analyses and stable isotopic analyses. The results can then be used to constrain general circulation models of climate change. The data contained in this data set are derived from the NOAA Advanced Very High Resolution Radiometer Multichannel Sea Surface Temperature data (AVHRR MCSST), which are obtainable from the Distributed Active Archive Center at the Jet Propulsion Laboratory (JPL) in Pasadena, Calif. The JPL tapes contain weekly images of SST from October 1981 through December 1990 in nine regions of the world ocean: North Atlantic, Eastern North Atlantic, South Atlantic, Agulhas, Indian, Southeast Pacific, Southwest Pacific, Northeast Pacific, and Northwest Pacific. This data set represents the results of calculations carried out on the NOAA data and also contains the source code of the programs that made the calculations. The objective was to derive the average sea-surface temperature of each month and week throughout the whole 10-year series, meaning, for example, that data from January of each year would be averaged together. The result is 12 monthly and 52 weekly images for each of the oceanic regions. Averaging the images in this way tends to reduce the number of grid cells that lack valid data and to suppress interannual variability. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the SST data; the interpolated topographic data are included. The images are provided in three formats: a modified form of run-length encoding (MRLE), Graphics Interchange Format (GIF), and Macintosh PICT format. Also included in the data set are programs that can retrieve seasonal temperature profiles at user-specified locations and that can decompress the data files. These nongraphical SST retrieval programs are provided in versions for UNIX, MS-DOS, and Macintosh computers. Graphical browse utilities are included for users of UNIX with the X Window System, 80386- based PC's, and Macintosh computers. proprietary -USGS_DDS_P12_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231553039-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number, type, and name: Number Type Name 1201 conventional Anticlinal Trends - Onshore 1202 conventional Basin Margin 1204 conventional Diagenetic 1211 conventional Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P12_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231553039-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number, type, and name: Number Type Name 1201 conventional Anticlinal Trends - Onshore 1202 conventional Basin Margin 1204 conventional Diagenetic 1211 conventional Anticlinal Trends - Offshore State Waters proprietary -USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary +USGS_DDS_P12_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231553039-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number, type, and name: Number Type Name 1201 conventional Anticlinal Trends - Onshore 1202 conventional Basin Margin 1204 conventional Diagenetic 1211 conventional Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional ALL STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary +USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary -USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary -USGS_DDS_P14_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231554068-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number and name: Number Name 1401 Santa Monica Fault System and Las Cienegas Fault and Block 1402 Southwestern Shelf and Adjacent Offshore State Lands 1403 Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 Whittier Fault Zone and Fullerton Embayment 1405 Northern Shelf and Northern Flank of Central Syncline 1406 Anaheim Nose 1407 Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary +USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary USGS_DDS_P14_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231554068-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number and name: Number Name 1401 Santa Monica Fault System and Las Cienegas Fault and Block 1402 Southwestern Shelf and Adjacent Offshore State Lands 1403 Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 Whittier Fault Zone and Fullerton Embayment 1405 Northern Shelf and Northern Flank of Central Syncline 1406 Anaheim Nose 1407 Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary +USGS_DDS_P14_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231554068-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number and name: Number Name 1401 Santa Monica Fault System and Las Cienegas Fault and Block 1402 Southwestern Shelf and Adjacent Offshore State Lands 1403 Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 Whittier Fault Zone and Fullerton Embayment 1405 Northern Shelf and Northern Flank of Central Syncline 1406 Anaheim Nose 1407 Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.75433, 32.527184, -115.904816, 34.236046 https://cmr.earthdata.nasa.gov/search/concepts/C2231553715-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 15 (San Diego - Oceanside) are listed here by play number, type, and name. proprietary USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.75433, 32.527184, -115.904816, 34.236046 https://cmr.earthdata.nasa.gov/search/concepts/C2231553715-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 15 (San Diego - Oceanside) are listed here by play number, type, and name. proprietary USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. proprietary USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province ALL STAC Catalog 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. proprietary USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary proprietary USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary proprietary -USGS_DDS_P17_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231548537-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number and name: Number Name 1701 Miocene Lacustrine (Lake Bruneau) 1702 Pliocene Lacustrine (Lake Idaho) 1703 Pre-Miocene 1704 Older Tertiary proprietary USGS_DDS_P17_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231548537-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number and name: Number Name 1701 Miocene Lacustrine (Lake Bruneau) 1702 Pliocene Lacustrine (Lake Idaho) 1703 Pre-Miocene 1704 Older Tertiary proprietary +USGS_DDS_P17_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231548537-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number and name: Number Name 1701 Miocene Lacustrine (Lake Bruneau) 1702 Pliocene Lacustrine (Lake Idaho) 1703 Pre-Miocene 1704 Older Tertiary proprietary USGS_DDS_P18_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Western Great Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231554181-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 18 (Western Great Basin) are listed here by play number, type, and name: Number Type Name 1801 conventional Hornbrook Basin-Modoc Plateau 1802 conventional Eastern Oregon Neogene Basins 1803 conventional Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 conventional Cretaceous Source Rocks, Northwestern Nevada 1805 conventional Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary USGS_DDS_P18_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Western Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231554181-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 18 (Western Great Basin) are listed here by play number, type, and name: Number Type Name 1801 conventional Hornbrook Basin-Modoc Plateau 1802 conventional Eastern Oregon Neogene Basins 1803 conventional Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 conventional Cretaceous Source Rocks, Northwestern Nevada 1805 conventional Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549693-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 18 (Western Great Basin) are listed here by play number and name: Number Name 1801 Hornbrook Basin-Modoc Plateau 1802 Eastern Oregon Neogene Basins 1803 Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 Cretaceous Source Rocks, Northwestern Nevada 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549693-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 18 (Western Great Basin) are listed here by play number and name: Number Name 1801 Hornbrook Basin-Modoc Plateau 1802 Eastern Oregon Neogene Basins 1803 Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 Cretaceous Source Rocks, Northwestern Nevada 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary -USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary -USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary +USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary +USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary -USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary -USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary +USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary -USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary +USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province ALL STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary -USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary +USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province ALL STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary +USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DOQ USGS Digital Orthophoto Quadrangles USGS_LTA STAC Catalog 1970-01-01 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566203-USGS_LTA.umm_json A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map. DOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution. The USGS produces three types of DOQs: 1. 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files. 2. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files. 3. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S. [Summary provided by the USGS.] proprietary USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour CEOS_EXTRA STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour ALL STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary @@ -15873,17 +15873,17 @@ USGS_Map_MF-2372_1.0 Hydrostructural Maps of the Death Valley Regional Flow Syst USGS_Map_MF-2373_1.0 Geologic maps and structure sections of the southwestern Santa Clara Valley and southern Santa Cruz Mountains, Santa Clara and Santa Cruz Counties, California CEOS_EXTRA STAC Catalog 1988-01-01 1997-12-31 -122, 36.998, -121.548, 37.252 https://cmr.earthdata.nasa.gov/search/concepts/C2231553047-CEOS_EXTRA.umm_json This database and accompanying plot files depict the distribution of geologic materials and structures at a regional (1:24,000) scale. The report is intended to provide geologic information for the regional study of materials properties, earthquake shaking, landslide potential, mineral hazards, seismic velocity, and earthquake faults. In addition, the report contains new information and interpretations about the regional geologic history and framework. However, the regional scale of this report does not provide sufficient detail for site development purposes. In addition, this map does not take the place of fault-rupture hazard zones designated by the California State Geologist (Hart and Bryant, 1997). Similarly, the database cannot be substituted for comprehensive maps that systematically identify and classify landslide hazards. This digital map database, compiled from previously published and unpublished data, and new mapping by the authors, represents the general distribution of bedrock and surficial deposits in the mapped area. Together with the accompanying text file (scvmf.ps, scvmf.pdf, scvmf.txt), it provides current information on the geologic structure and stratigraphy of the area covered. The database delineates map units that are identified by general age and lithology following the stratigraphic nomenclature of the U.S. Geological Survey. The scale of the source maps limits the spatial resolution (scale) of the database to 1:24,000 or smaller. proprietary USGS_Map_MF-2381-A_1.0 Geologic Map of the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554614-CEOS_EXTRA.umm_json This digital geologic and tectonic database of the Death Valley ground-water model area, as well as its accompanying geophysical maps, are compiled at 1:250,000 scale. The map compilation presents new polygon, line, and point vector data for the Death Valley region. The map area is enclosed within a 3 degree X 3 degree area along the border of southern Nevada and southeastern California. In addition to the Death Valley National Park and Death Valley-Furnace Creek fault systems, the map area includes the Nevada Test Site, the southwest Nevada volcanic field, the southern end of the Walker Lane (from southern Esmeralda County, Nevada, to the Las Vegas Valley shear zone and Stateline fault system in Clark County, Nevada), the eastern California shear zone (in the Cottonwood and Panamint Mountains), the eastern end of the Garlock fault zone (Avawatz Mountains), and the southern basin and range (central Nye and western Lincoln Counties, Nevada). This geologic map improves on previous geologic mapping in the area by providing new and updated Quaternary and bedrock geology, new interpretation of mapped faults and regional structures, new geophysical interpretations of faults beneath the basins, and improved GIS coverages. The basic geologic database has tectonic interpretations imbedded within it through attributing of structure lines and unit polygons which emphasize significant and through-going structures and units. An emphasis has been put on features which have important impacts on ground-water flow. Concurrent publications to this one include a new isostatic gravity map (Ponce and others, 2001), a new aeromagnetic map (Ponce and Blakely, 2001), and contour map of depth to basement based on inversion of gravity data (Blakely and Ponce, 2001). This map compilation was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the Department of Energy in conjunction with the U. S. Geological Survey and National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. The geologic compilation and tectonic interpretations contained within this database will serve as the basic framework for the flow model. The database also represents a synthesis of many sources of data compiled over many years in this geologically and tectonically significant area. proprietary USGS_Map_MF-2381-C_1.0 Isostatic Gravity Map of the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231555211-CEOS_EXTRA.umm_json An isostatic gravity map of the Death Valley groundwater model area was prepared from over 40,0000 gravity stations as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants out of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary -USGS_Map_MF-2381-D_1.0 Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California ALL STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554157-CEOS_EXTRA.umm_json An aeromagnetic map of the Death Valley groundwater model area was prepared from published aeromagnetic surveys as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary USGS_Map_MF-2381-D_1.0 Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554157-CEOS_EXTRA.umm_json An aeromagnetic map of the Death Valley groundwater model area was prepared from published aeromagnetic surveys as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary +USGS_Map_MF-2381-D_1.0 Aeromagnetic Map of the Death Valley Ground-water Model Area, Nevada and California ALL STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554157-CEOS_EXTRA.umm_json An aeromagnetic map of the Death Valley groundwater model area was prepared from published aeromagnetic surveys as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary USGS_Map_MF-2381-E_1.0 Map Showing Depth to Pre-Cenozoic Basement in the Death Valley Ground-water Model Area, Nevada and California CEOS_EXTRA STAC Catalog 1970-01-01 -118, 35, -115, 38.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231554277-CEOS_EXTRA.umm_json A depth to basement map of the Death Valley groundwater model area was prepared using over 40,0000 gravity stations as part of an interagency effort by the U.S. Geological Survey and the U.S. Department of Energy to help characterize the geology and hydrology of southwest Nevada and parts of California. This dataset was completed in support of the Death Valley Ground-Water Basin regional flow model funded by the U.S. Department of Energy in conjunction with the U. S. Geological Survey and U.S. National Park Service. The proposed model is intended to address issues concerning the availability of water in Death Valley National Park and surrounding counties of Nevada and California and the migration of contaminants off of the Nevada Test Site and Yucca Mountain high-level waste repository. proprietary USGS_Map_MF-2385_1.0 Map and map database of susceptibility to slope failure by sliding and earthflow in the Oakland area, California CEOS_EXTRA STAC Catalog 1970-01-01 -122.375, 37.625, -122, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231551540-CEOS_EXTRA.umm_json Mitigation is superior to post-disaster response in reducing the billions of dollars in losses resulting from U.S. natural disasters, and information that predicts the varying likelihood of geologic hazards can help public agencies improves the necessary decision making on land use and zoning. Accordingly, this map was created to increase the resistance of one urban area, metropolitan Oakland, California, to land sliding. Prepared in a geographic information system from a statistical model, the map estimates the relative likelihood of local slopes to fail by two processes common to this area of diverse geology, terrain, and land use. Map data that predict the varying likelihood of land sliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km2) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits. Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area where the three basic constituents, a geologic map, a landslide inventory, and a slope map, are available in digital form. Added predictive power of the new susceptibility model may reside in attributes that remain to be explored-among them seismic shaking, distance to nearest road, and terrain elevation, aspect, relief, and curvature. proprietary USGS_NAWQA_HG_DEP Atmospheric Deposition of Mercury in the Boston Area CEOS_EXTRA STAC Catalog 1970-01-01 -78, 40, -70, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550487-CEOS_EXTRA.umm_json Atmospheric deposition has been found to be the dominant source of mercury (Hg) in New England's aquatic environment (Krabbenhoft and others, 1999; Northeast States for Coordinated Air Use Management (NESCAUM) and others, 1998). Little is known about atmospheric mercury deposition in urban areas because most atmospheric monitoring to date has been done in rural areas. Preliminary water, sediment, and fish tissue data, collected by U.S. Geological Survey's New England Coastal Basins (NECB) study as part of the National Water Quality Assessment (NAWQA) program, shows elevated concentrations of mercury in the Boston metropolitan area. The NECB Mercury Deposition Network is a four-site, 2-year data collection effort by the USGS to help define the levels of mercury in precipitation and identify how atmospheric mercury may be contributing to mercury in the aquatic ecosystem. [Summary provided by the USGS.] proprietary USGS_NEIC_NEARRT Current and Near Real Time Earthquake Data from the USGS/National Earthquake Information Center (NEIC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551913-CEOS_EXTRA.umm_json The National Earthquake Information Center (NEIC of the U.S. Geological Survey provides current earthquake information and data including interactive earthquake maps, near real time earthquake data, fast moment and broadband solutions, and lists of earthquakes for the past 3 weeks. Current earthquake information and data are located at: http://earthquake.usgs.gov/ Near real time earthquake data is located at: http://earthquake.usgs.gov/ Archives of past earthquakes can be found at: http://earthquake.usgs.gov/earthquakes/eqinthenews/ proprietary USGS_NHD_CATCH National Hydrography Dataset Catchment Delineations CEOS_EXTRA STAC Catalog 1970-01-01 -170, 17, -46, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2231554271-CEOS_EXTRA.umm_json Topographically-based catchments will be delineated for all stream-reach segments of the National Hydrography Dataset (NHD) within the entire conterminous United States. The NHD is a digital hydrographic dataset produced by the USGS, in cooperation with the U.S. Environmental Protection Agency (USEPA), that shows streams, lakes, ponds, and wetlands for the Nation at an initial scale of 1:100,000. This effort is being supported by the USEPA and USGS and is intended to benefit a wide variety of water-quality and stream-flow studies across the nation. The catchment-delineation technique is the same as that developed for use in the New England SPARROW model. The New England SPARROW model was the first to utilize the detail of the National Hydrography Dataset (NHD) as the underlying stream-reach network. Final products for this project will be the completion of NHD catchment delineations for the conterminous United States, which will be part of the NHDPlus project to be completed and made available in 2006. proprietary -USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points ALL STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary -USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary +USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points ALL STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points ALL STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary +USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary ALL STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary USGS_NPS_AcadiaSpatialVeg_Final Acadia National Park Vegetation Mapping Project - Spatial Vegetation Data CEOS_EXTRA STAC Catalog 1997-05-27 1997-05-28 -69, 43.99574, -67.99682, 44.50385 https://cmr.earthdata.nasa.gov/search/concepts/C2231552959-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%. proprietary @@ -16021,8 +16021,8 @@ USGS_OFR_2004_1038 Inventory of Significant Mineral Deposit Occurrences in the H USGS_OFR_2004_1039 Location, Age, and Tectonic Significance of the Western Idaho Suture Zone CEOS_EXTRA STAC Catalog 1970-01-01 -118, 43, -112, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231552012-CEOS_EXTRA.umm_json The Western Idaho Suture Zone (WISZ) represents the boundary between crust overlying Proterozoic North American lithosphere and Late Paleozoic and Mesozoic intraoceanic crust accreted during Cretaceous time. Highly deformed plutons constituted of both arc and sialic components intrude the WISZ and in places are thrust over the accreted terranes. Pronounced variations in Sr, Nd, and O isotope ratios and in major and trace element composition occur across the suture zone in Mesozoic plutons. The WISZ is located by an abrupt west to east increase in initial 87Sr/86Sr ratios, traceable for over 300 km from eastern Washington near Clarkston, east along the Clearwater River thorough a bend to the south of about 110° from Orofino Creek to Harpster, and extending south-southwest to near Ola, Idaho, where Columbia River basalts conceal its extension to the south. K-Ar and 40Ar/39Ar apparent ages of hornblende and biotite from Jurassic and Early Cretaceous plutons in the accreted terranes are highly discordant within about 10 km of the WISZ, exhibiting patterns of thermal loss caused by deformation, subsequent batholith intrusion, and rapid rise of the continental margin. Major crustal movements within the WISZ commenced after about 135 Ma, but much of the displacement may have been largely vertical, during and following emplacement of batholith-scale silicic magmas. Deformation continued until at least 85 Ma and probably until 74 Ma, progressing from south to north. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1049_1.0 Geologic and Bathymetric Reconnaissance Overview of the San Pedro Shelf Region, Southern California CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -118.33333, 33.46667, -117.83333, 33.78333 https://cmr.earthdata.nasa.gov/search/concepts/C2231548808-CEOS_EXTRA.umm_json This report presents a series of maps that describe the bathymetry and late Quaternary geology of the San Pedro shelf area as interpreted from seismic-reflection profiles and 3.5-kHz and multibeam bathymetric data. Some of the seismic-reflection profiles were collected with Uniboom and 120-kJ sparker during surveys conducted by the U.S. Geological Survey (USGS) in 1973 (K-2-73-SC), 1978 (S-2-78-SC), and 1979 (S-2a-79-SC). The remaining seismic-reflection profiles were collected in 2000 using Geopulse boomer and minisparker during USGS cruise A-1-00-SC. The report consists of seven oversized sheets: 1. Map of 1978 and 1979 uniboom seismic-reflection and 3.5-kHz tracklines used to map faults and folds on San Pedro Shelf. 2. Maps of multibeam shaded bathymetric relief with faults and folds, and bathymetric contours. 3. Isopach map of unconsolidated sediment, seismic-reflection profile across the San Pedro shelf, seismic-reflection profile across San Gabriel paleo-valley. 4. Seismic-reflection profiles across the Palos Verdes Fault Zone. 5. Geologic map and samples of Uniboom and 120-kJ sparker seismic-reflection profiles used to make the map. 6. Map showing thickness of uppermost (Holocene?) sediment layer. 7. Map of San Gabriel Canyon paleo-valley and associated drainage basins. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1054 Assessment of Hazards Associated with the Bluegill Landslide, South-Central Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 41.64, -110.7, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231554051-CEOS_EXTRA.umm_json The Bluegill landslide, located in south-central Idaho, is part of a larger landslide complex that forms an area in the Salmon Falls Creek drainage named Sinking Canyon. The landslide is on public property administered by the U.S. Bureau of Land Management (BLM). As part of ongoing efforts to address possible public safety concerns, the BLM requested that the U.S. Geological Survey (USGS) conduct a preliminary hazard assessment of the landslide, examine possible mitigation options, and identify alternatives for further study and monitoring of the landslide. This report presents the findings of that assessment based on a field reconnaissance of the landslide on September 24, 2003, a review of data and information provided by BLM and researchers from Idaho State University, and information collected from other sources. [Summary provided by the USGS.] proprietary -USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory CEOS_EXTRA STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory ALL STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory CEOS_EXTRA STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1064 Coastal Vulnerability Assessment of Cape Hatteras National Seashore (CAHA) to Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -80, 33, -76, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231549408-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cape Hatteras National Seashore (CAHA) in North Carolina. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range, and mean significant wave height. The rankings for each variable were combined and an index value was calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Cape Hatteras National Seashore consists of stable and washover dominated segments of barrier beach backed by wetland and marsh. The areas within Cape Hatteras that are likely to be most vulnerable to sea-level rise are those with the highest occurrence of overwash and the highest rates of shoreline change. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1067 Digital Database of Selected Aggregate and Related Resources in Ada, Boise, Canyon, Elmore, Gem, and Owyhee Counties, Southwestern Idaho CEOS_EXTRA STAC Catalog 1934-01-01 2003-12-31 -117.01154, 42.29952, -115.10053, 44.17547 https://cmr.earthdata.nasa.gov/search/concepts/C2231549777-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) compiled a database of aggregate sites and geotechnical sample data for six counties - Ada, Boise, Canyon, Elmore, Gem, and Owyhee - in southwest Idaho as part of a series of studies in support of the Bureau of Land Management (BLM) planning process. Emphasis is placed on sand and gravel sites in deposits of the Boise River, Snake River, and other fluvial systems and in Neogene lacustrine deposits. Data were collected primarily from unpublished Idaho Transportation Department (ITD) records and BLM site descriptions, published Army Corps of Engineers (ACE) records, and USGS sampling data. The results of this study provides important information needed by land-use planners and resource managers, particularly in the BLM, to anticipate and plan for demand and development of sand and gravel and other mineral material resources on public lands in response to the urban growth in southwestern Idaho. The aggregate database combines two data sets - site information and geotechnical sample data - into an integrated spatial database with 82 unique fields. The material source site data set includes information on 680 sites, and the geotechnical data set consists of selected information from 2,723 laboratory analyses of samples collected from many, but not all, of the sites. The 680 aggregate sites are divided into six classes: sand & gravel (614); rock quarry (43); cinder quarry (9); placer tailings (8); talus (4); and mine waste rock (2). Most importantly, the aggregate database includes detailed location information allowing individual sites to be located at least within a section and most often within a small parcel of a section. Additional information includes, but is not limited to: lithology-mineralogy or geologic formation (if known); surface ownership; size; production; permitting; agency; and number of samples. Geotechnical data include: lab number and test date; field parameters including sample location, type of material, and size; and the results of geotechnical analyses - gradation (grain size distribution), Los Angeles (LA) Degradation, sand equivalent, absorption, density, and several other tests. Ninety-five percent of the 2,723 geotechnical sample records include gradation data, and 72 percent of the samples have sand equivalent data. However, LA Degradation, absorption, and bulk density data are reported only in about 30 percent of the sample records. Large volumes of geotechnical data reside in a variety of accessible but little-used archives maintained by local and county highway districts, state transportation bureaus, and federal engineering, construction and transportation agencies. Integration of good quality geotechnical lithogeochemical information, particularly in digital form suitable for geospatial analysis, can produce profoundly superior databases that may allow more accurate and reliable ""expert"" decision making and improved land use planning. The database that accompanies this report, structured for direct import into geographic information system (GIS) software, is the first step toward producing such an integrated geologic-geotechnical spatial database. [Summary provided by the USGS.]" proprietary USGS_OFR_2004_1069 A 30-Year Record of Surface Mass Balance (1966-95) and Motion and Surface Altitude (1975-95) at Wolverine Glacier, Alaska CEOS_EXTRA STAC Catalog 1966-04-01 1995-12-31 -156, 57, -144, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2231554448-CEOS_EXTRA.umm_json Scientific measurements at Wolverine Glacier, on the Kenai Peninsula in south-central Alaska, began in April 1966. At three long-term sites in the research basin, the measurements included snow depth, snow density, heights of the glacier surface and stratigraphic summer surfaces on stakes, and identification of the surface materials. Calculations of the mass balance of the surface strata-snow, new firn, superimposed ice, and old firn and ice mass at each site were based on these measurements. Calculations of fixed-date annual mass balances for each hydrologic year (October 1 to September 30), as well as net balances and the dates of minimum net balance measured between time-transgressive summer surfaces on the glacier, were made on the basis of the strata balances augmented by air temperature and precipitation recorded in the basin. From 1966 through 1995, the average annual balance at site A (590 meters altitude) was -4.06 meters water equivalent; at site B (1,070 meters altitude), was -0.90 meters water equivalent; and at site C (1,290 meters altitude), was +1.45 meters water equivalent. Geodetic determination of displacements of the mass balance stake, and glacier surface altitudes was added to the data set in 1975 to detect the glacier motion responses to variable climate and mass balance conditions. The average surface speed from 1975 to 1996 was 50.0 meters per year at site A, 83.7 meters per year at site B, and 37.2 meters per year at site C. The average surface altitudes were 594 meters at site A, 1,069 meters at site B, and 1,293 meters at site C; the glacier surface altitudes rose and fell over a range of 19.4 meters at site A, 14.1 meters at site B, and 13.2 meters at site C. [Summary provided by the USGS.] proprietary @@ -16065,8 +16065,8 @@ USGS_OFR_2005_1070_1.0 Molokai Benthic Habitat Mapping CEOS_EXTRA STAC Catalog 1 USGS_OFR_2005_1132_1.0 Ground-Magnetic Studies of the Amargosa Desert Region, California and Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231555068-CEOS_EXTRA.umm_json High-resolution aeromagnetic surveys of the Amargosa Desert region, California and Nevada, exhibit a diverse array of magnetic anomalies reflecting a wide range of mid- and upper-crustal lithologies. In most cases, these anomalies can be interpreted in terms of exposed rocks and sedimentary deposits. More difficult to explain are linear magnetic anomalies situated over lithologies that typically have very low magnetizations. Aeromagnetic anomalies are observed, for example, over thick sections of Quaternary alluvial deposits and spring deposits associated with past or modern ground-water discharge in Ash Meadows, Pahrump Valley, and Furnace Creek Wash. Such deposits are typically considered nonmagnetic. To help determine the source of these aeromagnetic anomalies, we conducted ground-magnetic studies at five areas: near Death Valley Junction, at Point of Rocks Spring, at Devils Hole, at Fairbanks Spring, and near Travertine Springs. Depth-to-source calculations show that the sources of these anomalies lie within the Tertiary and Quaternary sedimentary section. We conclude that they are caused by discrete volcanic units lying above the pre-Tertiary basement. At Death Valley Junction and Travertine Springs, these concealed volcanic units are probably part of the Miocene Death Valley volcanic field exposed in the nearby Greenwater Range and Black Mountains. The linear nature of the aeromagnetic anomalies suggests that these concealed volcanic rocks are bounded and offset by near-surface faults. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1135_1.0 Modified Mercalli Intensity Maps for the 1906 San Francisco Earthquake Plotted in ShakeMap Format CEOS_EXTRA STAC Catalog 1906-04-18 1906-04-18 -124, 34, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231554244-CEOS_EXTRA.umm_json This website presents Modified Mercalli Intensity maps for the great San Francisco earthquake of April 18, 1906. These new maps combine two important developments. First, we have re-evaluated and relocated the damage and shaking reports compiled by Lawson (1908). These reports yield intensity estimates for more than 600 sites and constitute the largest set of intensities ever compiled for a single earthquake. Second, we use the recent ShakeMap methodology to map these intensities. The resulting MMI intensity maps are remarkably detailed and eloquently depict the enormous power and damage potential of this great earthquake. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1144 Huminite Reflectance Measurements of Paleocene and Upper Cretaceous Coals from Borehole Cuttings, Zavala and Dimmit Counties, South Texas CEOS_EXTRA STAC Catalog 1970-01-01 -107.31, 25.19, -92.85, 37.14 https://cmr.earthdata.nasa.gov/search/concepts/C2231553355-CEOS_EXTRA.umm_json The reflectance of huminite in 19 cuttings samples was determined in support of ongoing investigations into the coal bed methane potential of subsurface Paleocene and Upper Cretaceous coals of South Texas. Coal cuttings were obtained from the Core Research Center of the Bureau of Economic Geology, The University of Texas at Austin. Geophysical logs, mud-gas logs, driller's logs, completion cards, and scout tickets were used to select potentially coal-bearing sample suites and to identify specific sample depths. Reflectance measurements indicate coals of subbituminous rank are present in a wider area in South Texas than previously recognized. [Summary provided by the USGS.] proprietary -USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 ALL STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 CEOS_EXTRA STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 ALL STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1153_1.0 Multibeam Bathymetry and Backscatter Data: Northeastern Channel Islands Region, Southern California CEOS_EXTRA STAC Catalog 2004-08-06 2004-08-15 -119.72, 33.88, -119.03, 34.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231553010-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) in cooperation with the Minerals Management Service (MMS) conducted multibeam mapping in the eastern Santa Barbara Channel and northeastern Channel Islands region from August 8 to15, 2004 aboard the R/V Maurice Ewing. The survey was directed and funded by the Minerals Management Service, which is interested in maps of hard bottom habitats, particularly natural outcrops, that support reef communities in areas affected by oil and gas activity. The maps are also useful to biologists studying fish that use the platforms and the sea floor beneath them as habitat. The survey collected bathymetry and corrected, co-registered acoustic backscatter using a Kongsberg Simrad EM1002 multibeam echosounder that was mounted on the hull of the R/V Maurice Ewing. Three main regions were mapped during the survey including: (1) the Eastern Santa Barbara Channel adjacent to an area previously mapped with multibeam-sonar by the Monterey Bay Aquarium Research Institute (see the MBARI Santa Barbara Basin Multibeam Survey web page), (2) the Footprint area south of Anacapa Island, which has been studied extensively by rockfish biologists and is considered a good site for a marine protected area, and (3) part of the submarine canyons along the continental slope south of Port Hueneme. These data will be used to support a number of new and ongoing projects including, habitat mapping, shelf and slope processes, and offshore hazards and resources. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1164_1.0 An Assessment of Volcanic Threat and Monitoring Capabilities in the United States: Framework for a National Volcano Early Warning System CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551822-CEOS_EXTRA.umm_json A National Volcano Early Warning System NVEWS is being formulated by the Consortium of U.S. Volcano Observatories (CUSVO) to establish a proactive, fully integrated, national-scale monitoring effort that ensures the most threatening volcanoes in the United States are properly monitored in advance of the onset of unrest and at levels commensurate with the threats posed. Volcanic threat is the combination of hazards (the destructive natural phenomena produced by a volcano) and exposure (people and property at risk from the hazards). The United States has abundant volcanoes, and over the past 25 years the Nation has experienced a diverse range of the destructive phenomena that volcanoes can produce. Hazardous volcanic activity will continue to occur, and because of increasing population, increasing development, and expanding national and international air traffic over volcanic regions the exposure of human life and enterprise to volcano hazards is increasing. Fortunately, volcanoes exhibit precursory unrest that if detected and analyzed in time allows eruptions to be anticipated and communities at risk to be forewarned with reliable information in sufficient time to implement response plans and mitigation measures. In the 25 years since the cataclysmic eruption of Mount St. Helens, scientific and technological advances in volcanology have been used to develop and test models of volcanic behavior and to make reliable forecasts of expected activity a reality. Until now, these technologies and methods have been applied on an ad hoc basis to volcanoes showing signs of activity. However, waiting to deploy a robust, modern monitoring effort until a hazardous volcano awakens and an unrest crisis begins is socially and scientifically unsatisfactory because it forces scientists, civil authorities, citizens, and businesses into playing catch up with the volcano, trying to get instruments and civil-defense measures in place before the unrest escalates and the situation worsens. Inevitably, this manner of response results in our missing crucial early stages of the volcanic unrest and hampers our ability to accurately forecast events. Restless volcanoes do not always progress to eruption; nevertheless, monitoring is necessary in such cases to minimize either over-reacting, which costs money, or under-reacting, which may cost lives. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1176 Flooding of the Androscoggin River during December 18-19, 2003, in Canton, Maine CEOS_EXTRA STAC Catalog 2003-12-18 2003-12-19 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231550802-CEOS_EXTRA.umm_json The Androscoggin River flooded the town of Canton, Maine in December 2003, resulting in damage to and (or) evacuation of 44 homes. Streamflow records at the U.S. Geological Survey (USGS) streamflow-gaging stations at Rumford (USGS station identification number 01054500) and Auburn (01059000) were used to estimate the peak streamflow for the Androscoggin in the town of Canton for this flood (December 18-19, 2003). The estimated peak flood streamflow at Canton was approximately 39,800 ft3/s, corresponding to an estimated recurrence interval of 4.4 years; however, an ice jam downstream from Canton Point on December 18-19 obstructed river flow resulting in a high-water elevation commensurate with an open-water flood approximately equal to a 15-year event. The high water-surface elevations attained during the December 18-19 flood event in Canton were higher than the expected open-water flood water-surface elevations; this verified the assumption that the water-surface elevation was augmented due to the downstream ice jam. The change in slope of the riverbed from upstream of Canton to the impoundment at the downstream corporate limits, and the river bend near Stevens Island are principal factors in ice-jam formation near Canton. The U.S. Army Corps of Engineers Ice Jam Database indicates five ice-jam-related floods (including December 2003) for the town of Canton: March 13, 1936; January 1978; March 12, 1987; January 29, 1996; and December 18-19, 2003. There have been more ice-jam-related flood events in Canton than these five documented events, but the exact number and nature of ice jams in Canton cannot be determined without further research. proprietary @@ -16095,8 +16095,8 @@ USGS_OFR_2006_1091 Concentrations of Nutrients, Pesticides, and Suspended Sedime USGS_OFR_2006_1096 Coastal Classification Atlas: Central Texas Coastal Classification Maps - Aransas Pass to Mansfield Channel CEOS_EXTRA STAC Catalog 1970-01-01 -102, 24, -96, 29 https://cmr.earthdata.nasa.gov/search/concepts/C2231551630-CEOS_EXTRA.umm_json The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high priority because they have dense populations or valuable resources that are at risk from storm waves. A secondary purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States. A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard-vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability. The map above shows the areas covered by this web site. Click on any of the location names or outlines to view the Coastal Classification Map for that area. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1110 Geophysical Studies of the Crump Geyser Known Geothermal Resource Area, Oregon, in 1975 CEOS_EXTRA STAC Catalog 1970-01-01 -130, 42, -122, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231552525-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) conducted geophysical studies in support of the resource appraisal of the Crump Geyser Known Geothermal Resource Area (KGRA). This area was designated as a KGRA by the USGS, and this designation became effective on December 24, 1970. The land classification standards for a KGRA were established by the Geothermal Steam Act of 1970 (Public Law 91-581). Federal lands so classified required competitive leasing for the development of geothermal resources. The author presented an administrative report of USGS geophysical studies entitled ""Geophysical background of the Crump Geyser area, Oregon, KGRA"" to a USGS resource committee on June 17, 1975. This report, which essentially was a description of geophysical data and a preliminary interpretation without discussion of resource appraisal, is in Appendix 1. Reduction of sheets or plates in the original administrative report to page-size figures, which are listed and appended to the back of the text in Appendix 1, did not seem to significantly degrade legibility. Bold print in the text indicates where minor changes were made. A colored page-size index and tectonic map, which also show regional geology not shown in figure 2, was substituted for original figure 1. Detailed descriptions for the geologic units referenced in the text and shown on figures 1 and 2 were separately defined by Walker and Repenning (1965) and presumably were discussed in other reports to the committee. Heavy dashed lines on figures 1 and 2 indicate the approximate KGRA boundary. One of the principal results of the geophysical studies was to obtain a gravity map (Appendix 1, fig. 10; Plouff, and Conradi, 1975, pl. 9), which reflects the fault-bounded steepness of the west edge of sediments and locates the maximum thickness of valley sediments at about 10 kilometers south of Crump Geyser. Based on the indicated regional-gravity profile and density-contrast assumptions for the two-dimensional profile, the maximum sediment thickness was estimated at 820 meters. A three-dimensional gravity model would have yielded a greater thickness. Audiomagnotelluric measurements were not made as far south as the location of the gravity low, as determined in the field, due to a lack of communication at that time. A boat was borrowed to collect gravity measurements along the edge of Crump Lake, but the attempt was curtailed by harsh, snowy weather on May 21, 1975, which shortly followed days of hot temperature. Most of the geophysical data and illustrations in Appendix 1 have been published (Gregory and Martinez, 1975; Plouff, 1975; and Plouff and Conradi, 1975), and Donald Plouff (1986) discussed a gravity interpretation of Warner Valley at the Fall 1986 American Geophysical Union meeting in San Francisco. Further interpretation of possible subsurface geologic sources of geophysical anomalies was not discussed in Appendix 1. For example, how were apparent resistivity lows (Appendix 1, figs. 3-6) centered near Crump Geyser affected by a well and other manmade electrically conductive or magnetic objects? What is the geologic significance of the 15-milligal eastward decrease across Warner Valley? The explanation that the two-dimensional gravity model (Appendix 1, fig. 14) was based on an inverse iterative method suggested by Bott (1960) was not included. Inasmuch as there was no local subsurface rock density distribution information to further constrain the gravity model, the three-dimensional methodology suggested by Plouff (1976) was not attempted. Inasmuch as the associated publication by Plouff (1975), which released the gravity data, is difficult to obtain and not in digital format, that report is reproduced in Appendix 2. Two figures of the publication are appended to the back of the text. A later formula for the theoretical value of gravity for the given latitudes at sea level (International Association of Geodesy, 1971) should be used to re-compute gravity anomalies. To merge the observed-gravity values printed in that report with later measurements, an empirically determined constant gravity datum shift should be applied. [Summary provided by the USGS.]" proprietary USGS_OFR_2006_1129_WIPP_NM_1.0 Online Aquifer-Test Data for Wells H-1, H-2A, H-2B, H-2C, and H-3 at the Waste Isolation Pilot Plant, Southeastern New Mexico CEOS_EXTRA STAC Catalog 1979-02-01 1980-07-31 -103.75, 32.33, -103.5, 32.41 https://cmr.earthdata.nasa.gov/search/concepts/C2231551503-CEOS_EXTRA.umm_json The U.S.Geological Survey Open-File Report consists of the results of a series of aquifer tests (shut-in test, flow test, bailing test, slug test, swabbing test and pressure-pulse test)performed by the U.S. Geological Survey on geologic units of Permian age at the Waste Isoliation Pilot Plant site between February 1979 and July 1980 in wells H-1, H-2 complex (H2-2A, H-2B, and H-2C), and H-3. The tested geologic units included the Magenta Dolomite and Culebra Dolomite Members of the Rustler Formation, and the contact zone between the Rustler and Salado Formations. Selected information on the tested formations, test dates, pre-test static water levels, test configurations, and raw data collected during these tests are tabulated in this report. [Summary taken in large part from U.S. Geological Survey Open-File Report abstract] proprietary -USGS_OFR_2006_1136 Aeromagnetic Survey of Dillingham Area in Southwest Alaska, A Website for the Preliminary Distribution of Data ALL STAC Catalog 2005-09-01 2005-10-22 -159.19, 58.3, -155.45, 60.06 https://cmr.earthdata.nasa.gov/search/concepts/C2231551877-CEOS_EXTRA.umm_json This is a USGS Open-File-Report for the preliminary release of aeromagnetic data collected in the Dillingham Area of Southwest Alaska and associated contractor reports. An airborne high-resolution magnetic survey was completed over the Dillingham and Nushagak Bay and Naknek area in southwestern Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by August 26th, 2005 and data acquisition was initiated on September 1st, 2005. The final data acquisition flight was completed on October 22nd, 2005. A total of 8,630 line-miles of data were acquired during the survey. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1136 Aeromagnetic Survey of Dillingham Area in Southwest Alaska, A Website for the Preliminary Distribution of Data CEOS_EXTRA STAC Catalog 2005-09-01 2005-10-22 -159.19, 58.3, -155.45, 60.06 https://cmr.earthdata.nasa.gov/search/concepts/C2231551877-CEOS_EXTRA.umm_json This is a USGS Open-File-Report for the preliminary release of aeromagnetic data collected in the Dillingham Area of Southwest Alaska and associated contractor reports. An airborne high-resolution magnetic survey was completed over the Dillingham and Nushagak Bay and Naknek area in southwestern Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by August 26th, 2005 and data acquisition was initiated on September 1st, 2005. The final data acquisition flight was completed on October 22nd, 2005. A total of 8,630 line-miles of data were acquired during the survey. [Summary provided by the USGS.] proprietary +USGS_OFR_2006_1136 Aeromagnetic Survey of Dillingham Area in Southwest Alaska, A Website for the Preliminary Distribution of Data ALL STAC Catalog 2005-09-01 2005-10-22 -159.19, 58.3, -155.45, 60.06 https://cmr.earthdata.nasa.gov/search/concepts/C2231551877-CEOS_EXTRA.umm_json This is a USGS Open-File-Report for the preliminary release of aeromagnetic data collected in the Dillingham Area of Southwest Alaska and associated contractor reports. An airborne high-resolution magnetic survey was completed over the Dillingham and Nushagak Bay and Naknek area in southwestern Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by August 26th, 2005 and data acquisition was initiated on September 1st, 2005. The final data acquisition flight was completed on October 22nd, 2005. A total of 8,630 line-miles of data were acquired during the survey. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1247 High-resolution chirp seismic reflection data acquired from the Cap de Creus shelf and canyon area, Gulf of Lions, Spain in 2004 CEOS_EXTRA STAC Catalog 2003-09-25 2003-10-01 3.1808, 42.1763, 3.4586, 42.4418 https://cmr.earthdata.nasa.gov/search/concepts/C2231550660-CEOS_EXTRA.umm_json This report consists of high-resolution chirp seismic reflection profiledata from the northern Gulf of Lions, Spain. These data were acquired in2004 using the Research Vessel Oceanus (USGS Cruise ID: O-1-04-MS). Thedata are available in binary and JPEG image formats. Binary data arein Society of Exploration Geologists (SEG) SEG-Y format and may bedownloaded for further processing or display. Reference maps andJPEG images of the profiles may be viewed with your Web browser. Marine seismic reflection data are used to image and mapsedimentary and structural features of the seafloor and subsurface.These data were acquired across the shelf and canyon area of the Gulfof Lions, Spain as part of a multinational effort to characterize thegeologic framework and sedimentary environment of the region.The specific objective of this seismic survey is to provide seismicreflection images of the depositional geometry of the upper 50 meters ofsubbottom stratigraphy in order to better understand the mechanisms ofsediment transport and deposition. These chirp seismic profiles providehigh-quality images with approximately 20 cm of verticalresolution and up to 80 m of subbottom penetration. Chirp seismic reflection profiles are acquired by means of anacoustic source and a hydrophone array, both contained in a single unittowed in the water behind a survey vessel. The sound source emits ashort (30 ms) swept-frequency (500 to 7200 Hz)acoustic pulse,which propagates through the water and sediment columns. The acousticenergy is reflected at density boundaries (such as the seafloor orsediment layers beneath the seafloor), and detected by the hydrophonearray, and digitally recorded by the onboard PC-based acquisition system.As the vessel moves, this process is repeated multiple times per second,producing a two-dimensional image of the shallow geologic structurebeneath the ship track. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1274 Land Area Changes in Coastal Louisiana After the 2005 Hurricanes: A Series of Three Maps CEOS_EXTRA STAC Catalog 1956-01-01 2005-12-31 -96, 30, -88, 32 https://cmr.earthdata.nasa.gov/search/concepts/C2231553246-CEOS_EXTRA.umm_json This report includes three posters with analyses of net land area changes in coastal Louisiana after the 2005 hurricanes (Katrina and Rita). The first poster presents a basic analysis of net changes from 2004 to 2005; the second presents net changes within marsh communities from 2004 to 2005; and the third presents net changes from 2004 to 2005 within the historical perspective of change in coastal Louisiana from 1956 to 2004. The purpose of this analysis was to provide preliminary information on land area changes shortly after Hurricanes Katrina and Rita and to serve as a regional baseline for monitoring wetland recovery following the 2005 hurricane season. Estimation of permanent losses cannot be made until several growing seasons have passed and the transitory impacts of the hurricanes are minimized, but this preliminary analysis indicates an approximate 217-mi2 (562.03-km2) decrease in land/increase in water across coastal Louisiana. [Summary provided by the USGS.] proprietary USGS_OFR_2006_1280 Metallogeny of the Great Basin: Crustal Evolution, Fluid Flow, and Ore Deposits CEOS_EXTRA STAC Catalog 1970-01-01 -126, 29, -116, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2231551576-CEOS_EXTRA.umm_json The Great Basin physiographic province in the Western United States contains a diverse assortment of world-class ore deposits. It currently (2006) is the world's second leading producer of gold, contains large silver and base metal (Cu, Zn, Pb, Mo, W) deposits, a variety of other important metallic (Fe, Ni, Be, REE's, Hg, PGE) and industrial mineral (diatomite, barite, perlite, kaolinite, gallium) resources, as well as petroleum and geothermal energy resources. Ore deposits are most numerous and largest in size in linear mineral belts with complex geology. [Summary provided by the USGS.] proprietary @@ -16118,8 +16118,8 @@ USGS_OFR_2007_1146 Estimated Magnitudes and Recurrence Intervals of Peak Flows o USGS_OFR_2007_1152 High-Resolution Seismic Imaging Investigations in Salt Lake and Utah Valleys for Earthquake Hazards CEOS_EXTRA STAC Catalog 2003-09-01 2005-09-30 -113, 40, -111.5, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231549137-CEOS_EXTRA.umm_json In support of earthquake hazards and ground motion studies by researchers at the Utah Geological Survey, University of Utah, Utah State University, Brigham Young University, and San Diego State University, the U.S. Geological Survey Geologic Hazards Team Intermountain West Project conducted three high-resolution seismic imaging investigations along the Wasatch Front between September 2003 and September 2005. These three investigations include: (1) a proof-of-concept P-wave minivib reflection imaging profile in south-central Salt Lake Valley, (2) a series of seven deep (as deep as 400 m) S-wave reflection/refraction soundings using an S-wave minivib in both Salt Lake and Utah Valleys, and (3) an S-wave (and P-wave) investigation to 30 m at four sites in Utah Valley and at two previously investigated S-wave (Vs) minivib sites. In addition, we present results from a previously unpublished downhole S-wave investigation conducted at four sites in Utah Valley. The locations for each of these investigations are shown in figure 1. Coordinates for the investigation sites are listed in Table 1. With the exception of the P-wave common mid-point (CMP) reflection profile, whose end points are listed, these coordinates are for the midpoint of each velocity sounding. Vs30 and Vs100, also shown in Table 1, are defined as the average shear-wave velocities to depths of 30 and 100 m, respectively, and details of their calculation can be found in Stephenson and others (2005). The information from these studies will be incorporated into components of the urban hazards maps along the Wasatch Front being developed by the U.S. Geological Survey, Utah Geological Survey, and numerous collaborating research institutions. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1159_2007-1159 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions in a Subbasin of the Red River of the North CEOS_EXTRA STAC Catalog 1970-01-01 -106, 37, -84, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231553843-CEOS_EXTRA.umm_json Concern over flooding along rivers in the Prairie Pothole Region has stimulated interest in developing spatially distributed hydrologic models to simulate the effects of wetland water storage on peak river flows. Such models require spatial data on the storage volume and interception area of existing and restorable wetlands in the watershed of interest. In most cases, information on these model inputs is lacking because resolution of existing topographic maps is inadequate to estimate volume and areas of existing and restorable wetlands. Consequently, most studies have relied on wetland area to volume or interception area relationships to estimate wetland basin storage characteristics by using available surface area data obtained as a product from remotely sensed data (e.g., National Wetlands Inventory). Though application of areal input data to estimate volume and interception areas is widely used, a drawback is that there is little information available to provide guidance regarding the application, limitations, and biases associated with such approaches. Another limitation of previous modeling efforts is that water stored by wetlands within a watershed is treated as a simple lump storage component that is filled prior to routing overflow to a pour point or gaging station. This approach does not account for dynamic wetland processes that influence water stored in prairie wetlands. Further, most models have not considered the influence of human-induced hydrologic changes, such as land use, that greatly influence quantity of surface water inputs and, ultimately, the rate that a wetland basin fills and spills. The goals of this study were to (1) develop and improve methodologies for estimating and spatially depicting wetland storage volumes and interceptions areas and (2) develop models and approaches for estimating/simulating the water storage capacity of potentially restorable and existing wetlands under various restoration, land use, and climatic scenarios. To address these goals, we developed models and approaches to spatially represent storage volumes and interception areas of existing and potentially restorable wetlands in the upper Mustinka subbasin within Grant County, Minn. We then developed and applied a model to simulate wetland water storage increases that would result from restoring 25 and 50 percent of the farmed and drained wetlands in the upper Mustinka subbasin. The model simulations were performed during the growing season (May October) for relatively wet (1993; 0.67 m of precipitation) and dry (1987; 0.32 m of precipitation) years. Results from the simulations indicated that the 25 percent restoration scenario would increase water storage by 2732 percent and that a 50 percent scenario would increase storage by 5363 percent. Additionally, we estimated that wetlands in the subbasin have potential to store 11.5720.98 percent of the total precipitation that fell over the entire subbasin area (52,758 ha). Our simulation results indicated that there is considerable potential to enhance water storage in the subbasin; however, evaluation and calibration of the model is necessary before simulation results can be applied to management and planning decisions. In this report we present guidance for the development and application of models (e.g., surface area-volume predictive models, hydrology simulation model) to simulate wetland water storage to provide a basis from which to understand and predict the effects of natural or human-induced hydrologic alterations. In developing these approaches, we tried to use simple and widely available input data to simulate wetland hydrology and predict wetland water storage for a specific precipitation event or a series of events. Further, the hydrology simulation model accounted for land use and soil type, which influence surface water inputs to wetlands. Although information presented in this report is specific to the Mustinka subbasin, the approaches and methods developed should be applicable to other regions in the Prairie Pothole Region. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1161 Historical Changes in the Mississippi-Alabama Barrier Islands and the Roles of Extreme Storms, Sea Level, and Human Activities CEOS_EXTRA STAC Catalog 1970-01-01 -94, 30, -86, 32 https://cmr.earthdata.nasa.gov/search/concepts/C2231555148-CEOS_EXTRA.umm_json An historical analysis of images and documents shows that the Mississippi-Alabama (MS-AL) barrier islands are undergoing rapid land loss and translocation. The barrier island chain formed and grew at a time when there was a surplus of sand in the alongshore sediment transport system, a condition that no longer prevails. The islands, except Cat, display alternating wide and narrow segments. Wide segments generally were products of low rates of inlet migration and spit elongation that resulted in well-defined ridges and swales formed by wave refraction along the inlet margins. In contrast, rapid rates of inlet migration and spit elongation under conditions of surplus sand produced low, narrow, straight barrier segments. [Summary provided by the USGS.] proprietary -USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California ALL STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California CEOS_EXTRA STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary +USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California ALL STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1190 Geophysical Data from Spring Valley to Delamar Valley, East-Central Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -115, 37, -113, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231549251-CEOS_EXTRA.umm_json Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin and these were investigated to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. Cooperative studies described herein have established 1,447 new gravity stations in the region, providing a detailed description of density variations in the middle to upper crust. All previously available gravity data for the study area were evaluated to determine their reliability, prior to combining with our recent results and calculating an up-to-date isostatic residual gravity map of the area. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill in the major valleys of the study area. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a much improved view of subsurface shapes of these basins and provides insights useful for the development of hydrogeologic models for the region. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1202 Geochemistry of Selected Coal Samples from Sumatra, Kalimantan, Sulawesi, and Papua, Indonesia CEOS_EXTRA STAC Catalog 1970-01-01 90, -20, 140, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2231555267-CEOS_EXTRA.umm_json Indonesia is an archipelago of more than 17,000 islands that stretches astride the equator for about 5,200 km in southeast Asia (figure 1) and includes major Cenozoic volcano-plutonic arcs, active volcanoes, and various related onshore and offshore basins. These magmatic arcs have extensive Cu and Au mineralization that has generated much exploration and mining in the last 50 years. Although Au and Ag have been mined in Indonesia for over 1000 years (van Leeuwen, 1994), it was not until the middle of the nineteenth century that the Dutch explored and developed major Sn and minor Au, Ag, Ni, bauxite, and coal resources. The metallogeny of Indonesia includes Au-rich porphyry Cu, porphyry Mo, skarn Cu-Au, sedimentary-rock hosted Au, epithermal Au, laterite Ni, and diamond deposits. For example, the Grasberg deposit in Papua has the world's largest gold reserves and the third-largest copper reserves (Sillitoe, 1994). Coal mining in Indonesia also has had a long history beginning with the initial production in 1849 in the Mahakam coal field near Pengaron, East Kalimantan; in 1891 in the Ombilin area, Sumatra, (van Leeuwen, 1994); and in South Sumatra in 1919 at the Bukit Asam mine (Soehandojo, 1989). Total production from deposits in Sumatra and Kalimantan, from the 19thth century to World War II, amounted to 40 million metric tons (Mt). After World War II, production declined due to various factors including politics and a boom in the world-wide oil economy. Active exploration and increased mining began again in the 1980's mainly through a change in Indonesian government policy of collaboration with foreign companies and the global oil crises (Prijono, 1989). This recent coal revival (van Leeuwen, 1994) has lead Indonesia to become the largest exporter of thermal (steam) coal and the second largest combined thermal and metallurgical (coking) coal exporter in the world market (Fairhead and others, 2006). The exported coal is desirable as it is low sulfur and ash (generally <1 and < 10 wt.%, respectively). Coal mining for both local use and for export has a very strong future in Indonesia although, at present, there are concerns about the strong need for a major revision in mining laws and foreign investment policies (Wahju, 2004; United States Embassy Jakarta, 2004). The World Coal Quality Inventory (WoCQI) program of the U.S. Geological Survey (Tewalt and others, 2005) is a cooperative project with about 50 countries (out of 70 coal-producing countries world-wide). The WoCQI initiative has collected and published extensive coal quality data from the world's largest coal producers and consumers. The important aspects of the WoCQI program are; (1) samples from active mines are collected, (2) the data have a high degree of internal consistency with a broad array of coal quality parameters, and (3) the data are linked to GIS and available through the world-wide-web. The coal quality parameters include proximate and ultimate analysis, sulfur forms, major-, minor-, and trace-element concentrations and various technological tests. This report contains geochemical data from a selected group of Indonesian coal samples from a range of coal types, localities, and ages collected for the WoCQI program. [Summary provided by the USGS.] proprietary USGS_OFR_2007_1208 Geophysical Characterization of Pre-Cenozoic Basement for Hydrocarbon Assessment, Yukon Flats, Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -170, 52, -132, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2231549660-CEOS_EXTRA.umm_json The Cenozoic basins of interior Alaska are poorly understood, but may host undiscovered hydrocarbon resources in sufficient quantities to serve remote villages and for possible export. Purported oil seeps and the regional occurrence of potential hydrocarbon source and reservoir rocks fuel an exploration interest in the 46,000 km2 Yukon Flats basin. Whether hydrocarbon source rocks are present in the pre-Cenozoic basement beneath Yukon Flats is difficult to determine because vegetation and surficial deposits obscure the bedrock geology, only limited seismic data are available, and no deep boreholes have been drilled. Analysis of regional potential field data (aeromagnetics and gravity) is valuable, therefore, for preliminary characterization of basement lithology and structure. We present our analysis as a red-green-blue composite spectral map consisting of: (1) reduced-to-the-pole magnetics (red), (2) magnetic potential (green), and (3) basement gravity (blue). The color and texture patterns on this composite map highlight domains with common geophysical characteristics and, by inference, lithology. The observed patterns yield the primary conclusion that much of the basin is underlain by Devonian to Jurassic oceanic rocks related to the Angayucham and Tozitna terranes (JDat). These rocks are part of a lithologically diverse assemblage of brittlely deformed, generally low-grade metamorphic rocks of oceanic affinity; such rocks probably have little or no potential for hydrocarbon generation. The JDat geophysical signature extends from the Tintina fault system northward to the Brooks Range. Along the eastern edge of the basin, JDat appears to overlie moderately dense and non-magnetic Proterozoic(?) and Paleozoic continental margin rocks. The western edge of the JDat in subsurface is difficult to distinguish due to the presence of magnetic granites similar to those exposed in the Ruby geanticline. In the southern portion of the basin, geophysical patterns indicate the possibility of overthrusting of Cenozoic sediments and underlying JDat by Paleozoic and Proterozoic rocks of the Schwatka sequence. These structural hypotheses provide the basis for an overthrust play within the Cenozoic section just south of the basin. [Summary provided by the USGS.] proprietary @@ -16173,11 +16173,11 @@ USGS_OFR_99-78_1.0 Digital Data Sets Describing Water Use, Toxic Chemical Releas USGS_OFR_99_414_1.0 Geologic Datasets for Weights-of-Evidence Analysis in Northeast Washington--3. Minerals-Related Permits on National Forests, 1967 to 1998 CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -121.25, 47.25, -117, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231554622-CEOS_EXTRA.umm_json This dataset was developed to provide mineral resource data for the region of northeast WA for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:24,000. Permits to explore for and (or) develop mineral resources on Federal lands can be used to indicate locations and types of mineral-related activities on national forests. Permits for these activities require filing of a Notice of Intent to conduct mineral exploration activities and (or) a Plan of Operation, if significant land disturbance results. This compilation of notices and plans for the Colville, Kaniksu, Okanogan, and Wenatchee national forests between 1967 and 1998 is intended for use in combination with geologic and economic information to predict future mineral-related activities in the region. This dataset consists of one Excel 97 spreadsheet file (of99-414.xls) which contains information about permits on national forest lands in northeast Washington State. [Summary provided by the USGS.] proprietary USGS_OFR_99_436 Archive of Sparker Subbottom Data Collected During USGS Cruise ALPH 98013, New York, 10-22 September, 1997 CEOS_EXTRA STAC Catalog 1998-09-10 1998-09-22 -74, 40.16, -73.25, 40.58 https://cmr.earthdata.nasa.gov/search/concepts/C2231550021-CEOS_EXTRA.umm_json This project will generate reconnaissance maps of the sea floor offshore of the New York - New Jersey metropolitan area -- the most heavily populated, and one of the most impacted coastal regions of the United States. The surveys will provide an overall synthesis of the sea floor environment, including seabed texture and bed forms, Quaternary strata geometry, and anthropogenic impact (e.g., ocean dumping, trawling, channel dredging). The goal of this project is to survey the offshore area, the harbor, and the southern shore of Long Island, providing a regional synthesis to support a wide range of management decisions and a basis for further process-oriented investigations. The project is conducted cooperatively with the U.S. Army Corps of Engineer (USACE). This CD-ROM contains digital high resolution seismic reflection data collected during the USGS ALPH 98013 cruise. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. [Summary provided by the USGS.] proprietary USGS_OFR_99_438_1.0 Digital geologic map of part of the Thompson Falls 1:100,000 quadrangle, Idaho CEOS_EXTRA STAC Catalog 1999-01-01 1999-12-31 -116, 47.5, -115, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2231554236-CEOS_EXTRA.umm_json This data set was developed to provide geologic map GIS of the Idaho portion of the Thompson Falls 1:100,000 quadrangle for use in future spatial analysis by a variety of users. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g., 1:62,500 or 1:24,000). The geology of the Thompson Falls 1:100,000 quadrangle, Idaho was compiled by Reed S. Lewis in 1997 onto a 1:100,000-scale topographic base map for input into an Arc/Info geographic information system (GIS). The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps. This GIS consists of two major Arc/Info data sets: one line and polygon file (tf100k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (tfpnt) containing structural data. [Summary provided by the USGS.] proprietary -USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian Assessment Area ALL STAC Catalog 1970-01-01 -87, 31, -77, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550930-CEOS_EXTRA.umm_json The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is a project that studies areas having various susceptibilities to acid deposition from air pollution which are designated on a three tier ranking in the region of the Southern Appalachian Assessment (SAA). The assessment is being conducted by Federal agencies that are members of the Southern Appalachian Man and Biosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high, medium, and low are assigned on the basis of bedrock compositions and their associated soils, and their capacities to neutralize acid precipitation. The data is available in Arc/Info export format. [Summary provided by the USGS] proprietary USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian Assessment Area CEOS_EXTRA STAC Catalog 1970-01-01 -87, 31, -77, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550930-CEOS_EXTRA.umm_json The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is a project that studies areas having various susceptibilities to acid deposition from air pollution which are designated on a three tier ranking in the region of the Southern Appalachian Assessment (SAA). The assessment is being conducted by Federal agencies that are members of the Southern Appalachian Man and Biosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high, medium, and low are assigned on the basis of bedrock compositions and their associated soils, and their capacities to neutralize acid precipitation. The data is available in Arc/Info export format. [Summary provided by the USGS] proprietary +USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian Assessment Area ALL STAC Catalog 1970-01-01 -87, 31, -77, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550930-CEOS_EXTRA.umm_json The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is a project that studies areas having various susceptibilities to acid deposition from air pollution which are designated on a three tier ranking in the region of the Southern Appalachian Assessment (SAA). The assessment is being conducted by Federal agencies that are members of the Southern Appalachian Man and Biosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high, medium, and low are assigned on the basis of bedrock compositions and their associated soils, and their capacities to neutralize acid precipitation. The data is available in Arc/Info export format. [Summary provided by the USGS] proprietary USGS_OFR_aqbound_1.0 Digital boundaries of the Antlers aquifer in southeastern Oklahoma CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -97.4976, 33.7288, -94.4684, 34.3644 https://cmr.earthdata.nasa.gov/search/concepts/C2231550862-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized aquifer boundaries of the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in about an 1,800-square-mile area. The data set includes the outcrop area of the Antlers Sandstone in Oklahoma and areas where the Antlers is overlain by alluvial and terrace deposits and a few small thin outcrops of the Goodland Limestone. Most of the aquifer boundary lines were extracted from published digital geology data sets. Some of the lines were interpolated in areas where the Antlers aquifer is overlain by alluvial and terrace deposits near streams and rivers. The interpolated lines are very similar to the aquifer boundaries published in a ground-water modeling report for the Antlers aquifer. The maps from which this data set was derived were scanned or digitized from maps published at a scale of 1:250,000. This data set is one of four digital map data sets being published together for this aquifer. The four data sets are: aqbound - aquifer boundaries cond - hydraulic conductivity recharg - aquifer recharge wlelev - water-level elevation contours proprietary -USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province ALL STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary +USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province ALL STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary USGS_P1650-a_1.0 Atlas of Relations Between Climatic Parameters and Distributions of Important Trees and Shrubs in North America CEOS_EXTRA STAC Catalog 1970-01-01 -170, 20, -80, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552968-CEOS_EXTRA.umm_json This atlas explores the continental-scale relations between the geographic ranges of woody plant species and climate in North America. A 25-km equal-area grid of modern climatic and bioclimatic parameters was constructed from instrumental weather records. The geographic distributions of selected tree and shrub species were digitized, and the presence or absence of each species was determined for each cell on the 25-km grid, thus providing a basis for comparing climatic data and species' distributions. The relations between climate and plant distributions are explored in graphical and tabular form. The results of this effort are primarily intended for use in biogeographic, paleoclimatic, and global-change research. These web pages provide access to the text, digital representations of figures, and supplemental data files from USGS Professional Paper 1650, chapters A and B. A printed set of these volumes can be ordered from the USGS at a cost of US$63.00. To order, please call or write: USGS Information Services Box 25286 Denver Federal Center Denver, CO 80225 Tel: 303-202-4700; Fax: 303-202-4693 [Summary provided by the USGS.] proprietary @@ -16192,8 +16192,8 @@ USGS_SESC_ExtinctFish Extinct North American Freshwater Fishes CEOS_EXTRA STAC C USGS_SESC_ImperiledFish American Fisheries Society Imperiled Freshwater and Diadromous Fishes of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551557-CEOS_EXTRA.umm_json About: This website presents the 2008 American Fisheries Society Endangered Species Committee list of imperiled North American freshwater and diadromous fishes. The committee considered continental fishes native to Canada, Mexico, and the United States, evaluated their conservation status and determined the major threats impacting these taxa. We use the terms taxon (singular) or taxa (plural) to include named species, named subspecies, undescribed forms, and distinct populations as characterized by unique morphological, genetic, ecological, or other attributes warranting taxonomic recognition. Undescribed taxa are included, based on the above diagnostic criteria in combination with known geographic distributions and documentation deemed of scientific merit, as evidenced from publication in peer-reviewed literature, conference abstracts, unpublished theses or dissertations, or information provided by recognized taxonomic experts. Although we did not independently evaluate the taxonomic validity of undescribed taxa, the committee adopted a conservative approach to recognize them on the basis of prevailing evidence which suggests that these forms are sufficiently distinct to warrant conservation and management actions. Summary: This is the third compilation of imperiled (i.e., endangered, threatened, vulnerable) plus extinct freshwater and diadromous fishes of North America prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1989, imperilment of inland fishes has increased substantially. This list includes 700 extant taxa representing 133 genera and 36 families, a 92% increase over the 364 listed in 1989. The increase reflects the addition of distinct populations, previously non-imperiled fishes, and recently described or discovered taxa. Approximately 39% of described fish species of the continent are imperiled. There are 230 vulnerable, 190 threatened, and 280 endangered extant taxa; 61 taxa are presumed extinct or extirpated from nature. Of those that were imperiled in 1989, most (89%) are the same or worse in conservation status; only 6% have improved in status, and 5% were delisted for various reasons. Habitat degradation and nonindigenous species are the main threats to at-risk fishes, many of which are restricted to small ranges. Documenting the diversity and status of rare fishes is a critical step in identifying and implementing appropriate actions necessary for their protection and management. Maps: In collaboration with the World Wildlife Fund, the committee developed a map of freshwater ecoregions that combines spatial and faunistic information derived from Maxwell and others (1995), Abell and others (2000; 2008), U.S. Geological Survey Hydrologic Unit Code maps (Watermolen 2002), Atlas of Canada (2003), and Commission for Environmental Cooperation (2007). Eighty ecoregions were identified based on physiography and faunal assemblages of the Atlantic, Arctic, and Pacific basins. Each taxon on the list was assigned to one or more ecoregions that circumscribes its native distribution. A variety of sources were used to obtain distributional information, most notably Lee and others (1980), Hocutt and Wiley (1986), Page and Burr (1991), Behnke (2002), Miller and others (2005), numerous state and provincial fish books for the United States and Canada, and the primary literature, including original taxonomic descriptions. Taxa were also associated with the states or provinces where they naturally occur or occurred in the past. proprietary USGS_SESC_ImperiledFreshwaterOrganisms Imperiled Freshwater Organisms of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231549663-CEOS_EXTRA.umm_json This website provides access to maps and lists of imperiled freshwater organisms of North America as determined by the American Fisheries Society (AFS) Endangered Species Committee (ESC). At this website, one can view lists of animals by freshwater ecoregion, by state or province boundary, and plot distributions of these same creatures by ecoregions or political boundaries. Both the AFS and U.S. Geological Survey (USGS) have a long standing commitment to the advancement of aquatic sciences and sharing that information with the public. Since 1972, the ESC has been tracking the status of imperiled fishes and aquatic invertebrates in North America. Recently, the fish (2008) and crayfish (2007) subcommittees provided revised status lists of at-risk taxa, and the mussel and snail subcommittees are in the process of completing similar revisions. Historically, the revised AFS lists of imperiled fauna have been published in Fisheries. With rapid advances in technology and information transfer, there is a growing need to provide to stakeholders immediate and dynamic data on imperiled resources. The USGS is a leader in aquatic resource research that effectively disseminates results from those studies to the public through print and internet media. A Memorandum Of Understanding formally establishes an agreement between the AFS and USGS to create this website that will serve as a conduit for information exchange about imperiled aquatic organisms of North America. proprietary USGS_SESC_SnailStatus American Fisheries Society List of Freshwater Snails from Canada and the United States CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551686-CEOS_EXTRA.umm_json About: This website presents the 2013 American Fisheries Society Endangered Species Committee list of freshwater snails (Gastropods) of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa. Summary: This is the first conservation status review for freshwater snails (gastropods) of Canada and the United States by the American Fisheries Society's Endangered Species Freshwater Gastropod Subcommittee. The goals of this contribution are to provide: 1) a current and comprehensive taxonomic authority list for all native freshwater gastropods of Canada and the United States, 2) provincial and state distributions as presently understood, 3) a conservation assessment, and, 4) references on their biology, distribution and conservation. Freshwater gastropods occupy every type of aquatic habitat ranging from subterranean aquifers to brawling montane headwater creeks. Gastropods are ubiquitous invertebrates and frequently dominate aquatic invertebrate biomass. Of the 703 gastropods documented by Johnson et al. (2013), 74% are imperiled or extinct (278 endangered, 102 threatened, 73 vulnerable, and 67 are considered extinct); only 157 species are considered stable. Map queries display species distributions in provinces and states in which they are believed to occur or occurred in the past, but considerable fieldwork is required to determine exact geographic limits of species. We hope this list stimulates a surge in the study of freshwater gastropods. Supporting Literature: Supporting literature for the North American freshwater gastropods assessment are organized alphabetically by state and province, followed by national, regional, and other general references. This literature compilation is not comprehensive, but offers considerable information for individuals interested in freshwater snails. Recovery Examples: Although the gastropod fauna of Canada and the United States is beleaguered by multiple forms of habitat loss, the fauna is resilient and capable of remarkable recovery when suitable habitat is available. Three examples of recovery demonstrate the inherent reviving potential of freshwater gastropods. Images of the incredible diversity of freshwater snails are presented in plates and photo gallery. Maps: Each species on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. Resources used to obtain distributional information include state and regional publications. proprietary -USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary +USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary USGS_SIR-5079_MSRiverFloodMaps Development of flood-inundation maps for the Mississippi River in Saint Paul, Minnesota CEOS_EXTRA STAC Catalog 1970-01-01 -93.15028, 44.90479, -92.999855, 44.97016 https://cmr.earthdata.nasa.gov/search/concepts/C2231549022-CEOS_EXTRA.umm_json Digital flood-inundation maps for a 6.3-mile reach of the Mississippi River in Saint Paul, Minnesota, were developed through a multi-agency effort by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers and in collaboration with the National Weather Service. The inundation maps, which can be accessed through the U.S. Geological Survey Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ and the National Weather Service Advanced Hydrologic Prediction Service site at http://water.weather.gov/ahps/inundation.php , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage at the Mississippi River at Saint Paul (05331000). The National Weather Service forecasted peak-stage information at the streamgage may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the Mississippi River by means of a one-dimensional step-backwater model. The hydraulic model was calibrated using the most recent stage-discharge relation at the Robert Street location (rating curve number 38.0) of the Mississippi River at Saint Paul (streamgage 05331000), as well as an approximate water-surface elevation-discharge relation at the Mississippi River at South Saint Paul (U.S. Army Corps of Engineers streamgage SSPM5). The model also was verified against observed high-water marks from the recent 2011 flood event and the water-surface profile from existing flood insurance studies. The hydraulic model was then used to determine 25 water-surface profiles for flood stages at 1-foot intervals ranging from approximately bankfull stage to greater than the highest recorded stage at streamgage 05331000. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from high-resolution topography data, to delineate potential areas flooded and to determine the water depths within the inundated areas for each stage at streamgage 05331000. The availability of these maps along with information regarding current stage at the U.S. Geological Survey streamgage and forecasted stages from the National Weather Service provides enhanced flood warning and visualization of the potential effects of a forecasted flood for the city of Saint Paul and its residents. The maps also can aid in emergency management planning and response activities, such as evacuations and road closures, as well as for post-flood recovery efforts. proprietary USGS_SOFIA_75_29_flows Baseline hydrologic data collection along the I-75 - State Road 29 corridor in the Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549536-CEOS_EXTRA.umm_json The objectives of this study are to develop and continue a program of surface water flow monitoring across I-75 and SR 29 in the I-75 corridor from Snake Road west to SR 29 and SR 29 from I-75 south to USGS site 02291000 Barron River near Everglades, Florida. Quarterly discharge measurements will be made along both reaches to assess hydrologic flow patterns and evaluate the feasibility of creating a stage-discharge/index-velocity relationship for this area. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary USGS_SOFIA_75_29_hydro_data Hydrologic Data Collected along I-75/SR29 corridor in Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549847-CEOS_EXTRA.umm_json The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary @@ -16208,8 +16208,8 @@ USGS_SOFIA_CarbonFlux Carbon Flux and Greenhouse Gasses of Restored and Degraded USGS_SOFIA_Ding_Darling_baseline Ding Darling National Wildlife Refuge - Greater Everglades Baseline Information and Response to CERP CEOS_EXTRA STAC Catalog 2009-10-01 2014-09-30 -82.5, 26.3, -81.6, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231549274-CEOS_EXTRA.umm_json The greater Everglades Restoration program includes a management plan for the C-43 Canal, or Caloosahatchee River. This plan affects the quantity, quality, and timing of freshwater releases at control structure S-79 at Franklin Locks. Freshwater contributions are from Lake Okeechobee, and farming runoff along the canal from Lake Okeechobee to the town of Alva. This study will provide basic information on the effects on the quality of water entering J. N. Ding Darling National Wildlife Refuge as the result of freshwater releases at control structure S-79 proprietary USGS_SOFIA_EDEN_grid_shapefile_v02 EDEN Grid Shapefile CEOS_EXTRA STAC Catalog 1970-01-01 -81.51, 24.7, -79.9, 27.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231549862-CEOS_EXTRA.umm_json This shapefile serves as a net (fishnet or grid) to be placed over the South Florida study area to allow for sampling within the 400 meter cells (grid cells or polygons). The origin and extent of the Everglades Depth Estimation Network (EDEN) grid were selected to cover not only existing Airborne Height Finder (AHF) data and current regions of interest for Everglades restoration, but to cover a rectangular area that includes all landscape units (USACE, 2004) and conservation areas in place at the time of its development. This will allow for future expansion of analyses throughout the Greater Everglades region should resources allow and scientific or management questions require it. Combined with the chosen extent, the 400m cell resolution produces a grid that is 675 rows and 375 columns.. The shapefile contains the 253125 grid cells described above. Some characteristics of this grid, such as the size of its cells, its origin, the area of Florida it is designed to represent, and individual grid cell identifiers, could not be changed once the grid database was developed. These characteristics were selected to design as robust a grid as possible and to ensure the grid’s long-term utility. proprietary USGS_SOFIA_EDEN_proj Everglades Depth Estimation Network (EDEN) CEOS_EXTRA STAC Catalog 1999-01-01 2008-10-28 -81.3, 25, -80.16, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550596-CEOS_EXTRA.umm_json The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground elevation modeling, and water-surface modeling that provides scientists and managers with current (1999-present), on-line water-depth information for the entire freshwater portion of the Greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to:1) guide large-scale field operations, 2) integrate hydrologic and ecological responses, and 3) support biological and ecological assessments that measure ecosystem responses to the implementation of the comprehensive Everglades Restoration plan (CERP) from the U.S. Army Corps of Engineers in 1999. Research has shown that relatively high-resolution data are needed to explicitly represent variations in the Everglades topography and vegetation that are important for landscape analysis and modeling. The EDEN project will provide a better representation of water depths if elevation variation within each 400-meter grid cell can be taken into account. The EDEN network provides a framework to integrate data collected by other agencies in a common quality-assured database. In addition to real-time network, collaboration among agencies will provide the EDEN project with valuable historic vegetation and water-depth data. This is the first time these data have been compiled and analyzed as a collective set. proprietary -USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 CEOS_EXTRA STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 ALL STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary +USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 CEOS_EXTRA STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary USGS_SOFIA_Ever_hydr_FB_dynam Interrelationships of Everglades Hydrology and Florida Bay Dynamics CEOS_EXTRA STAC Catalog 1850-01-01 2004-12-31 -80.89015, 25.1004, -80.39827, 25.471722 https://cmr.earthdata.nasa.gov/search/concepts/C2231554284-CEOS_EXTRA.umm_json This interdisciplinary synthesis project is designed to identify and document the interrelation of Everglades’ hydrology and tidal dynamics of Florida Bay on ecosystem response to past environmental changes, both natural and human imposed. The project focuses on integrating historical, hydrological, and ecological findings of scientific investigations within the Southern Inland and Coastal System (SICS), which encompasses the transition zone between the wetlands of Taylor Slough and C-111 canal and nearshore embayments of Florida Bay. In the ecological component, hindcast simulations of historical flow events are being developed for ecological analyses. The Across Trophic Level System Simulation (ATLSS) ecological modeling team is collaborating with the SICS hydrologic modeling team to develop the necessary hydrologic inputs for refined indicator species models. The interconnected freshwater wetland and coastal marine ecosystems of south Florida have undergone numerous human disturbances, including the introduction of exotic species and the alteration of wetland hydroperiods, landscape characteristics, and drainage patterns through implementation of the extensive canal and road system and the expansion of agricultural activity. In this project, collaborative efforts are focused on documenting the impact of past hydrological and ecological changes along the southern Everglades interface with Florida Bay by reconstructing past hydroperiods and investigating the correlation of human-imposed and natural impacts on hydrological changes with shifts in biotic species. The primary objectives are to identify the historical effects of past management practices, to integrate refined hydrological and ecological modeling efforts at indicator species levels to identify cause-and-effect relationships, and to produce a report that documents findings that link hydrological and ecological changes to management practices, wherever evident. proprietary USGS_SOFIA_Fbbslmap Florida Bay Bottom Salinity Maps CEOS_EXTRA STAC Catalog 1994-11-01 1996-12-31 -81.167, 24.83, -80.33, 25.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231549334-CEOS_EXTRA.umm_json The maps show the bottom salinity for Florida Bay at 5ppt salinity intervals approximately every other month beginning in November 1994 through December 1996. Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay. proprietary USGS_SOFIA_Fbbtypes Florida Bay Bottom Types map - USGS_SOFIA_Fbbtypes CEOS_EXTRA STAC Catalog 1996-01-01 1997-01-31 -81.25, 24.75, -80.25, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553376-CEOS_EXTRA.umm_json The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987). The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated. proprietary @@ -16233,12 +16233,12 @@ USGS_SOFIA_MeHg_degrad_rates Methylmercury Degradation Rates CEOS_EXTRA STAC Cat USGS_SOFIA_SF_CIR_DOQs Color Infrared Digital Orthophoto Quadrangles for the South Florida Ecosystem Area CEOS_EXTRA STAC Catalog 1994-01-01 1999-12-31 -82.2, 24.6, -80.1, 27.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231553946-CEOS_EXTRA.umm_json The digital orthophoto quadrangles (DOQ's) produced by the USGS for the South Florida Ecosystem Initiative iare color-infrared, 1-meter ground resolution quadrangle images covering 3.75 minutes of latitude by 3.75 minutes of longitude at a map scale of 12,000. Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquadrangle (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75 minutes of latitude by 3.75 minutes of longitude) image cast on the Universal Transverse Mercator projection (UTM) on the North American Datum of 1983 (NAD83). The geographic extent of the DOQ is equivalent to a quarter-quadrangle plus the overedge ranges from a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The radiometric image brightness values are stored as 256 gray levels, ranging from 0 to 255. The standard, uncompressed gray scale DOQ format contains an ASCII header followed by a series of 8-bit image data lines. The keyword-based, ASCII header may vary in the number of data entries. The header is affixed to the beginning of the image and is composed of strings of 80 characters with an asterisk (*) as character 79 and an invisible newline character as character 80. Each keyword string contains information for either identification, display, or registration of the image. Additional strings of blanks are added to the header so that the length of a header line equals the number of bytes in a line of image data. The header line will be equal in length to the length of an image line. If the sum of the byte count of the header is less than the sample count of one DOQ image line, then the remainder of the header is padded with the requisite number of 80 character blank entries, each terminated with an asterisk and newline character. The objective of this project was to provide color infrared (CIR) digital orthophoto coverage for the entire south Florida ecosystem area. The main advantage of a digital orthophoto is that it gives a measurable image free of distortion. Therefore, the digital orthophotos for the ecosystem provide multi-use base images for identifying natural and manmade features and for determining their extent and boundaries; the images can also be used for the interpretation and classification of these areas. proprietary USGS_SOFIA_SnailKites_AppleSnails Comprehensive Monitoring Plan for Snail Kites and Apple Snails in the Greater Everglades CEOS_EXTRA STAC Catalog 2010-01-01 2015-12-31 -81.6, 25, -80.6, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554049-CEOS_EXTRA.umm_json The endangered snail kite (Rostrhamus sociabilis) is a wetland-dependent raptor feeding almost exclusively on a single species of aquatic snail, the Florida apple snail (Pomacea paludosa). The viability of the kite population is dependent on the hydrologic conditions (both short-term and long-term) that (1) maintain sufficient abundances and densities of apple snails, and (2) provide suitable conditions for snail kite foraging and nesting, which include specific vegetative community compositions. Many wetlands comprising its range are no longer sustained by the natural processes under which they evolved (USFWS 1999, RECOVER 2005), and not necessarily characteristic of the historical ecosystems that once supported the kite population (Bennetts and Kitchens 1999, Martin et al. 2008). Natural resource managers currently lack a fully integrative approach to managing hydrology and vegetative communities with respect to the apple snail and snail kite populations. At this point in time the kite population is approximately 1,218 birds (Cattau et al 2012), down from approximately 4000 birds in 1999. It is imperative to improve our understanding hydrological conditions effecting kite reproduction and recruitment. Water Conservation area 3-A, WCA3A, is one of the 'most critical' wetlands comprising the range of the kite in Florida (see Bennetts and Kitchens 1997, Mooij et al. 2002, Martin et al. 2006, 2008). Snail kite reproduction in WCA3A sharply decreased after 1998 (Martin et al. 2008), and alarmingly, no kites were fledged there in 2001, 2005, 2007, or 2008. Bowling (20098) found that juvenile movement probabilities away (emigrating) from WCA3A were significantly higher for the few kites that did fledge there in recent years (i.e. 2003, 2004, 2006) compared to those that fledged there in the 1990s. The paucity of reproduction in and the high probability of juveniles emigrating from WCA3A are likely indicative of habitat degradation (Bowling 20098, Martin et al. 2008), which may stem, at least in part, from a shift in water management regimes (Zweig and Kitchens 2008). Given the recent demographic trends in snail kite population, the need for a comprehensive conservation strategy is imperative; however, information gaps currently preclude our ability to simultaneously manage the hydrology in WCA3A with respect to vegetation, snails, and kites. While there have been significant efforts in filling critical information gaps regarding snail kite demography (e.g., Martin et al. 2008) and variation in apple snail density to water management issues (e.g., Darby et al. 2002, Karunaratne et al. 2006, Darby et al. 2008), there is surprisingly very little information relevant for management that directly links variation in apple snail density with the demography and behavior of snail kites (but see Bennetts et al. 2006). The U.S. Fish and Wildlife Service (USFWS), the U. S. Army Corps of Engineers, and the Florida Fish and Wildlife Conservation Commission (FWC) have increasingly sought information pertaining to the potential effects of specific hydrological management regimes with respect to the apple snail and snail kite populations, as well as the vegetative communities that support them. proprietary USGS_SOFIA_YY_Males Development of YY male technology to control non-native fishes in the Greater Everglades CEOS_EXTRA STAC Catalog 2009-10-01 -81, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231552421-CEOS_EXTRA.umm_json Dozens of non-native fish species have established throughout south Florida (including Everglades National Park, Big Cypress National Preserve, Biscayne National Park and various state and private lands). Thus far, research on these species has focused on documenting their distributions, natural history, and physiological tolerances. Research is beginning to emerge on interactions of native species with non-natives, although it is only in the early stages. Research on control of non-native fishes in South Florida is also lacking, although it is potentially the most important and useful to natural resource managers. At present, the only management techniques available to control non-native fishes are physical removal, dewatering or ichthyocides. Unfortunately, all of these methods negatively impact native fauna as well as the targeted non-native fishes and require a great deal of effort (and therefore, funding). Herein, we propose a research program focused on applying a genetic technique common in aquaculture to control of non-native fishes. This proposal focuses on developing a technique (YY supermales) to control a non-native fish in South Florida (African jewelfish Hemichromis letourneuxi). However, the concept can be applied to a wide variety of species, including other fishes (e.g., brown hoplo Hoplosternum littorale), invasive applesnails (Pomacea spp.), the Australian red claw crayfish (Cherax spp.) and the green mussel (Perna veridis). proprietary -USGS_SOFIA_aerial-photos Aerial Photos of the 1940s CEOS_EXTRA STAC Catalog 1940-02-14 1940-08-21 -81.9, 24.41, -79.98, 26.22 https://cmr.earthdata.nasa.gov/search/concepts/C2231554384-CEOS_EXTRA.umm_json The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number. A foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html proprietary USGS_SOFIA_aerial-photos Aerial Photos of the 1940s ALL STAC Catalog 1940-02-14 1940-08-21 -81.9, 24.41, -79.98, 26.22 https://cmr.earthdata.nasa.gov/search/concepts/C2231554384-CEOS_EXTRA.umm_json The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number. A foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html proprietary +USGS_SOFIA_aerial-photos Aerial Photos of the 1940s CEOS_EXTRA STAC Catalog 1940-02-14 1940-08-21 -81.9, 24.41, -79.98, 26.22 https://cmr.earthdata.nasa.gov/search/concepts/C2231554384-CEOS_EXTRA.umm_json The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number. A foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html proprietary USGS_SOFIA_analysis_hist_wq Analysis of Historic Water Quality Data CEOS_EXTRA STAC Catalog 1960-01-01 2005-09-30 -81.55, 25.11, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553759-CEOS_EXTRA.umm_json "The Big Cypress National Preserve (BICY), the Everglades National Park (EVER), and Loxahatchee National Wildlife Refuge (LOX) are water-dominated ecosystems that are susceptible to water-quality impacts. A comprehensive analysis of historical water-quality and ancillary data is needed to direct the restoration of the Everglades and the adoption of water-quality standards in BICY, EVER, and LOX because of their designations as Outstanding Florida Waters. Big Cypress National Preserve (BICY), Everglades National Park (EVER)), and Loxahatchee National Wildlife Refuge (LOX) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY, EVER, and LOX are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 ""internal marsh"" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 ""internal"" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two parks has yet to be performed. Water-quality data have been collected at 14 internal marsh sites in LOX by the U.S. Fish and Wildlife Service for over 10 years. These samples have been analyzed by SFWMD laboratory. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality. The initial study area was in BICY and EVER; the study area was extended into LOX in 2003." proprietary USGS_SOFIA_asr_data_lake_okee Aquifer Storage and Recovery Data (Lake Okeechobee) CEOS_EXTRA STAC Catalog 1999-08-01 2000-05-31 -81.08, 26.35, -80.28, 27.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231554472-CEOS_EXTRA.umm_json The objective of this project was to determine geochemically significant water-quality characteristics of possible aquifer storage and recovery (ASR) source and receiving waters north of Lake Okeechobee and at a site along the Hillsboro Canal. The data from this study will be combined with similar information on the detailed composition of aquifer materials in ASR receiving zones to develop geochemical models. Such models are needed to evaluate the possible chemical reactions that may change the physical properties of the aquifer matrix and/or the quality of injected water prior to recovery. proprietary -USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program CEOS_EXTRA STAC Catalog 1996-01-01 -81.30333, 24.696152, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231554119-CEOS_EXTRA.umm_json The ATLSS (Across Trophic Level System Simulation) program addresses CERP’s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading. An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS’s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions There are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds. Models currently available are: ATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1) Spatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1) Spatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17) GIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1) Landscape Models: High Resolution Topography (HRT - Version 1.4.8) Vegetation productivity (HTDAM - Version 1.1) High Resolution Hydrology (HRH - Version 1.4.8) proprietary USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program ALL STAC Catalog 1996-01-01 -81.30333, 24.696152, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231554119-CEOS_EXTRA.umm_json The ATLSS (Across Trophic Level System Simulation) program addresses CERP’s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading. An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS’s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions There are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds. Models currently available are: ATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1) Spatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1) Spatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17) GIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1) Landscape Models: High Resolution Topography (HRT - Version 1.4.8) Vegetation productivity (HTDAM - Version 1.1) High Resolution Hydrology (HRH - Version 1.4.8) proprietary +USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program CEOS_EXTRA STAC Catalog 1996-01-01 -81.30333, 24.696152, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231554119-CEOS_EXTRA.umm_json The ATLSS (Across Trophic Level System Simulation) program addresses CERP’s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading. An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS’s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions There are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds. Models currently available are: ATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1) Spatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1) Spatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17) GIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1) Landscape Models: High Resolution Topography (HRT - Version 1.4.8) Vegetation productivity (HTDAM - Version 1.1) High Resolution Hydrology (HRH - Version 1.4.8) proprietary USGS_SOFIA_avian_ecology_spoonbills Avian Ecology of the Greater Everglades (Roseate Spoonbill and Limpkins) CEOS_EXTRA STAC Catalog 2002-10-01 2005-09-30 -81.25, 24.875, -80.375, 25.375 https://cmr.earthdata.nasa.gov/search/concepts/C2231549705-CEOS_EXTRA.umm_json "The primary objectives of our research are to (1) quantify the changes in spatial distribution and success of nesting spoonbills relative to hydrologic patterns, (2) test hypotheses about the causal mechanisms for observed changes, (3) establish a science-based criteria for nesting distribution and success to be used as a performance measure for hydrologic restoration, and (4) estimate demographic parameters. To meet these objectives, we will use a combined field/modeling approach. Based on previous and concurrent research, hypothesized relationships between hydrology, fish populations, and spoonbill nesting distribution and success will be expressed in a simple, but spatially explicit, conceptual model. Field data will be collected and compared with predicted responses to monitor changes in spoonbill nesting as hydrologic restoration is implemented, and to test the hypothesized mechanisms for observed changes. Variation of hydrologic conditions among years and locations is a virtual certainty; thus we will treat this variation in a quasi-experimental framework where the variation in wet and dry season conditions constitutes a series of ""natural experiments"". Our project is designed to evaluate the effect of hydrologic restoration on the nesting distribution and success of Roseate Spoonbills (Ajaia ajaia) in Florida Bay and surrounding mangrove estuarine habitats. This project is further designed to test hypotheses about the causal mechanisms of observed changes. The Everglades ecosystem has suffered extensive degradation over the past century, including an 85-90% decrease in the numbers of wading birds. Previous monitoring of Roseate Spoonbills in Florida Bay over the past 50 years has shown that this species responds markedly to changes in hydrology and corresponding changes in prey abundance and availability. Shifts in nesting distribution and declines in nest success have been attributed to declines in prey populations as a direct result of water management. Consequently, the re-establishment of spoonbill colonies in northeast Florida Bay is one change predicted under a conceptual model of the mangrove estuarine transition zone of Florida Bay. Changes in nesting distribution and success will further be used as a performance measure for success of restoration efforts and will be incorporated in a model linking mangrove fish populations and spoonbills to alternative hydrologic scenarios." proprietary USGS_SOFIA_ba_geologic_data Biscayne Aquifer geologic data CEOS_EXTRA STAC Catalog 1998-01-01 2005-12-31 -80.6, 25.5, -80.3, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550961-CEOS_EXTRA.umm_json This report from which the data is taken identifies and characterizes candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using GPR, cyclostratigraphy, borehole geophysical logs, continuously drilled cores, and paleontology. About 60 mi of GPR profiles were acquired and are used to calculate the depth to shallow geologic contacts and hydrogeologic units, image karst features, and produce a qualitative perspective of the porosity distribution within the upper part of the karstic Biscayne aquifer in the Lake Belt area of north-central Miami-Dade County. . Descriptions of lithology, rock fabric, cyclostratigraphy, and depositional environments of 50 test coreholes were linked to geophysical data to provide a more refined hydrogeologic framework for the upper part of the Biscayne aquifer. Interpretation of depositional environments was constrained by analysis of depositional textures and molluscan and benthic foraminiferal paleontology. Digital borehole images were used to help quantify large-scale vuggy porosity. Preliminary heat-pulse flowmeter data were coupled with the digital borehole image data to identify potential ground-water flow zones. The objectives of this cooperative project were to identify and characterize candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using ground-penetrating radar, cyclostratigraphy, borehole geophysical logs, continuously drilled cores and paleontology. In 1998, the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD), initiated a study to provide a regional-scale hydrogeologic framework of a shallow semiconfining unit within the Biscayne aquifer of southeastern Florida. Initially, the primary objective was to characterize and delineate a low-permeability zone in the upper part of the Biscayne aquifer that spans the base of the Miami Limestone and uppermost part of the Fort Thompson Formation. Delineation of this zone was to aid development of a conceptual hydrogeologic model to be used as input into the SFWMD Lake Belt ground-water model. The approximate area encompassed by the conceptual hydrogeologic model is shown as the study area at http://sofia.usgs.gov/exchange/cunningham/bbwelllocation.html. Subsequent analysis of the preliminary data suggested hydraulic compartmentalization occurred within the Biscayne aquifer, and that there was a need to characterize and delineate ground-water flow zones and relatively low-permeability zones within the upper part of the Biscayne aquifer. Consequently, preliminary results suggested that the historical understanding of the porosity and preferential pathways for Biscayne aquifer ground-water flow required considerable revision. This project was carried out in cooperation with the South Florida Water Management District (SFWMD). proprietary USGS_SOFIA_bbcw_geophysical Biscayne Bay Coastal Wetlands Geophysical Data CEOS_EXTRA STAC Catalog 2004-01-01 -80.4, 25.4, -80.3, 25.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231549059-CEOS_EXTRA.umm_json The objectives of this data acquisition project were to complete the downhole geophysical logging including video and flowmeter logging of two core holes (9A and 11A), which are the deepest wells at monitor well sites 0009AB and 0011AB. The goal of the Comprehensive Everglades Restoration Plan Biscayne Bay Coastal Wetlands Project (BBCWP) is to rehydrate wetlands and reduce point-source discharge to Biscayne Bay. The BBCWP will replace lost overland flow and partially compensate for the reduction in ground-water seepage by redistributing, through a spreader system, available surface water entering the area from regional canals. The proposed redistribution of freshwater flow across a broad front is expected to restore or enhance freshwater wetlands, tidal wetlands, and near shore bay habitat. A critical component of the BBCWP is the development of a realistic representation of ground-water flow within the karst Biscayne aquifer. Mapping these ground-water flow units is key to the development of models that simulate ground-water flow from the Everglades and urban areas through the coastal wetlands to Biscayne Bay. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the Biscayne Bay Coastal Wetlands Project Delivery Team installed two monitor-well sites and collected the necessary detailed hydrogeologic data. The L-31 North Canal Seepage Management Pilot Project is intended to curtail easterly seepage emanating from within Everglades National Park (ENP). The pilot project is examining various seepage management technologies as well as operational changes that could be implemented to reduce the water losses from ENP. This project is in close proximity to Biscayne Bay so an effort has been made to combine ongoing work efforts at the two project areas. The distribution of seepage into the L-31 North Canal and beneath it is not known with any degree of certainty today. A canal draw down experiment was conducted to provide additional field data that will be utilized to refine seepage estimates in the study area as well as determine aquifer parameters in the study area. This project was funded by the USGS Florida Integrated Science Center and the South Florida Water Management District (SFWMD). proprietary @@ -16255,8 +16255,8 @@ USGS_SOFIA_chron_isotope_geochem_FL_Keys Chronology and Isotope Geochemistry of USGS_SOFIA_coastal_ever_tjslll_04 Coastal Everglades Wetlands: Hydrology, Vegetation and Sediment Dynamics CEOS_EXTRA STAC Catalog 2002-10-01 2009-12-31 -81.75, 25, -80.25, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231550711-CEOS_EXTRA.umm_json This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration). The objective of this project is to conduct integrated studies to develop an understanding of how hydrologic parameters, disturbance, sediment, and global change (e.g. sea level) influence ecological systems in coastal wetlands. Hydrological factors studied include surface and groundwater stage and conductivity, surface water flow, nutrient concentration and suspended sediment. Fire, freeze, hurricanes and lightning strikes are among the disturbances that are important in coastal wetlands. Sediment elevation changes in coastal wetlands as a function of plant growth and decomposition, accretion or erosion due to tides and surface water flows, fire (in freshwater peats) and hurricanes. Both positive and negative feedbacks on sediment elevation have been discovered. Sea level has increased almost 30cm in the past century. The influence of continued sea level rise on CERP for restoring coastal areas is unknown at present. These questions have been addressed by the development of an integrated network of sampling and measurement sites where instrumentation is collocated. Many sites have surface and ground water sampling wells, sediment elevations tables and permanent vegetation plots. Transects, with both permanent plots and hydrology sampling wells, have been established across the mangrove - marsh ecotone to examine the influence of hydrology and fires (both partly controllable), freezes and sea level (not manageable) on the position of the ecotone. proprietary USGS_SOFIA_coastal_grads Coastal Gradients of Flow, Salinity, and Nutrients CEOS_EXTRA STAC Catalog 2003-01-01 2010-12-31 -81.125, 25.08, -80.08, 25.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231552103-CEOS_EXTRA.umm_json Ten monitoring stations will be operated and maintained along the southwest coast of ENP, the Everglades wetlands, and along the coastlines of northeastern Florida Bay and northwest Barnes Sound. Data collected at these 10 stations will include water level, velocity, salinity, and temperature. Three stations (Upstream North River, North River, and West Highway Creek) will also include automatic samplers for the collection of water samples and determination of Total Nutrients (TN and TP). These 10 stations will complement information currently being generated through an existing network of 20 hydrologic monitoring stations of on-going USGS projects. By combining data collected from the ten monitoring stations and the existing monitoring network, information will be available across 9 generalized coastal gradients or transects. Data collected at all flow sites will be transmitted in near real time (every 1 or 4 hours) by way of satellite telemetry to the automated data processing system (ADAPS) database in the USGS Center for Water and Restoration Studies (CWRS) in Miami and available for CERP purposes. In addition to data from monitoring stations described above, salinity surveys will be performed along these 9 generalized transects, and these will include salinity, temperature, and GPS data from boat-mounted systems. Surveys will be performed regularly on a quarterly basis and twice following hydrologic events, totaling a maximum of 6 surveys per year. The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. The MAP also presents the system-wide performance measures representative of the natural and human systems found in South Florida that will be evaluated to help determine the success of CERP. These system-wide performance measures address the responses of the South Florida ecosystem that the CERP is explicitly designed to improve, correct, or otherwise directly affect. A separate Performance Measure Documentation Report being prepared by RECOVER provides the scientific, technical, and legal basis for the performance measures. This project is intended to support the Greater Everglades (GE) Wetlands module of the MAP and is directly linked to the monitoring or supporting enhancement component In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for the MAP activities. proprietary USGS_SOFIA_coastal_grads_salsurveys Coastal Gradients Salinity Surveys CEOS_EXTRA STAC Catalog 2003-12-11 -81, 25.16, -80.38, 25.57 https://cmr.earthdata.nasa.gov/search/concepts/C2231553403-CEOS_EXTRA.umm_json Ten monitoring stations were operated and maintained along the southwest coast of ENP, the Everglades wetlands, and along the coastlines of northeastern Florida Bay and northwest Barnes Sound. Data collected at these 10 stations includes water level, velocity, salinity, and temperature. These 10 stations will complement information currently being generated through an existing network of 20 hydrologic monitoring stations of on-going USGS projects. The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. The MAP also presents the system-wide performance measures representative of the natural and human systems found in South Florida that will be evaluated to help determine the success of CERP. These system-wide performance measures address the responses of the South Florida ecosystem that the CERP is explicitly designed to improve, correct, or otherwise directly affect. A separate Performance Measure Documentation Report being prepared by RECOVER provides the scientific, technical, and legal basis for the performance measures. This project is intended to support the Greater Everglades (GE) Wetlands module of the MAP and is directly linked to the monitoring or supporting enhancement component In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for the MAP activities. proprietary -USGS_SOFIA_coupled_sw-gw_model A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands CEOS_EXTRA STAC Catalog 1995-01-01 2009-09-30 -81.56, 25.02, -80, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553820-CEOS_EXTRA.umm_json This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development. The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B). proprietary USGS_SOFIA_coupled_sw-gw_model A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands ALL STAC Catalog 1995-01-01 2009-09-30 -81.56, 25.02, -80, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553820-CEOS_EXTRA.umm_json This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development. The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B). proprietary +USGS_SOFIA_coupled_sw-gw_model A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands CEOS_EXTRA STAC Catalog 1995-01-01 2009-09-30 -81.56, 25.02, -80, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553820-CEOS_EXTRA.umm_json This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development. The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B). proprietary USGS_SOFIA_dade_biscayne_limit_west_arc Approximate Western Limit of the Biscayne Aquifer in Dade County, USGS WRIR 90-4108, figure 16 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.874054, 25.422379, -80.652664, 25.98292 https://cmr.earthdata.nasa.gov/search/concepts/C2231550143-CEOS_EXTRA.umm_json The map shows the approxiamte western limit of the Biscayne aquifer in Miami-Dade County. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_dade_config_base_biscayne_arc Configuration of the Base of the Biscayne Aquifer in Dade County, USGS WRIR 90-4108, figure 16 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.858925, 25.187017, -80.11909, 25.986544 https://cmr.earthdata.nasa.gov/search/concepts/C2231549896-CEOS_EXTRA.umm_json The map shows the altitude below sea level of the base of the Biscayne aquifer in Miami-Dade County. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_dade_config_base_glime_arc Configuration of the Base of the Gray Limestone Aquifer in Dade County, Fl, USGS WRIR 90-4108, figure 15 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.85567, 25.2942, -80.331, 25.994343 https://cmr.earthdata.nasa.gov/search/concepts/C2231554187-CEOS_EXTRA.umm_json Contours of the altitude below sea level of the base of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary @@ -16266,8 +16266,8 @@ USGS_SOFIA_dawmet Ecosystem History: Terrestrial and Fresh-Water Ecosystems of s USGS_SOFIA_discharge_tamiami_canal Discharge Data (Tamiami Canal) CEOS_EXTRA STAC Catalog 1986-01-01 2001-12-31 -81.5, 25.75, -80.5, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231553115-CEOS_EXTRA.umm_json The data are from the following four stations: Station 02288800 - Tamiami Canal Outlets, Monroe to Carnestown; Station 02288900 - Tamiami Canal Outlets, 40-Mile Bend to Monroe, near Miami, FL; Station 02289040 - Tamiami Canal Outlets, Levee 67A to 40-Mile Bend, near Miami, FL; Station 02289060 - Tamiami Canal Outlets, Levee 30 to Levee 67A, near Miami, FL. The data were compiled from records from 1986 to 1999 in the USGS Ft. Lauderdale, FL office of the Water Resources Discipline in 2000. Each station has numerous individual flow measurements at gages that were used in the calculation of the mean flow for each station. The data were collected by USGS personnel and the gages are maintained and operated by USGS Ft. Lauderdale office personnel. Canals are a major water-delivery component of the south Florida ecosystem. They interact with surrounding flow systems and waterbodies, either directly through structure discharges and levee overflows or indirectly through levee seepage and leakage, and thereby quantitatively affect wetland hydroperiods as well as estuarine salinities. Knowledge of this flow interaction, as well as timing, extent, and duration of inundation that it contributes to, is needed to identify and eliminate any potential adverse effects of altered flow conditions and transported constituents on vegetation and biota. Comprehensive analytical tools and methods are needed to assess the effects of nutrient and contaminant loads from agricultural and urban run-off entering canals and thereby conveyed into connected wetlands and other adjoining coastal ecosystems. These data from the individual gages were transferred to electronic form to provide a better understanding of the distribution of flow from north to south under the Tamiami Trail to aid in decisions about future changes to flow along the Trail. proprietary USGS_SOFIA_dk_merc_cycl_bio Mercury Cycling and Bioaccumulation CEOS_EXTRA STAC Catalog 2000-10-01 2006-12-31 -81.33137, 24.67165, -80.22201, 25.890877 https://cmr.earthdata.nasa.gov/search/concepts/C2231550667-CEOS_EXTRA.umm_json This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA’s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure. Although ecological impacts from phosphorous contamination have become synonymous with water quality in south Florida, especially for Everglades restoration, there are several other contaminants presently entering the Everglades that may be of equal or greater impact, including: pesticides, herbicides, polycyclic aromatic hydrocarbons, and trace metals. This project focuses on mercury, a sparingly soluble trace metal that is principally derived from atmospheric sources and affects the entire south Florida ecosystem. Mercury interacts with another south Florida contaminant, sulfur, that is derived from agricultural runoff, and results in a problem with potentially serious toxicological impacts for all the aquatic food webs (marine and freshwater) in the south Florida ecosystem. The scientific focus of this project is to examine the complex interactions of these contaminants (synergistic and antagonistic), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological and chemical components of this ecosystem. However, it remains uncertain what overall effects will occur as these components react to the perturbations (especially the biological and chemical components) and toward what type of 'new ecosystem' the Everglades will evolve. The approaches used by this study have been purposefully chosen to yield results that should be directly useable by land management and restoration decision makers. Presently, we are addressing several major questions surrounding the mercury research field, and the Everglades Restoration program: (l) What, if any, ecological benefit to the Everglades would be realized if mercury emissions reductions would be enacted, and over what time scales (years or tens of years) would improvements be realized? (2) What is the role of old mercury (previously deposited and residing in soils and sediment) versus new mercury (recent deposition) in fueling the mercury problem? (3) In the present condition, is controlling sulfur or mercury inputs more important for reducing the mercury problem in the Everglades? (4) Does sulfur loading have any additional ecological impacts that have not been realized previously (e.g., toxicity to plant and animals) that may be contributing to an overall decreased ecological health? (5) Commercial fisheries in the Florida Bay are contaminated with mercury, is this mercury derived from Everglades runoff or atmospheric runoff? (6) What is the precise role of carbon (the third member of the 'methylmercury axis of evil', along with sulfur and mercury), and do we have to be concerned with high levels of natural carbon mobilization from agricultural runoff as well? (7) Hundreds of millions of dollars are being, or have been spent, on STA construction to reduce phosphorus loading to the Everglades, however, recently constructed STAs have yielded the highest known concentration of toxic methylmercury; can STA operations be altered to reduce methylmercury production and maintain a high level of phosphorus retention over extended periods of time? The centerpiece of our research continues to be the use of environmental chambers (enclosures or mesocosms), inside which we conduct dosing experiments using sulfate, dissolved organic carbon and mercury isotopic tracers. The goal of the mesocosm experiments is to quantify the in situ ecological response to our chemical dosing, and to also determine the ecosystem recovery time to the doses. proprietary USGS_SOFIA_eco_assess_risk_toxics Ecological Risk Assessment of Toxic Substances in the Greater Everglades Ecosystem: Wildlife Effects and Exposure Assessment CEOS_EXTRA STAC Catalog 2000-10-01 2004-09-30 -81.125, 25.125, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231551985-CEOS_EXTRA.umm_json This project will be carried out in several locations throughout those areas critical to the South Florida Restoration Initiative. These areas include: 1) Water Conservation Areas 1, 2, and 3 of the Central Everglades, 2) Everglades National Park, 3) Loxahatchee National Wildlife Refuge, 4) Big Cypress National Preserve, 5) multiple Miami Metropolitan area canals and drainages, and 6) restoration related STA’s (STA’s 1-6) adjacent to the Everglades. Specific site selections will be based upon consideration of USACE restoration plans and upon discussions with other place-based and CESI approved projects. The overall objectives are characterize the exposure of wildlife to contaminants within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk, and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. Historically, little consideration has been given to environmental chemical stressors/contaminants within the ecosystem restoration efforts for the Greater Everglades Ecosystem. The restoration is primarily guided by determining and restoring the historical relationships between ecosystem function and hydrology. The restoration plan was formulated to restore the natural hydrology and therefore, the resultant landscape patterns, bio-diversity and wildlife abundance. However, additional efforts need to consider the role that chemical contaminants such as pesticides and other inorganic/organic contaminants play in the structure and function of the resultant South Florida ecosystems. Indeed, the current level of agriculture and expanding urbanization and development necessitate that more emphasis be placed on chemical contaminants within this sensitive ecosystem and the current restoration efforts. The primary goal of the proposed project, therefore, is to develop an improved understanding of the exposure/fate (i.e. degradation, metabolism, dissipation, accumulation and transport) and potential ecological effects produced as a result of chemical stressors and their interactions in South Florida freshwater and wetland ecosystems. The overall objectives are to evaluate the risk posed by contaminants to biota within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. The specific objectives of this project are to: 1. Assess current exposure and potential adverse effects for appropriate receptors/species within the South Florida ecosystems with some emphasis on DOI trust species. These efforts will determine whether natural populations are significantly exposed to a variety of chemical stressors/contaminants, such as mercury, chlorinated hydrocarbon pesticides, historic and/or current use agricultural chemicals, and/or mixtures, as well as document lethal and non-lethal adverse effects in multiple health, physiologic and/or endocrine endpoints. 2. Assess exposure and potential adverse effects for appropriate species within South Florida as a function of restoration implementation. proprietary -USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 CEOS_EXTRA STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 ALL STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary +USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 CEOS_EXTRA STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary USGS_SOFIA_eco_hist_db_version 3 Ecosystem History of South Florida Estuaries Data CEOS_EXTRA STAC Catalog 1994-02-24 2008-03-20 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552167-CEOS_EXTRA.umm_json The Ecosystem History Access Database contains listings of all sites (modern and core), modern monitoring site survey information, and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Scientists over the past few decades have noticed that the South Florida ecosystem has become increasingly stressed. The purposes of the ecosystem history projects (started in 1995) are to determine what south Florida's estuaries have looked like over time, how they have changed, and what is the rate and frequency of change. To accomplish this, shallow sediment cores are collected within the bays, and the faunal and floral remains, sediment geochemistry, and shell biochemistry are analyzed. Modern field data are collected from the same region as the cores and serve as proxies to allow accurate interpretation of past depositional environments. The USGS South Florida Ecosystem History Project is designed to integrate studies from a number of researchers compiling data from terrestrial, marine, and freshwater ecosystems within south Florida. The project is divided into 3 regions: Biscayne Bay and the Southeast coast, Florida Bay and the Southwest coast, and Terrestrial and Freshwater Ecosystems of Southern Florida. The purpose of the projects is to provide information about the ecosystem's recent history based on analyses of paleontology, geochemistry, hydrology, and sedimentology of cores taken from the south Florida region. Data generated from the South Florida Ecosystem History project will be integrated to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters. Biscayne Bay is of interest to scientists because of the rapid urbanization that has occurred in the Miami area and includes Biscayne National Park. Dredging, propeller scars, and changes in freshwater input have altered parts of Biscayne Bay. Currently, the main freshwater input to Biscayne Bay is through the canal system, but many scientists believe subsurface springs used to introduce fresh groundwater into the Bay ecosystem. Study of the modern environment and core sediments from Biscayne Bay will provide important information on past salinity and seagrass coverage which will be useful for predicting future change within the Bay. Plant and animal communities in the South Florida ecosystem have undergone striking changes over the past few decades. In particular, Florida Bay has been plagued by seagrass die-offs, algal blooms, and declining sponge and shellfish populations. These alterations in the ecosystem have traditionally been attributed to human activities and development in the region. Scientists at the U.S. Geological Survey (USGS) are studying the paleoecological changes taking place in Florida Bay in hopes of understanding the physical environment to aid in the restoration process. As in Biscayne Bay, scientists must first determine which changes are part of the natural variation in Florida Bay and which resulted from human activities. To answer this question, scientists are studying both modern samples and piston cores that reveal changes over the past 150-600 years. These two types of data complement each other by providing information about the current state of the Bay, changes that occurred over time, and patterns of change. Terrestrial ecosystems of South Florida have undergone numerous human disturbances, ranging from alteration of the hydroperiod, fire history, and drainage patterns through implementation of the canal system to expansion of the agricultural activity to the introduction of exotic species such as Melalueca, Australian pine, and the Pepper Tree. Over historical time, dramatic changes in the ecosystem have been documented and these changes attributed to various human activities. However, cause-and-effect relationships between specific biotic and environmental changes have not been established scientifically. One part of the South Florida Ecosystem History group of project is designed to document changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted from documented human activities, and to establish the baseline level of variability in the South Florida ecosystem to estimate whether the observed changes are greater than what would occur naturally. Specific goals of this part of the project are to 1) document the patterns of floral and faunal changes at sites throughout southern Florida over the last 150 years, 2) determine whether the changes occurred throughout the region or whether they were localized, 3) examine the floral and faunal history of the region over the last few millennia, 4) determine the baseline level of variability in the communities prior to significant human activity in the region, and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region. proprietary USGS_SOFIA_eco_hist_swcoast_srs_04 Ecosystem History of the Southwest Coast-Shark River Slough Outflow Area CEOS_EXTRA STAC Catalog 2003-10-01 2008-09-30 -81.75, 25, -80.83, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231554376-CEOS_EXTRA.umm_json The objectives of this project are to document impacts of changes in salinity, water quality, coastal plant and animal communities and other critical ecosystem parameters on a subdecadal-centennial scale in the southwest coastal region (from Whitewater Bay, north to the 10,000 Islands), and to correlate these changes with natural events and resource management practices. Emphasis will be placed on 1) determining the amount, timing and sources of freshwater influx (groundwater vs. runoff) into the coastal ecosystem prior to and since significant anthropogenic alteration of flow; and 2) determining whether the rate of mangrove and brackish marsh migration inland has increased since 20th century water diversion and what role sealevel rise might play in the migration. First, the environmental preferences and distributions of modern fauna and flora are established through analyses of modern samples in south Florida estuaries and coastal systems. Much of these data have already been obtained through project work conducted in Florida Bay and the terrestrial Everglades starting in 1995. These modern data are used as proxies for interpreting the historical data from Pb-210 and C-14 dated sediment cores based on assemblage analysis. On the basis of USGS data obtained from cores in Florida Bay and Biscayne Bay, the temporal span of the cores should be at a minimum the last 150 years; this is in agreement with University of Miami data showing sedimentation rates in Whitewater Bay to be approximately 1cm/year. For the estuarine/coastal ecosystems, a multidisciplinary, multiproxy approach will be utilized on cores from a transect from Whitewater Bay north to 10,000 Islands. Biochemical analyses of shells and chemical analyses of sediments will be used to refine data on salinity and nutrient supply, and isotopic analyses of shells will determine sources of water influx into the system. Nutrient analyses will be conducted to determine historical patterns of nutrient influx. To examine the inland migration of the mangrove/coastal marsh ecotone, transects from the mouth of the Shark and Harney Rivers inland into Shark River slough will be taken. These cores will be evaluated for floral remains, nutrients, charcoal, and if present, faunal remains. This project will provide 1) baseline data for restoration managers and hydrologic modelers on the amount and sources of freshwater influx into the southwest coastal zone and the quality of the water, 2) the relative position of the coastal marsh-mangrove ecotone at different periods in the past, and 3) data to test probabilities of system response to restoration changes. One of the primary goals of the Central Everglades Restoration Plan (CERP) is to restore the natural flow of water through the terrestrial Everglades and into the coastal zones. Historically, Shark River Slough, which flows through the central portion of the Everglades southwestward, was the primary flow path through the Everglades Ecosystem. However, this flow has been dramatically reduced over the last century as construction of canals, water conservation areas and the Tamiami Trail either retained or diverted flow from Shark River Slough. The reduction in flow and changes in water quality through Shark River have had a profound effect on the freshwater marshes and the associated coastal ecosystems. Additionally, the flow reduction may have shifted the balance of fresh to salt-water inflow along coastal zones, resulting in an acceleration of the rate of inland migration of mangroves into the freshwater marshes. For successful restoration to occur, it is critical to understand how CERP and the natural patterns of freshwater flow, precipitation, and sea level rise will affect the future maintenance of the mangrove-freshwater marsh ecotone and the coastal environment. proprietary USGS_SOFIA_eden_dem_cm_nov07_nc Everglades Depth Estimation Network (EDEN) November 2007 Digital Elevation Model for use with EDENapps CEOS_EXTRA STAC Catalog 1995-01-01 2007-12-31 -81.36353, 25.229605, -80.22176, 26.683613 https://cmr.earthdata.nasa.gov/search/concepts/C2231551925-CEOS_EXTRA.umm_json This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface. This file is a modification of the eden dem released in October of 2007 (i.e., eden_em_oct07) in which the elevation values have been converted from meters (m) to centimeters(cm) for use by EDEN applications software. This file is intended specifically for use in the EDEN applications software. Aside from this difference in horizontal units, the following documentation applies. These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network. proprietary @@ -16343,8 +16343,8 @@ USGS_SOFIA_integrating_manatee Effects of hydrological restoration on manatees: USGS_SOFIA_karst_model Linking a conceptual karst hydrogeologic model of the Biscayne aquifer to ground-water flow simulations from Everglades National Park to Biscayne National Park - Phase 1 CEOS_EXTRA STAC Catalog 2005-01-01 2009-12-31 -81.5, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550454-CEOS_EXTRA.umm_json This project in being undertaken to develop a high-resolution 3-dimensional karst hydrogeologic framework of the Biscayne aquifer between Everglades National Park (ENP) and Biscayne National Park (BNP) using test coreholes, borehole geophysical logging, cyclostratigraphy, hydrostratigraphy, and hydrologic modeling. The development of an expanded conceptual karst hydrogeologic framework in this project will be used to assist development of procedures for numeric simulations to improve the monitoring and assessment of the response of the ground-water system to hydrologic changes caused by CERP-related changes in stage within the Everglades wetlands, including seepage-management pilot project implementation. Specifically, the development of procedures for ground-water modeling of the karst Biscayne aquifer in the area of Northern Shark Slough will help determine the appropriate hydrologic response to rainfall and translate that information into appropriate performance targets for input into the design and operating rules to manage water levels and flow volumes for the two Seepage Management Areas. Mapping of the karstic stratiform ground-water flow passageways in the Biscayne aquifer is recent and limited to a small area of Miami-Dade County adjacent to the Everglades wetlands. Extension of this karst framework between the Everglades wetlands and coastal Biscayne Bay will aid in the simulation of coupled ground-water and surface-water flows to Biscayne Bay. The development of procedures for modeling in the karst Biscayne aquifer will useful to the establishment of minimum flows and levels to the Biscayne Bay and seasonal flow patterns. Also, these improved procedures for simulations will assist in ecologic modeling efforts of Biscayne Bay coastal estuaries. Research is needed to determine how planned Comprehensive Everglades Restoration Plan (CERP) seepage control actions within the triple-porosity karstic Biscayne aquifer in the general area of Northeast Shark Slough will affect ground-water flows and recharge between the Everglades wetlands and Biscayne Bay. A fundamental problem in the simulation of karst ground-water flow and solute transport is how best to represent aquifer heterogeneity as defined by the spatial distribution of porosity, permeability, and storage. The triple porosity of the Biscayne aquifer is principally: (1) matrix of interparticle and separate-vug porosity, providing much of the storage and, under dynamic conditions, diffuse-carbonate flow; (2) touching-vug porosity creating stratiform ground-water flow passageways; and (3) less common conduit porosity composed mainly of bedding plane vugs, thin solution pipes, and cavernous vugs. The objectives of this project are to: (1) build on the Lake Belt area hydrogeologic framework (recently completed by the principal investigator), mainly using cyclostratigraphy and digital optical borehole images to map porosity types and develop the triple-porosity karst framework between the Everglades wetlands and Biscayne Bay; and (2) develop procedures for numerical simulation of ground-water flow within the Biscayne aquifer multi-porosity system. proprietary USGS_SOFIA_kendall_stable_isotopes Application of Stable Isotope Techniques to Identifying Foodweb Structure, Contaminant Sources, and Biogeochemical Reactions in the Everglades CEOS_EXTRA STAC Catalog 1995-03-01 1999-10-31 -81.0202, 25.2475, -80.3069, 26.6712 https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.umm_json "This is the largest isotope foodweb study ever attempted in a marsh ecosystem, and combines detailed, long-term, trophic and biogeochemical studies at selected well-monitored USGS/SFWMD/FGFFC sites with limited synoptic foodweb data from over 300 sites sampled during 1996 and 1999 by a collaboration with the EPA-REMAP program. The preliminary synthesis of the biota isotopes at USGS and 1996 REMAP sites provides a mechanism for extrapolating the detailed foodwebs developed at the intensive USGS sites to the entire marsh system sampled by REMAP. Furthermore, this unique study strongly suggests that biota isotopes provide a simple means for monitoring how future ecosystem changes affect the role of periphyton (vs. macrophyte-dominated detritus) in local foodchains, and for predictive models for foodweb structure and MeHg bioaccumulation under different proposed land-management changes. Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is ""getting the water right"". However, the underlying goal is actually to re-establish, as much as possible, the ""pre-development"" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major ""long-term"" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the Everglades (ACME) project in 1996 and was made a separate project in 2000." proprietary USGS_SOFIA_kitchens_snail_kite Estimation of Critical Parameters in Conjunction with Monitoring the Florida Snail Kite Population CEOS_EXTRA STAC Catalog 2000-10-01 2003-09-30 -83.32674, 24.229189, -79.897285, 29.138569 https://cmr.earthdata.nasa.gov/search/concepts/C2231550848-CEOS_EXTRA.umm_json Life history traits and the population dynamics of the snail kite may vary considerably across space and over time. Understanding the influence of environmental (spatial and temporal) variation on demographic parameters is essential to understanding the population dynamics of a given species. Recognition of information needs for management decisions and conservation strategies has resulted in an increased emphasis on correlations to spatial and temporal environmental variation in relation to demographic studies. The purpose if this study is to provide valid estimates of the demographic parameters of the snail kite, including temporal and spatial variability due to environmental factors. These parameters will be used in a predictive model of the snail kite already developed under the ATLSS Program (Mooij et al. 2002). The snail kite (Rostrhamus sociabilis) is an endangered species that resides in the highly fluctuating ecosystem in the central and southern Florida wetlands. Many demographic traits, such as stage-dependent survival, reproduction, and movement of the snail kite vary both temporally and spatially. How these demographic parameters vary as a function of environmental conditions, hydrology in particular, is crucial for understanding how the snail kite will respond to proposed changes in water regulation in South and Central Florida. In particular, these data are needed for testing and improving the existing spatially-explicit, individual-based ATLSS snail kite model, developed by Mooij and Bennetts, which has recently been delivered to Department of Interior and other agencies (Mooij et al. 2002). From these data and the model, projections can be made on snail kite response to any hydrologic scenario. Also, continued estimates will be made of the rate of population growth. Assessing the demographic parameters is critical for identifying and evaluating the effectiveness of management actions and conservation strategies. In addition, new modeling techniques, such as structural modeling are being explored to better understand the effects of hydrology on the snail kite. The objectives of this project are the following: 1. To monitor the status of the snail kite population trends in central and southern Florida. 2. To provide estimates of demographic parameters for the spatially explicit individual-based model in ATLSS. 3. To collaborate with Dr. Wolf Mooij of the Netherlands Institute of Ecology to use snail kite data to validate the snail kite model. proprietary -USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" ALL STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary +USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" ALL STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary USGS_SOFIA_lake_okee_bathy_data Lake Okeechobee Bathymetry data CEOS_EXTRA STAC Catalog 2001-09-01 -81.125, 26.625, -80.5, 27.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231550957-CEOS_EXTRA.umm_json The data from the bathymetric mapping of Lake Okeechobee are provided in two forms: as raw data files and as elevation contour maps. High resolution acoustic bathymetric surveying is a proven method to map sea and lake floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day lakebed in Lake Okeechobee. This information can be used by water-management decision-makers to better assess the water capacity of the lake at various levels. proprietary USGS_SOFIA_land_margin_ecosystems Dynamics of Land Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate CEOS_EXTRA STAC Catalog 2002-10-01 2009-12-31 -81.75, 25, -80.25, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552313-CEOS_EXTRA.umm_json This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration). proprietary USGS_SOFIA_lbwfbay Ecosystem History: Florida Bay and Southwest Coast CEOS_EXTRA STAC Catalog 1995-02-01 2003-02-06 -80.75, 24.75, -80.33, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553226-CEOS_EXTRA.umm_json "Recent negative trends in the Florida Bay ecosystem have been attributed to human activities, however, neither the natural patterns of change, nor the pre-human baseline for the environment have been determined. The major objectives of this project are 1) to determine patterns of faunal and floral change over the last 150-200 years, and 2) to explore associations between biotic changes and anthropogenically-induced changes and/or natural changes in the physical environment. Environmental managers and policy makers responsible for restoring the Everglades ecosystem to a ""natural state"" can use these data to make economical and realistic decisions about restoration goals and to determine interim steps to ameliorate further damage to the ecosystem. The history of the ecosystem during the last 150-200 years is studied by analysis of faunal and floral assemblages from a series of shallow cores taken in Florida Bay. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro-and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay (Ecosystems History: Terrestrial and Fresh Water Ecosystems of Southern Florida Project and Ecosystems History: Biscayne Bay and the southeast coast Project). The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. This project is one component of an interdisciplinary study of the ecosystem history in Florida Bay. A number of USGS and other agencies scientist's are examining a series of shallow cores (~1-2 m) collected from Florida Bay. By studying the patterns of change that have occurred in the ecosystem over the last two centuries, we gain insight into the natural processes, including the natural range of variability that exists within any ecosystem. We can then determine the degree to which anthropogenic-induced change has effected the system. This understanding is critical to the restoration effort; otherwise we will be attempting to restore the system to a targeted snapshot in time, without understanding how realistic or obtainable those goals are. The ecosystem history component of the initiative will save time and money by providing realistic, economical, obtainable goals. Our component of this study is to analyze the down-core faunal and floral assemblages, over the last 150-200 years. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro- and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment." proprietary @@ -16403,12 +16403,12 @@ USGS_arapbase_Version 1.0, July 22, 1998 COVERAGE ARAPBASE -- Structure contours USGS_benchmark_1.0 Locations of NASQAN benchmark stations CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -127.042595, 27.19216, -69.387886, 48.367382 https://cmr.earthdata.nasa.gov/search/concepts/C2231550268-CEOS_EXTRA.umm_json This coverage was created for the 1990-91 National Water Summary. The coverage shows locations of NASQAN benchmark stations. Procedures_Used: The point coverage was created from data taken from U.S. Geological Survey computer files. proprietary USGS_cir89_Version 1.0 Color-infrared composite of Landsat data for the Sarcobatus Flat area of the Death Valley regional flow system, Nevada and California, 1989 CEOS_EXTRA STAC Catalog 1989-06-21 1989-06-21 -117.216324, 36.997658, -116.66944, 37.40421 https://cmr.earthdata.nasa.gov/search/concepts/C2231554772-CEOS_EXTRA.umm_json "The data set was created to determine phreatophyte boundaries used in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California"". The raster-based, color-infrared composite was derived from Landsat Thematic Mapper imagery data acquired during June 1989 for the Sarcobatus Flat area of the Death Valley regional flow system. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study. The raster-based, color-infrared composite (CIR) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1989 for the Death Valley regional flow system, Nevada and California. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the classification process. The wavelengths of these channels correspond to those used for a CIR composite. The data range of each channel is divided into eight divisions. The 512 possible combinations are then reduced to 256. A color table of red, green, and blue values is created for display of the image. Sixteen possible color values exist for each color. These values are scaled between 0 and 255. The image is reduced from more than 16 million colors to 256 colors. Reviews The CIR image for 1989 was checked for consistency and accuracy during the data processing. Two external reviews were done. The reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. The Landsat Entity-identification number is LT5040034008917210." proprietary USGS_cira92_Version 1.0 Color-infrared composite of Landsat data for the Death Valley regional flow system, Nevada and California, 1992 CEOS_EXTRA STAC Catalog 1992-06-01 1992-06-13 -117.550385, 35.378323, -115.251015, 37.653557 https://cmr.earthdata.nasa.gov/search/concepts/C2231551442-CEOS_EXTRA.umm_json "This data set was created to determine phreatophyte boundaries for use in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California"". The raster-based, color-infrared composite was derived from Landsat Thematic Mapper imagery data acquired during June 1992 for the Death Valley regional flow system. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study. The raster-based, color-infrared composite (CIR) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for the Death Valley ground-water flow system, Nevada and California. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the classification process. The wavelengths of these channels correspond to those used for a CIR composite. The data range of each channel is divided into eight divisions. The 512 possible combinations are then reduced to 256. A color table of red, green, and blue values is created for display of the image. Sixteen possible color values exist for each color. These values are scaled between 0 and 255. The image is reduced from more than 16 million colors to 256 colors." proprietary -USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary -USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary +USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary -USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary +USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary +USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary USGS_erf1_Version 1.2, August 01, 1999 ERF1 -- Enhanced River Reach File 1.2 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.8169, 23.247017, -65.55541, 48.19323 https://cmr.earthdata.nasa.gov/search/concepts/C2231552175-CEOS_EXTRA.umm_json ERF1 was designed to be a digital data base of river reaches capable of supporting regional and national water-quality and river-flow modeling and transport investigations in the water-resources community. ERF1 has been recently used at the U.S. Geological Survey to support interpretations of stream water-quality monitoring network data (see Alexander and others, 1996; Smith and others, 1995). In these analyses, the reach network has been used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water-quality models of stream nutrient transport. The digital data set ERF1 includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1)to ensure the hydrologic integrity of the digital reach traces and to quantify the time of travel of river reaches and reservoirs [see U.S.EPA (1996) for a description of the original RF1]. Any use of trade, product, or firm names is for descriptive proprietary USGS_erfi-2_2.0, November 19, 2001 ERF1-2 -- Enhanced River Reach File 2.0 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.85945, 23.243486, -65.37739, 48.194405 https://cmr.earthdata.nasa.gov/search/concepts/C2231551816-CEOS_EXTRA.umm_json "This report describes the process of enhancements to the stream reach network, ERF1, which is an enhanced version of EPA's RF1. The U.S. Environmental Protection Agency's reach file (RF1) is a database of interconnected stream segments or ""reaches"" that comprise the surface water drainage system for the United States. A variety of attributes have been assigned to each reach in support of spatial analysis and mapping applications. ERF1-2 was designed to be a digital database of river reaches capable of supporting regional and national water-quality and river-flow modeling by the water-resources community. ERF1, on which ERF1-2 is based, is used at the U.S. Geological Survey to support national-level water-quality monitoring modeling with the SPARROW model (see Alexander and others, 2000; Smith and others, 1997). In the current and earlier analyses, the reach network is used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water- quality models of stream nutrient transport. Acknowledgements The authors would like to thank Richard Smith, a co-developer of the SPARROW approach, Kristine Verdin, and Stephen Char, all of the U.S. Geological Survey, for providing technical assistance. The reviewers of this report, Dave Stewart, and Mike Wieczorek, are also acknowledged for their significant contributions. The digital segmented network based on watershed boundaries, ERF1-2, includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1) (USEPA, 1996; DeWald and others, 1985) to support national and regional-scale surface water-quality modeling. Alexander and others (1999) developed ERF1, which assessed the hydrologic integrity of the digital reach traces and calculated the mean water time-of-travel in river reaches and reservoirs. ERF1-2 serves as the foundation for SPARROW (Spatially Referenced Regressions (of nutrient transport) On Watershed) modeling. Within the context of a Geographic Information System, SPARROW estimates the proportion of watersheds in the conterminous U.S. with outflow concentrations of several nutrients, including total nitrogen and total phosphorus, (Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997). This version of the network expands on ERF1 (version 1.2; Alexander et al. 1999), and includes the incremental and total drainage area derived from 1-kilometer (km) elevation data for North America. Previous estimates of the water time-of-travel were recomputed for reaches with water- quality monitoring sites that included two reaches. The mean flow and velocity estimates for these split reaches are based on previous estimation methods (Alexander et al., 1999) and are unchanged in ERF1-2. Drainage area calculations provide data used to estimate the contribution of a given nutrient to the outflow. Data estimates depend on the accuracy of node connectivity. Reaches split at water- quality or pesticide-monitoring sites indicate the source point for estimating the contribution and transport of nutrients and their loads throughout the watersheds. The ERF1-2 coverage extends the earlier ERF1 coverage by providing digital-elevation-model (DEM-based estimates of reach drainage area founded on the 1-kilometer data for North America (Verdin, 1996; Verdin and Jenson, 1996). A 1-kilometer raster grid of ERF1-2 projected to Lambert Azimuthal Equal Area, NAD 27 Datum (Snyder, 1987), was merged with the HYDRO1K flow direction data set (Verdin and Jenson, 1996) to generate a DEM-based watershed grid, ERF1_2WS. The watershed boundaries are maintained in a raster (grid cell) format as well as a vector (polygon) format for subsequent model analysis. Both the coverage, ERF1-2, and the grid, ERF1-2WS are available at: ""http://water.usgs.gov/orh/nrwww/sparrow_section5_nolan.pdf"". Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology." proprietary USGS_etsite_Version 1.0 Evapotranspiration sites within the Ash Meadows and Oasis Valley discharge areas, Nevada CEOS_EXTRA STAC Catalog 1993-01-01 1999-01-01 -116.73254, 36.37027, -116.296814, 37.063698 https://cmr.earthdata.nasa.gov/search/concepts/C2231552240-CEOS_EXTRA.umm_json The digital data set was created to display site locations at which micrometeorological data were collected in Ash Meadows and Oasis Valley, Nev. The digital data set provides locations and general descriptions of sites instrumented to collect micrometeorological data from which mean annual ET rates were computed. Sites are located in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through present. Introduction The digital data set was created in cooperation with the U.S. Department of Energy. The data set was created as part of a study to refine current estimates of ground-water discharge from the major discharge areas of the Death Valley regional flow system. This digital data set provides locations and general descriptions of sites instrumented during recent studies of evapotranspiration in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through 2001. Reviews The digital data set has gone through a multi-level, quality-control process to ensure that the data are a reasonable representation of source points. Reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although the data set has been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data and related materials. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non-proprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. Users should exercise caution and judgment in applying these data, and be aware that errors may be present in any or all of the digital image data. If errors are encountered in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. proprietary @@ -16500,50 +16500,86 @@ VH_bathy_99_1 Bathymetric data of Long and Tryne Fjords at Vestfold Hills, Antar VIIRSJ1_L1_2 NOAA-20 VIIRS Level-1 Data, version 2 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1570117995-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L1_GEO_2 NOAA-20 VIIRS Geolocation Product Data, version 2 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1940926976-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB). proprietary VIIRSJ1_L1_OBC_2 NOAA-20 VIIRS On-Board Calibartion Product Data, version 2 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2512681107-OB_DAAC.umm_json The VIIRS Geolocation Onboard Calibrator (OBC)-IP file contains solar diffuser observations, the associated gain state and HAM side information, and all engineering and housekeeping data, including unscaled data from the Solar Diffuser Stability Monitor (SDSM)/VIIRS Earth View Radiometric Calibration Unit and the Solar Diffuser GEO angles. proprietary +VIIRSJ1_L2_IOP_2022.0 NOAA-20 VIIRS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928893-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L2_IOP_NRT_2022.0 NOAA-20 VIIRS Level-2 Regional Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928892-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L2_IOP_NRT_R2022.0 NOAA-20 VIIRS Regional Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494492-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L2_IOP_R2022.0 NOAA-20 VIIRS Regional Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494493-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L2_OC_2022.0 NOAA-20 VIIRS Level-2 Regional Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928899-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L2_OC_NRT_2022.0 NOAA-20 VIIRS Level-2 Regional Ocean Color (OC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928895-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L2_OC_NRT_R2022.0 NOAA-20 VIIRS Regional Ocean Color (OC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494496-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L2_OC_R2022.0 NOAA-20 VIIRS Regional Ocean Color (OC) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494497-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L2_SST3_2024.0 NOAA-20 VIIRS Level-2 Regional Triple-window Sea Surface Temperature (SST3) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805761-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L2_SST3_NRT_2024.0 NOAA-20 VIIRS Level-2 Regional Triple-window Sea Surface Temperature (SST3) - Near Real Time (NRT) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805758-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L2_SST_2024.0 NOAA-20 VIIRS Level-Regional Regional 11µm Day/Night Sea Surface Temperature (SST) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805774-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L2_SST_NRT_2024.0 NOAA-20 VIIRS Level-2 Regional 11µm Day/Night Sea Surface Temperature (SST) - Near Real Time (NRT) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805767-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary +VIIRSJ1_L3b_CHL_2022.0 NOAA-20 VIIRS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928906-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_CHL_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Chlorophyll (CHL) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928904-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_CHL_NRT_R2022.0 NOAA-20 VIIRS Global Binned Chlorophyll (CHL) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494498-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_CHL_R2022.0 NOAA-20 VIIRS Global Binned Chlorophyll (CHL) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494499-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_IOP_2022.0 NOAA-20 VIIRS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928908-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_IOP_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928907-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_IOP_NRT_R2022.0 NOAA-20 VIIRS Global Binned Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494500-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_IOP_R2022.0 NOAA-20 VIIRS Global Binned Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494501-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_KD_2022.0 NOAA-20 VIIRS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928913-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_KD_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928911-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_KD_NRT_R2022.0 NOAA-20 VIIRS Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494502-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_KD_R2022.0 NOAA-20 VIIRS Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494503-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_LAND_2022.0 NOAA-20 VIIRS Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928918-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_LAND_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928916-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_LAND_R2022.0 NOAA-20 VIIRS Global Binned Normalized Difference Vegetation Index Land Reflectance Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494504-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3b_NSST_2024.0 NOAA-20 VIIRS Level-3 Global Binned 11µm Nighttime Sea Surface Temperature (NSST) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805793-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3b_NSST_NRT_2024.0 NOAA-20 VIIRS Level-3 Global Binned 11µm Nighttime Sea Surface Temperature (NSST) - Near Real Time (NRT) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805781-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary +VIIRSJ1_L3b_PAR_2022.0 NOAA-20 VIIRS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928924-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_PAR_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Photosynthetically Available Radiation (PAR) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928922-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_PAR_NRT_R2022.0 NOAA-20 VIIRS Global Binned Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494505-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_PAR_R2022.0 NOAA-20 VIIRS Global Binned Photosynthetically Active Radiation (PAR) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494507-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_PIC_2022.0 NOAA-20 VIIRS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928926-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_PIC_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Particulate Inorganic Carbon (PIC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928925-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_PIC_NRT_R2022.0 NOAA-20 VIIRS Global Binned Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494508-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_PIC_R2022.0 NOAA-20 VIIRS Global Binned Particulate Inorganic Carbon (PIC) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494513-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_POC_2022.0 NOAA-20 VIIRS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928930-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_POC_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Particulate Organic Carbon (POC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928929-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_POC_NRT_R2022.0 NOAA-20 VIIRS Global Binned Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494526-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_POC_R2022.0 NOAA-20 VIIRS Global Binned Particulate Organic Carbon (POC) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494537-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_RRS_2022.0 NOAA-20 VIIRS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928932-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3b_RRS_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Binned Remote-Sensing Reflectance (RRS) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928931-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_RRS_NRT_R2022.0 NOAA-20 VIIRS Global Binned Remote-Sensing Reflectance (RRS) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494549-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_RRS_R2022.0 NOAA-20 VIIRS Global Binned Remote-Sensing Reflectance (RRS) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494560-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3b_SST3_2024.0 NOAA-20 VIIRS Level-3 Global Binned Triple-window Sea Surface Temperature (SST3) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805823-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3b_SST3_NRT_2024.0 NOAA-20 VIIRS Level-3 Global Binned Triple-window Sea Surface Temperature (SST3) - Near Real Time (NRT) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805813-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3b_SST_2024.0 NOAA-20 VIIRS Level-3 Global Binned 11µm Daytime Sea Surface Temperature (SST) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805844-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3b_SST_NRT_2024.0 NOAA-20 VIIRS Level-3 Global Binned 11µm Day/Night Sea Surface Temperature (SST) - Near Real Time (NRT) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805835-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary +VIIRSJ1_L3m_CHL_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928935-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_CHL_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Chlorophyll (CHL) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928934-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_CHL_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Chlorophyll (CHL) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494562-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_CHL_R2022.0 NOAA-20 VIIRS Global Mapped Chlorophyll (CHL) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494567-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_IOP_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928937-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_IOP_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928936-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_IOP_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494570-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_IOP_R2022.0 NOAA-20 VIIRS Global Mapped Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494573-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_KD_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928961-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_KD_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396928940-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_KD_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494576-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_KD_R2022.0 NOAA-20 VIIRS Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494577-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_LAND_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929044-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_LAND_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Normalized Difference Vegetation Index Land Reflectance - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929006-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_LAND_R2022.0 NOAA-20 VIIRS Global Mapped Normalized Difference Vegetation Index Land Reflectance Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494585-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3m_NSST_2024.0 NOAA-20 VIIRS Level-3 Global Mapped 11µm Nighttime Sea Surface Temperature (NSST) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805858-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3m_NSST_NRT_2024.0 NOAA-20 VIIRS Level-3 Global Mapped 11µm Nighttime Sea Surface Temperature (NSST) - Near Real Time (NRT) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805848-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary +VIIRSJ1_L3m_PAR_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929137-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_PAR_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Photosynthetically Available Radiation (PAR) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929083-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_PAR_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494598-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_PAR_R2022.0 NOAA-20 VIIRS Global Mapped Photosynthetically Active Radiation (PAR) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494601-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_PIC_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929194-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_PIC_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929168-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_PIC_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494610-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_PIC_R2022.0 NOAA-20 VIIRS Global Mapped Particulate Inorganic Carbon (PIC) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494620-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_POC_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929215-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_POC_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Particulate Organic Carbon (POC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929206-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_POC_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494623-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_POC_R2022.0 NOAA-20 VIIRS Global Mapped Particulate Organic Carbon (POC) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494632-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_RRS_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929228-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ1_L3m_RRS_NRT_2022.0 NOAA-20 VIIRS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3396929220-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_RRS_NRT_R2022.0 NOAA-20 VIIRS Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494639-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ1_L3m_RRS_R2022.0 NOAA-20 VIIRS Global Mapped Remote-Sensing Reflectance (RRS) Data, version R2022.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2340494643-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ1_L3m_SST3_2024.0 NOAA-20 VIIRS Level-3 Global Mapped Triple-window Sea Surface Temperature (SST3) Data, version 2024.0 OB_DAAC STAC Catalog 2017-11-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3166805864-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary @@ -16553,39 +16589,71 @@ VIIRSJ1_L3m_SST_NRT_2024.0 NOAA-20 VIIRS Level-3 Global Mapped 11µm Day/Night S VIIRSJ2_L1_1 NOAA-21 VIIRS Level-1 Data, version 1 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675296-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB). proprietary VIIRSJ2_L1_GEO_1 NOAA-21 VIIRS Geolocation Product Data, version 1 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675294-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) Geolocation (GEO) Products are data containing terrain corrected solar zenith and azimuth angles, satellite zenith and azimuth angles, as well as latitudes and longitudes for each VIIRS grid point for each of the three VIIRS resolutions. (375m, 750m, and DNB). proprietary VIIRSJ2_L1_OBC_1 NOAA-21 VIIRS On-Board Calibartion Product Data, version 1 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675298-OB_DAAC.umm_json The VIIRS Geolocation Onboard Calibrator (OBC)-IP file contains solar diffuser observations, the associated gain state and HAM side information, and all engineering and housekeeping data, including unscaled data from the Solar Diffuser Stability Monitor (SDSM)/VIIRS Earth View Radiometric Calibration Unit and the Solar Diffuser GEO angles. proprietary +VIIRSJ2_L2_IOP_2022.0 NOAA-21 VIIRS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023581-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L2_IOP_NRT_2022.0 NOAA-21 VIIRS Level-2 Regional Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023577-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L2_IOP_NRT_R2022.0 NOAA-21 VIIRS Regional Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675299-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L2_IOP_R2022.0 NOAA-21 VIIRS Regional Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675301-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ2_L2_LAND_R2022.0 NOAA-21 VIIRS Regional Normalized Difference Vegetation Index Land Reflectance Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675303-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L2_OC_2022.0 NOAA-21 VIIRS Level-2 Regional Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023590-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L2_OC_NRT_2022.0 NOAA-21 VIIRS Level-2 Regional Ocean Color (OC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023585-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L2_OC_NRT_R2022.0 NOAA-21 VIIRS Regional Ocean Color (OC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675305-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L2_OC_R2022.0 NOAA-21 VIIRS Regional Ocean Color (OC) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675307-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_CHL_2022.0 NOAA-21 VIIRS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023608-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_CHL_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Chlorophyll (CHL) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023598-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_CHL_NRT_R2022.0 NOAA-21 VIIRS Global Binned Chlorophyll (CHL) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675309-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_CHL_R2022.0 NOAA-21 VIIRS Global Binned Chlorophyll (CHL) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675310-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_IOP_2022.0 NOAA-21 VIIRS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023621-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_IOP_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023615-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_IOP_NRT_R2022.0 NOAA-21 VIIRS Global Binned Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675312-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_IOP_R2022.0 NOAA-21 VIIRS Global Binned Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675315-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_KD_2022.0 NOAA-21 VIIRS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023633-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_KD_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023627-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_KD_NRT_R2022.0 NOAA-21 VIIRS Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675317-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_KD_R2022.0 NOAA-21 VIIRS Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675318-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ2_L3b_LAND_R2022.0 NOAA-21 VIIRS Global Binned Normalized Difference Vegetation Index Land Reflectance Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675320-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_PAR_2022.0 NOAA-21 VIIRS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023638-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_PAR_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Photosynthetically Available Radiation (PAR) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023635-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_PAR_NRT_R2022.0 NOAA-21 VIIRS Global Binned Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675322-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_PAR_R2022.0 NOAA-21 VIIRS Global Binned Photosynthetically Active Radiation (PAR) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675324-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_PIC_2022.0 NOAA-21 VIIRS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023646-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_PIC_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Particulate Inorganic Carbon (PIC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023643-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_PIC_NRT_R2022.0 NOAA-21 VIIRS Global Binned Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675326-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_PIC_R2022.0 NOAA-21 VIIRS Global Binned Particulate Inorganic Carbon (PIC) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675328-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_POC_2022.0 NOAA-21 VIIRS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023654-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_POC_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Particulate Organic Carbon (POC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023649-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_POC_NRT_R2022.0 NOAA-21 VIIRS Global Binned Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675330-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_POC_R2022.0 NOAA-21 VIIRS Global Binned Particulate Organic Carbon (POC) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675332-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_RRS_2022.0 NOAA-21 VIIRS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023669-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3b_RRS_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Binned Remote-Sensing Reflectance (RRS) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023664-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_RRS_NRT_R2022.0 NOAA-21 VIIRS Global Binned Remote-Sensing Reflectance (RRS) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675333-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3b_RRS_R2022.0 NOAA-21 VIIRS Global Binned Remote-Sensing Reflectance (RRS) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675335-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_CHL_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023706-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_CHL_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Chlorophyll (CHL) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023675-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_CHL_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Chlorophyll (CHL) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675337-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_CHL_R2022.0 NOAA-21 VIIRS Global Mapped Chlorophyll (CHL) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675340-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_IOP_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023806-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_IOP_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023758-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_IOP_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675342-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_IOP_R2022.0 NOAA-21 VIIRS Global Mapped Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675346-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_KD_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023908-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_KD_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023859-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_KD_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675351-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_KD_R2022.0 NOAA-21 VIIRS Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675353-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSJ2_L3m_LAND_R2022.0 NOAA-21 VIIRS Global Mapped Normalized Difference Vegetation Index Land Reflectance Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675355-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_PAR_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023959-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_PAR_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Photosynthetically Available Radiation (PAR) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023949-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_PAR_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Photosynthetically Available Radiation (PAR) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675358-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_PAR_R2022.0 NOAA-21 VIIRS Global Mapped Photosynthetically Active Radiation (PAR) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675366-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_PIC_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023974-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_PIC_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023967-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_PIC_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Particulate Inorganic Carbon (PIC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675374-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_PIC_R2022.0 NOAA-21 VIIRS Global Mapped Particulate Inorganic Carbon (PIC) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675381-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_POC_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023999-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_POC_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Particulate Organic Carbon (POC) - Near Real-time (NRT) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397023985-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_POC_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Particulate Organic Carbon (POC) - Near Real Time (NRT) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675386-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_POC_R2022.0 NOAA-21 VIIRS Global Mapped Particulate Organic Carbon (POC) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675396-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_RRS_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397024028-OB_CLOUD.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary +VIIRSJ2_L3m_RRS_NRT_2022.0 NOAA-21 VIIRS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version 2022.0 OB_CLOUD STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3397024011-OB_CLOUD.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_RRS_NRT_R2022.0 NOAA-21 VIIRS Global Mapped Remote-Sensing Reflectance (RRS) - NRT Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675403-OB_DAAC.umm_json The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit. proprietary VIIRSJ2_L3m_RRS_R2022.0 NOAA-21 VIIRS Global Mapped Remote-Sensing Reflectance (RRS) Data, version R2022.0 OB_DAAC STAC Catalog 2022-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2652675415-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary VIIRSN_L1_2 Suomi-NPP VIIRS Level-1 Data, version 2 OB_DAAC STAC Catalog 2012-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1570118532-OB_DAAC.umm_json The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB). proprietary @@ -17095,11 +17163,11 @@ WENTZ_NIMBUS-7_SMMR_L2_1 NIMBUS-7 SMMR GLOBAL AIR-SEA PARAMETERS IN SWATH (Wentz WENTZ_SASS_SIGMA0_L2_1 SEASAT SCATTEROMETER BINNED 50KM SIGMA-0 DATA (Wentz) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197621-POCLOUD.umm_json Contains Seasat-A Scatterometer (SASS) Sigma-0 measurements for the entire Seasat mission, from July 1978 until October 1978, produced by Frank Wentz at Remote Sensing Systems. The data are presented chronologically by swath and consist of the forward and aft values, binned in 50 km cells. For each cell there are 17 parameters including time, location, incidence angle, sigma-0, instrument corrections, and data quality. proprietary WHITECAPS_0 Influence of Whitecaps on Aerosol and Ocean-Color Remote Sensing OB_DAAC STAC Catalog 2011-02-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360700-OB_DAAC.umm_json The influence of whitecaps on ocean color and aerosol remote sensing from space were invistigated onboard the R/V Melville (MV1102) from Cape Town, South Africa to Valparaiso, Chile from February 2, 2011 to March 14, 2011. Satellite imagery has revealed relatively large amounts of aerosols and particulate organic and inorganic carbon in the Southern oceans, but it is not clear whether this is real or the result of not taking into account properly whitecap effects in the retrieval algorithms. By measuring whitecap optical properties and profiles of marine reflectance and backscattering and absorption coefficients, a bulk whitecap reflectance model will be developed. The measurements will allow comparisons of the aerosol optical thickness and marine reflectance one should retrieve (i.e., in the absence of whitecaps and bubbles) with the satellite-derived estimates. The parameters/variables that will be measured include whitecap coverage, surface reflectance, aerosol optical thickness, in situ profiles of marine reflectance, backscattering and attenuation coefficients, and particle size distribution, and absorption and backscattering coefficients and HPLC pigments from water samples. The backscattering and absorption measurements from water samples will characterize conditions without whitecaps. Cruise information can be found in the R2R repository: https://www.rvdata.us/search/cruise/MV1102. proprietary WILKS_2018_Chatham_sedimenttraps_specieslist_3 Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific AU_AADC STAC Catalog 1996-06-17 1997-05-07 174.90234, -45.39845, 179.73633, -40.71396 https://cmr.earthdata.nasa.gov/search/concepts/C1459701888-AU_AADC.umm_json "This spreadsheet contains species lists and counts from four sediment trap records. The sediment traps were deployed for ~1 year north and south of the Chatham Rise, New Zealand, between 1996 and 1997. Sheets 1a and 1b refer to North Chatham Rise (NCR). 1a = the 300m trap. 1b = the 1000m trap. Sheets 2a and 2b are for the South Chatham Rise traps (SCR). 2a= 300m, 2b= 1000m. Counting was undertaken on 1/16th splits. Material was cleaned of organics before diatom counting under light microscopy. Coccolith counting on uncleaned material was only undertaken at the 300m traps. Radiolarians and silicoflagellates were counted but not identified. Diatoms and coccoliths were counted along non-overlapping transects until 300 specimens had been counted per sample, or until 10 transects had been made. This dataset includes counts of diatom, coccolithophores, radiolarians and silicoflagellates for four sediment trap records- North Chatham Rise (NCR) and South Chatham Rise (SCR) at two trap depths each (300 m and 1000 m). It is intended as supplementary material to Wilks et al. 2018 (submitted) ""Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific."" Numbers are raw count per sample cup. Authorities are given. Coordinates of traps given in degrees, minutes and seconds." proprietary -WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND ALL STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND SCIOPS STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary +WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND ALL STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary WIR_98_4105 Major-Ion, Nutrient, and Trace-Element Concentrations in the Steamboat Creek Basin CEOS_EXTRA STAC Catalog 1996-09-09 1996-09-13 -122.7, 42.3, -122.5, 43.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554333-CEOS_EXTRA.umm_json In September 1996, a water-quality study was done by the U.S. Geological Survey, in coordination with the U.S. Forest Service, in headwater streams of Steamboat Creek, a tributary to the North Umpqua River Basin in southwestern Oregon. Field measurements were made in and surface-water and bottom-sediment samples were collected from three tributaries of Steamboat Creek-Singe Creek, City Creek, and Horse Heaven Creek-and at one site in Steamboat Creek upstream from where the three tributaries flow into Steamboat Creek. Water samples collected in Singe Creek had larger concentrations of most major-ion constituents and smaller concentrations of most nutrient constitu ents than was observed in the other three creeks. City Creek, Horse Heaven Creek, and Steamboat Creek had primarily calcium bicarbonate water, whereas Singe Creek had primarily a calcium sulfate water; the calcium sulfate water detected in Singe Creek, along with the smallest observed alkalinity and pH values, suggests that Singe Creek may be receiving naturally occurring acidic water. Of the 18 trace elements analyzed in filtered water samples, only 6 were detected-aluminum, barium, cobalt, iron, manganese, and zinc. All six of the trace elements were detected in Singe Creek, at concentrations generally larger than those observed in the other three creeks. Of the detected trace elements, only iron and zinc have chronic toxicity criteria established by the U.S. Environmental Protection Agency (USEPA) for the protection of aquatic life; none exceeded the USEPA criterion. Bottom-sediment concentrations of antimony, arsenic, cadmium, copper, lead, mercury, zinc, and organic carbon were largest in City Creek. In City Creek and Horse Heaven Creek, concentrations for 11 constituents--antimony, arsenic, cadmium, copper, lead, manganese (Horse Heaven Creek only), mercury, selenium, silver, zinc, and organic carbon (City Creek only)--exceeded concentrations considered to be enriched in streams of the nearby Willamette River Basin, whereas in Steamboat Creek only two trace elements--antimony and nickel--exceeded Willamette River enriched concentrations. Bottom-sediment concentrations for six of these constituents in City Creek and Horse Heaven Creek--arsenic, cadmium, copper, lead, mercury, and zinc--also exceeded interim Canadian threshold effect level (TEL) concentrations established for the protection of aquatic life, whereas only four constituents between Singe Creek and Steamboat Creek--arsenic, chromium, copper (Singe Creek only), and nickel--exceeded the TEL concentrations. The data set checked for the concentrations of major ions, nutrients, and trace elements in water and bottom sediments collected in the four tributaries during the low-flow conditions of September 9-13, 1996. Stream-water chemistry results were contrasted, and trace-element concentrations were compared with U.S. Environmental Protection Agency chronic aquatic life toxicity criteria. Bottom-sediment trace-element results were also contrasted and compared with concentrations considered to be enriched in streams of the nearby Willamette River Basin and to interim Canadian threshold level (TEL) concentrations established for the protection of aquatic life. The area of study was Headwater streams of Steamboat Creek, a tributary to the north Umpqua River Basin in southwestern Oregon Field measurements and surface-water and bottom-sediment samples at each of the four sites included streamflow, stream temperature, specific conductance, dissolved oxygen, pH, alkalinity, major ions in filtered water (8 constituents), low-level concentrations of trace elements in filtered water (18 elements), and trace elements and carbon in bottom sediment (47 elements). Stream temperature, specific conductance, dissolved oxygen, and pH were measured using a calibrated Hydrolab multiparameter unit. Because stream widths were less than 8 feet, field measurements were made only near the center of flow at 1 foot or less below water surface. The Hydrolab unit was calibrated at each site before and after sampling. Stream temperatures were recorded to the nearest 0.1 degree Centigrade; specific conductance to the nearest 1 microsiemen per centimeter at 25 degrees Centigrade ; dissolved oxygen to the nearest 0.1 milligrams per liter; and pH to the nearest 0.1 pH units. Measurements of streamflow were made in accordance with standard USGS procedures (Rantz and others, 1982). Alkalinity measurements were made on filtered water samples using an incremental titration method (Shelton, 1994), and results were reported to the nearest 1 milligram per liter as calcium carbonate (CaCO3). Water samples were collected using 1-liter narrow-mouth acid-rinsed polyethylene bottles from a minimum of eight verticals in the cross section, suing an equal-width-increment method described by Edwards and Glysson (1988), and composited into a 8-liter polyethylene acid-rinsed churn splitter. Sample and compositing containers were prerinsed with native water prior to sample collection. Water samples were collected using clean procedures as outlined by Horowitz and others (1994). Samples were chilled on ice from time of sample collection until analysis, except when samples were processed. Processing of the field samples was accomplished either in the mobile field laboratory or in an area suitably clean for carrying out the filtering and preservation procedures. Samples for major ions, nutrients, and trace elements in filtered water (operationally defined as dissolved) were passed through 0.45 micrometer pore-size capsule filters into polyethylene bottles using procedures outlined by Horowitz and others (1994). Filtered-water trace-element samples were preserved with 0.5 milliliter of ultra-pure nitric acid per 250 mL of sample; nutrient samples were placed in dark brown polyethylene bottles and were chilled for preservation. All chemical samples were shipped to the USGS National Water Quality Laboratory (NWQL) in Arvada, Colorado, for analysis according to methods outlined by Fishman (1993). The information for this metadata was taken from the Online Publications of the Oregon District at http://oregon.usgs.gov/pubs_dir/online_list.html . proprietary -WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season ALL STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary +WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary WLDAS_NOAHMP001_DA1_D1.0 WLDAS Noah-MP 3.6 Land Surface Model L4 Daily 0.01 degree x 0.01 degree Version D1.0 (WLDAS_NOAHMP001_DA1) at GES DISC GES_DISC STAC Catalog 1979-01-02 -124.925, 25.065, -89.025, 52.925 https://cmr.earthdata.nasa.gov/search/concepts/C2789781977-GES_DISC.umm_json The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region. WLDAS uses NASA’s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables. proprietary WOCE91_Chlorophyll_1 Chlorophyll a data collected on the 1991 WOCE voyage of the Aurora Australis AU_AADC STAC Catalog 1991-10-08 1991-10-26 136.393, -62.294, 154.937, -45.183 https://cmr.earthdata.nasa.gov/search/concepts/C1214314037-AU_AADC.umm_json Chloropyll a data were collected along the WOCE transect on voyage 1 of the Aurora Australis, during October of 1991. These data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms). proprietary WOES_Chlorophyll_1 Aurora Australis Voyage 9 (WOES) 1992-93 Chlorophyll a Data AU_AADC STAC Catalog 1993-03-12 1993-05-03 139.71167, -65.888, 155.11171, -43.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214314038-AU_AADC.umm_json This dataset contains chlorophyll a data collected by the Aurora Australis on Voyage 7, 1992-1993 - the WOES (Wildlife Oceanography Ecosystem Survey) cruise. Samples were collected from March-May of 1993. These data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms). proprietary @@ -17121,10 +17189,10 @@ WV03_SWIR_L1B_1 WorldView-3 Level 1B Shortwave Infrared 8-Band Satellite Imagery WV04_MSI_L1B_1 WorldView-4 Level 1B Multispectral 4-Band Satellite Imagery CSDA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497446902-CSDA.umm_json The WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The multispectral imagery has a spatial resolution of 1.24m at nadir and has a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program. proprietary WV04_Pan_L1B_1 WorldView-4 Level 1B Panchromatic Satellite Imagery CSDA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497439327-CSDA.umm_json The WorldView-4 Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the WorldView-110 camera across the global land surface from December 2016 to January 2019. This data product includes panchromatic imagery with a spatial resolution of 0.31m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program. proprietary WV_LCC_SC_FSCA_1 Land Cover Classification, Snow Cover, and Fractional Snow-Covered Area Maps from Maxar WorldView Satellite Images V001 NSIDC_ECS STAC Catalog 2015-05-20 2019-05-05 -121.203708, 38.867847, -108.032283, 48.672717 https://cmr.earthdata.nasa.gov/search/concepts/C2695676729-NSIDC_ECS.umm_json This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license. proprietary -WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources SCIOPS STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources ALL STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary -WYGISC_LANDUSE Agricultural Land Use of Wyoming SCIOPS STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary +WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources SCIOPS STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary WYGISC_LANDUSE Agricultural Land Use of Wyoming ALL STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary +WYGISC_LANDUSE Agricultural Land Use of Wyoming SCIOPS STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary WaterBalance_Daily_Historical_GRIDMET_1.5 Daily Historical Water Balance Products for the CONUS LPCLOUD STAC Catalog 1980-01-01 2023-12-31 -131.70607, 21.115301, -60.530453, 55.457306 https://cmr.earthdata.nasa.gov/search/concepts/C2674694066-LPCLOUD.umm_json This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model. Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format. proprietary WaterBalance_Monthly_Historical_GRIDMET_1.5 Monthly Historical Water Balance Products for the CONUS LPCLOUD STAC Catalog 1980-01-01 2023-12-31 -131.70607, 21.115301, -60.530453, 55.457306 https://cmr.earthdata.nasa.gov/search/concepts/C2674700048-LPCLOUD.umm_json This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model. Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format. proprietary WebbRosenzweig_548_1 Global Soil Texture and Derived Water-Holding Capacities (Webb et al.) ORNL_CLOUD STAC Catalog 1950-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216863033-ORNL_CLOUD.umm_json A standardized global data set of soil horizon thicknesses and textures (particle size distributions). proprietary @@ -17139,19 +17207,19 @@ Wetland_VegClassification_PAD_2069_1 ABoVE: Wetland Vegetation Classification fo WhitePhenoregions_799_1 Phenoregions For Monitoring Vegetation Responses to Climate Change ORNL_CLOUD STAC Catalog 1982-01-01 1999-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784383305-ORNL_CLOUD.umm_json The overall purpose in this research was to identify the regions of the world best suited for long-term monitoring of biospheric responses to climate change, i.e., monitoring land surface phenology. The user is referred to White et al. [2005] for further details. Using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity.This dataset contains material providing users with direct access to data used to construct the figures in White et al. [2005]. Users are referred to this reference for additional information. Data files include ASCII and binary versions of the image files for the 500 elemental phenoregions and the 136 final monitoring phenoregions (shown in figure below) and a corresponding .jpg map. Also included are the classification data in tabular ACSII format for each of the 500 elemental phenoregions.Selected monitoring phenoregions. Phenoregions with fewer than 100 pixels or dominated by crop, urban or barren landcover removed. The 136 remaining phenoregions are those passing the screening factors in Table 1 and are shown with normalized rankings by landcover. (From White et al., 2005) proprietary WhiteSpruce_Leaf_Traits_Alaska_2124_1 ABoVE: White Spruce Photosynthetic and Leaf Traits, Alaska and New York, 2017 ORNL_CLOUD STAC Catalog 2017-06-19 2017-07-20 -149.75, 41.4, -74.02, 67.99 https://cmr.earthdata.nasa.gov/search/concepts/C2636355463-ORNL_CLOUD.umm_json This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format. proprietary WhiteSpruce_Leaf_Traits_Alaska_2124_1 ABoVE: White Spruce Photosynthetic and Leaf Traits, Alaska and New York, 2017 ALL STAC Catalog 2017-06-19 2017-07-20 -149.75, 41.4, -74.02, 67.99 https://cmr.earthdata.nasa.gov/search/concepts/C2636355463-ORNL_CLOUD.umm_json This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format. proprietary -Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ALL STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ORNL_CLOUD STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary +Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ALL STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary Wildfire_Impacts_Boreal_Ecosys_2359_1 Impacts of Wildfires on Boreal Forest Ecosystem Carbon Dynamics ORNL_CLOUD STAC Catalog 1986-01-01 2020-12-31 -166, 43.5, -53, 70 https://cmr.earthdata.nasa.gov/search/concepts/C3234724704-ORNL_CLOUD.umm_json This dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data. The delta Normalized Burn Ratio (dNBR), which can be used to represent burn severity for a fire, was calculated for each individual fire over the time period. The interactions between canopy, fire and soil thermal dynamics were modelled using a soil surface energy balance model incorporated into a previous Terrestrial Ecosystem Model (TEM). Using the revised TEM, two regional simulations were conducted with and without fire disturbance. Fire polygons were dissected into each unit with unique fire history and then intersected with each grid cell to measure fire impacts. The output values for each grid cell are the area-weighted mean of each fire polygon and unburned area within the cell. Two extra simulations without a canopy energy balance scheme were also conducted to quantify the impact of the canopy. Soil temperature was simulated with and without the canopy energy balance scheme in the model in addition to considering fire impacts. proprietary -Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ORNL_CLOUD STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ALL STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary -Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary +Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ORNL_CLOUD STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ALL STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary +Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ORNL_CLOUD STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ALL STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary -Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ALL STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ORNL_CLOUD STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary -Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ALL STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary +Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ALL STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ORNL_CLOUD STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary +Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ALL STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary Willow_Veg_Plots_1368_1 Arctic Vegetation Plots in Willow Communities, North Slope, Alaska, 1997 ORNL_CLOUD STAC Catalog 1997-07-09 1997-08-17 -149.85, 68.03, -148.08, 70.19 https://cmr.earthdata.nasa.gov/search/concepts/C2170969823-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected in July and August 1997 from 85 study plots in willow shrub communities located along a north-south transect from the Brooks Range to Prudhoe Bay on the North Slope of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in three broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the region and across Alaska. proprietary WindSat-REMSS-L3U-v7.0.1a_7.0.1a GHRSST Level 3U Global Subskin Sea Surface Temperature version7.0.1a from the WindSat Polarimetric Radiometer on the Coriolis satellite POCLOUD STAC Catalog 2002-06-01 2020-10-19 -179.99, -39.06, 180, 39.01 https://cmr.earthdata.nasa.gov/search/concepts/C2036878925-POCLOUD.umm_json "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions. By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by ""rt"" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. ""Final"" data (currently identified by ""v7.0.1a"" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final ""v7.0.1a"" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter ""a"" refers to the file incompliance with GHRSST format." proprietary Wolves_Denning_Pups_Climate_1846_1 ABoVE: Wolf Denning Phenology and Reproductive Success, Alaska and Canada, 2000-2017 ALL STAC Catalog 2000-03-29 2017-08-31 -154.58, 52.97, -112.97, 67.84 https://cmr.earthdata.nasa.gov/search/concepts/C2143401778-ORNL_CLOUD.umm_json This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range. proprietary @@ -17162,14 +17230,14 @@ WorldView-2.full.archive.and.tasking_8.0 WorldView-2 full archive and tasking ES WorldView-3.full.archive.and.tasking_8.0 WorldView-3 full archive and tasking ESA STAC Catalog 2014-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336965-ESA.umm_json "WorldView-3 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-3 offers archive and tasking panchromatic products up to 0.31m GSD resolution, 4-Bands/8-Bands products up to 1.24 m GSD resolution, and SWIR products up to 3.70 m GSD resolution. Band Combination Data Processing Level Resolution High Res Optical: Panchromatic and 4-bands Standard(2A)/View Ready Standard (OR2A) 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map Ready (Ortho) 1:12.000 Orthorectified 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm High Res Optical: 8-bands Standard(2A)/View Ready Standard (OR2A) 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map Ready (Ortho) 1:12.000 Orthorectified 30 cm, 40 cm, 50/60 cm High Res Optical: SWIR Standard(2A)/View Ready Standard (OR2A) 3.7 m or 7.5 m (depending on the collection date) Map Ready (Ortho) 1:12.000 Orthorectified 4-Bands being an optional from: 4-Band Multispectral (BLUE, GREEN, RED, NIR1) 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) 8-Bands being an optional from: 8-Band Multispectral (COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) 8-Band Bundle (PAN, COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) Native 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary WorldView-4.full.archive_7.0 WorldView-4 full archive ESA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547572305-ESA.umm_json WorldView-4 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-4 offers archive panchromatic products up to 0.31m GSD resolution, and 4-Bands Multispectral products up to 1.24m GSD resolution Band Combination: Panchromatic and 4-bands Data Processing Level: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m The options for 4-Bands are the following: • 4-Band Multispectral (BLUE, GREEN, RED, NIR1) • 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) • 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) • 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) • 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) The list of available archived data can be retrieved using the Image Library (https://www.euspaceimaging.com/image-library/) catalogue. proprietary WorldView.ESA.archive_9.0 WorldView ESA archive ESA STAC Catalog 2009-02-07 2020-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689694-ESA.umm_json "The WorldView ESA archive is composed of products acquired by WorldView-1, -2, -3 and -4 satellites and requested by ESA supported projects over their areas of interest around the world Panchromatic, 4-Bands, 8-Bands and SWIR products are part of the offer, with the resolution at Nadir depicted in the table. Band Combination Mission GSD Resolution at Nadir GSD Resolution (20° off nadir) Panchromatic WV-1 50 cm 55 cm WV-2 46 cm 52 cm WV-3 31 cm 34 cm WV-4 31 cm 34 cm 4-Bands WV-2 1.84 m 2.4 m WV-3 1.24 m 1.38 m WV-4 1.24 m 1.38 m 8-Bands WV-2 1.84 m 2.4 m WV-3 1.24 m 1.38 m SWIR WV-3 3.70 m 4.10 m The 4-Bands includes various options such as Multispectral (separate channel for Blue, Green, Red, NIR1), Pan-sharpened (Blue, Green, Red, NIR1), Bundle (separate bands for PAN, Blue, Green, Red, NIR1), Natural Colour (pan-sharpened Blue, Green, Red), Coloured Infrared (pan-sharpened Green, Red, NIR). The 8-Bands being an option from Multispectral (COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2) and Bundle (PAN, COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2). The processing levels are: Standard (2A): normalised for topographic relief View Ready Standard: ready for orthorectification (RPB files embedded) View Ready Stereo: collected in-track for stereo viewing and manipulation (not available for SWIR) Map Scale (Ortho) 1:12,000 Orthorectified: additional processing unnecessary Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/WorldView/ available on the Third Party Missions Dissemination Service. The following table summarises the offered product types EO-SIP Product Type Band Combination Processing Level Missions WV6_PAN_2A Panchromatic (PAN) Standard/View Ready Standard WorldView-1 and 4 WV6_PAN_OR Panchromatic (PAN) View Ready Stereo WorldView-1 and 4 WV6_PAN_MP Panchromatic (PAN) Map Scale Ortho WorldView-1 and 4 WV1_PAN__2A Panchromatic (PAN) Standard/View Ready Standard WorldView-2 and 3 WV1_PAN__OR Panchromatic (PAN) View Ready Stereo WorldView-2 and 3 WV1_PAN__MP Panchromatic (PAN) Map Scale Ortho WorldView-2 and 3 WV1_4B__2A 4-Band (4B) Standard/View Ready Standard WorldView-2, 3 and 4 WV1_4B__OR 4-Band (4B) View Ready Stereo WorldView-2, 3 and 4 WV1_4B__MP 4-Band (4B) Map Scale Ortho WorldView-2, 3 and 4 WV1_8B_2A 8-Band (8B) Standard/View Ready Standard WorldView-2 and 3 WV1_8B_OR 8-Band (8B) View Ready Stereo WorldView-2 and 3 WV1_8B_MP 8-Band (8B) Map Scale Ortho WorldView-2 and 3 WV1_S8B__2A SWIR Standard/View Ready Standard WorldView-3 WV1_S8B__MP SWIR Map Scale Ortho WorldView-3 As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary -XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary -XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary +XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary +XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary -XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary +XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_MODIS_Aqua_1 MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km LAADS STAC Catalog 2019-01-01 2023-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859238768-LAADS.umm_json The MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Aqua is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Aqua/MODIS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_MODIS_Aqua product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_MODIS_Aqua product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Aqua Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_MODIS_Terra_1 MODIS/Terra Dark Target Aerosol 5-Min L2 Swath 10 km LAADS STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859248304-LAADS.umm_json The MODIS/Terra Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Terra is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Terra/MODIS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_MODIS_Terra product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_MODIS_Terra product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Terra Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_VIIRS_NOAA20_1 VIIRS/NOAA20 Dark Target Aerosol L2 6-Min Swath 6 km LAADS STAC Catalog 2019-01-01 2023-05-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859228520-LAADS.umm_json The VIIRS/NOAA20 L2 Dark Target Aerosol 6-Min L2 Swath 6 km product, short-name XAERDT_L2_VIIRS_NOAA20 is provided at 6-km spatial resolution (at-nadir) and a 6-minute cadence that typically yields about 130 granules over the daylit hours of a 24-hour period. The NOAA20/VIIRS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_VIIRS_NOAA20 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_VIIRS_NOAA20 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_VIIRS_NOAA20 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary @@ -17178,19 +17246,19 @@ Xingu_Albedo_Radiation_1622_1 Net Radiation and Albedo from MODIS for Xingu Rive YKDelta_EnvChange_InfoExchange_1894_1 Alaska's Changing YK Delta: Knowledge Exchange between Elders and Geoscientists, 2018 ORNL_CLOUD STAC Catalog 2018-11-14 2018-11-16 -166.55, 59.58, -159.48, 63.43 https://cmr.earthdata.nasa.gov/search/concepts/C2170972782-ORNL_CLOUD.umm_json This dataset provides a booklet documenting the discussions and outcomes from a knowledge-exchange meeting with Yup'ik elders from the Yukon-Kuskokwim Delta (YKD), western Alaska, community members, and natural scientist to discuss landscape and weather changes that have been observed in their homelands. The meeting was held during November 14-16, 2018. Yup'ik participants represented several YKD villages that occupy very different biophysical environments, and they have lifelong perspectives of environmental conditions and change that predate the era of Earth-observing satellites by many decades. Nearly 16 hours of discussion and testimonials from YKD elders were recorded during the meeting. The booklet is structured according to the environmental change processes that were discussed (e.g., coastal flooding, permafrost thaw, shrub expansion, climate change) and includes narrative summaries, quotations from participants, graphical illustrations, and examples of the field- and remote-sensing-based scientific findings, and map products developed as part of the larger ABoVE project. proprietary YKDelta_EnvChange_InfoExchange_1894_1 Alaska's Changing YK Delta: Knowledge Exchange between Elders and Geoscientists, 2018 ALL STAC Catalog 2018-11-14 2018-11-16 -166.55, 59.58, -159.48, 63.43 https://cmr.earthdata.nasa.gov/search/concepts/C2170972782-ORNL_CLOUD.umm_json This dataset provides a booklet documenting the discussions and outcomes from a knowledge-exchange meeting with Yup'ik elders from the Yukon-Kuskokwim Delta (YKD), western Alaska, community members, and natural scientist to discuss landscape and weather changes that have been observed in their homelands. The meeting was held during November 14-16, 2018. Yup'ik participants represented several YKD villages that occupy very different biophysical environments, and they have lifelong perspectives of environmental conditions and change that predate the era of Earth-observing satellites by many decades. Nearly 16 hours of discussion and testimonials from YKD elders were recorded during the meeting. The booklet is structured according to the environmental change processes that were discussed (e.g., coastal flooding, permafrost thaw, shrub expansion, climate change) and includes narrative summaries, quotations from participants, graphical illustrations, and examples of the field- and remote-sensing-based scientific findings, and map products developed as part of the larger ABoVE project. proprietary Young_Russian_Forest_Map_1330_1 Distribution of Young Forests and Estimated Stand Age across Russia, 2012 ORNL_CLOUD STAC Catalog 2012-01-01 2012-12-31 -180, 32.86, 180, 87.24 https://cmr.earthdata.nasa.gov/search/concepts/C2773252554-ORNL_CLOUD.umm_json This data set provides the distribution of young forests (forests less than 27 years of age) and their estimated stand ages across the full extent of Russia at 500-m resolution for the year 2012. The distribution of young forests was modeled with MODIS 500-m records for 12- to 27-year-old forests and augmented with the 0- to 11-year-old forest distribution as aggregated from 30 m resolution contemporary Landsat imagery. proprietary -ZZZ302 Alabama Remote Sensing Archive Multispectral Imagery of Alabama from Landsat and Skylab ALL STAC Catalog 1972-01-01 1984-01-01 -92, 24, -80, 35 https://cmr.earthdata.nasa.gov/search/concepts/C1214584460-SCIOPS.umm_json Multispectral imagery of the state of Alabama is available from the Geological Survey of Alabama for the time period of 1972-1984. Imagery from the Landsat multispectral scanner (MSS) is available as prints or transparencies for all bands (with selected color composites avaliable) at an approximate scale of 1:1,000,000. MSS data is collected in four spectral bands ranging from 0.5 to 1.1 micrometer with a ground resolution of about 80m. Images available from Skylab 3 and 4 include 9 x 9 prints and transparencies at 1:750,000 (skylab 3) and 1:500,000 (skylab 4). These images were taken in 1973 and are along three tracks; northeast from New Orleans, LA to South Carolina, northeast from Pensacola, FL to Columbus, GA, and from Pearl River, Jackson MI to Pensacola, FL. The multispectral photographic facility onboard Skylab provided imagery in several wavelength bands ranging from 0.5 to 0.9 Micrometers. This camera system provided ground resolution of approximately 40 m in visible wavelengths to 75 m in the infrared. A variety of high and low altitude aircraft imagery of Alabama is also available from the Geological Survey of Alabama. Microfiche images of MSS/TM imagery of North America since 1986 (landsat browse imagery) are also available. Similar imagery for other locations and time periods is available from the Eros Data Center. proprietary ZZZ302 Alabama Remote Sensing Archive Multispectral Imagery of Alabama from Landsat and Skylab SCIOPS STAC Catalog 1972-01-01 1984-01-01 -92, 24, -80, 35 https://cmr.earthdata.nasa.gov/search/concepts/C1214584460-SCIOPS.umm_json Multispectral imagery of the state of Alabama is available from the Geological Survey of Alabama for the time period of 1972-1984. Imagery from the Landsat multispectral scanner (MSS) is available as prints or transparencies for all bands (with selected color composites avaliable) at an approximate scale of 1:1,000,000. MSS data is collected in four spectral bands ranging from 0.5 to 1.1 micrometer with a ground resolution of about 80m. Images available from Skylab 3 and 4 include 9 x 9 prints and transparencies at 1:750,000 (skylab 3) and 1:500,000 (skylab 4). These images were taken in 1973 and are along three tracks; northeast from New Orleans, LA to South Carolina, northeast from Pensacola, FL to Columbus, GA, and from Pearl River, Jackson MI to Pensacola, FL. The multispectral photographic facility onboard Skylab provided imagery in several wavelength bands ranging from 0.5 to 0.9 Micrometers. This camera system provided ground resolution of approximately 40 m in visible wavelengths to 75 m in the infrared. A variety of high and low altitude aircraft imagery of Alabama is also available from the Geological Survey of Alabama. Microfiche images of MSS/TM imagery of North America since 1986 (landsat browse imagery) are also available. Similar imagery for other locations and time periods is available from the Eros Data Center. proprietary +ZZZ302 Alabama Remote Sensing Archive Multispectral Imagery of Alabama from Landsat and Skylab ALL STAC Catalog 1972-01-01 1984-01-01 -92, 24, -80, 35 https://cmr.earthdata.nasa.gov/search/concepts/C1214584460-SCIOPS.umm_json Multispectral imagery of the state of Alabama is available from the Geological Survey of Alabama for the time period of 1972-1984. Imagery from the Landsat multispectral scanner (MSS) is available as prints or transparencies for all bands (with selected color composites avaliable) at an approximate scale of 1:1,000,000. MSS data is collected in four spectral bands ranging from 0.5 to 1.1 micrometer with a ground resolution of about 80m. Images available from Skylab 3 and 4 include 9 x 9 prints and transparencies at 1:750,000 (skylab 3) and 1:500,000 (skylab 4). These images were taken in 1973 and are along three tracks; northeast from New Orleans, LA to South Carolina, northeast from Pensacola, FL to Columbus, GA, and from Pearl River, Jackson MI to Pensacola, FL. The multispectral photographic facility onboard Skylab provided imagery in several wavelength bands ranging from 0.5 to 0.9 Micrometers. This camera system provided ground resolution of approximately 40 m in visible wavelengths to 75 m in the infrared. A variety of high and low altitude aircraft imagery of Alabama is also available from the Geological Survey of Alabama. Microfiche images of MSS/TM imagery of North America since 1986 (landsat browse imagery) are also available. Similar imagery for other locations and time periods is available from the Eros Data Center. proprietary ZinkeSoil_221_1 Global Organic Soil Carbon and Nitrogen (Zinke et al.) ORNL_CLOUD STAC Catalog 1940-01-01 1986-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216862657-ORNL_CLOUD.umm_json A compilation of worldwide soil carbon and nitrogen data for more than 3500 soil profiles. proprietary Zinke_soil_683_1 LBA Regional Organic Soil Carbon and Nitrogen Data (Zinke et al.) ORNL_CLOUD STAC Catalog 1940-01-01 1984-12-31 -85, -25, -30, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2777326924-ORNL_CLOUD.umm_json The data set contains a subset of a global organic soil carbon and nitrogen data set (Zinke et al. 1986). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The point data are available in three formats: a comma-delimited ASCII file (*.csv), an ESRI shapefile, and an ESRI export file (*.e00).The data for the global data set (Zinke et al. 1986) were obtained from soil surveys conducted by Zinke in 1965-1984 and from soil survey literature. The main samples for laboratory analyses were collected at uniform soil increments and included bulk density determinations. Many samples reported in the literature did not have uniform soil increments or bulk density determinations. Only soil profiles that had been sampled either to a meter in depth or to actual depth were included in this database from soil survey literature. When carbon content was known but bulk densities were absent from soil samples reported in the literature, densities were estimated by regression analysis on the basis of the relationship between organic carbon content and measured bulk density in 1800 soil profiles for which bulk densities were known.Further information can be found at ftp://daac.ornl.gov/data/lba/carbon_dynamics/Zinke_soil/comp/zinke_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html. proprietary ZoblerSoilDerived_540_1 Global Soil Types, 0.5-Degree Grid (Modified Zobler) ORNL_CLOUD STAC Catalog 1974-01-01 1982-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216862776-ORNL_CLOUD.umm_json A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler's (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. proprietary ZoblerSoil_418_1 Global Soil Types, 1-Degree Grid (Zobler) ORNL_CLOUD STAC Catalog 1972-01-01 1982-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216862716-ORNL_CLOUD.umm_json A global digital data base of soil properties is available at 1 degree longitude resolution. For each land cell, the data base includes major and associated soil units, surface texture, and slope; phase and miscellaneous land units are included where available. The data base was compiled as part of an effort to improve modeling of the hydrologic cycle in the GISS Genreal Circulation Model. proprietary Zobler_Soil_649_1 SAFARI 2000 Soil Types, 0.5-Deg Grid (Modified Zobler) ORNL_CLOUD STAC Catalog 1974-01-01 1981-12-31 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788353687-ORNL_CLOUD.umm_json A SAFARI 2000 data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler's (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. proprietary ZonalFlux_0 Measurements from the western equatorial Pacific Ocean OB_DAAC STAC Catalog 1996-04-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360710-OB_DAAC.umm_json Measurements taken in the western equatorial Pacific Ocean in 1996. proprietary -a-dataset-of-40000-trees-with-section-wise-measured-stem-diameter-and-length-and_1.0 A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland ALL STAC Catalog 2024-01-01 2024-01-01 7.56, 47.23, 7.56, 47.23 https://cmr.earthdata.nasa.gov/search/concepts/C3383774336-ENVIDAT.umm_json The data presented here were prepared for publication in form of a data paper. The dataset presents an update of the information in Didion M, Herold A, Vulovic Z, Nitzsche J, Stillhard J (2022) Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory. EnviDat. doi:https://www.doi.org/10.16904/envidat.358. The new dataset is based on an alternative, more accurate and comprehensive dataset, that was identified as the source of the dataset in Didion et al. 2022. It includes additional data, including newly digitized information. The dataset includes section-wise stem measurements on 40’349 felled individual trees and empirically derived coarse (diameter >= 7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range. The dataset has undergone quality controls and due to its origin from 768 plots of the Experimental Forest Management project long-term consistency is assured. proprietary a-dataset-of-40000-trees-with-section-wise-measured-stem-diameter-and-length-and_1.0 A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland ENVIDAT STAC Catalog 2024-01-01 2024-01-01 7.56, 47.23, 7.56, 47.23 https://cmr.earthdata.nasa.gov/search/concepts/C3383774336-ENVIDAT.umm_json The data presented here were prepared for publication in form of a data paper. The dataset presents an update of the information in Didion M, Herold A, Vulovic Z, Nitzsche J, Stillhard J (2022) Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory. EnviDat. doi:https://www.doi.org/10.16904/envidat.358. The new dataset is based on an alternative, more accurate and comprehensive dataset, that was identified as the source of the dataset in Didion et al. 2022. It includes additional data, including newly digitized information. The dataset includes section-wise stem measurements on 40’349 felled individual trees and empirically derived coarse (diameter >= 7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range. The dataset has undergone quality controls and due to its origin from 768 plots of the Experimental Forest Management project long-term consistency is assured. proprietary +a-dataset-of-40000-trees-with-section-wise-measured-stem-diameter-and-length-and_1.0 A dataset of 40000 trees with section-wise measured stem diameter and length and volume of branches from across Switzerland ALL STAC Catalog 2024-01-01 2024-01-01 7.56, 47.23, 7.56, 47.23 https://cmr.earthdata.nasa.gov/search/concepts/C3383774336-ENVIDAT.umm_json The data presented here were prepared for publication in form of a data paper. The dataset presents an update of the information in Didion M, Herold A, Vulovic Z, Nitzsche J, Stillhard J (2022) Datasets for deriving functions for the stem- and branchwood volume in the Swiss National Forest Inventory. EnviDat. doi:https://www.doi.org/10.16904/envidat.358. The new dataset is based on an alternative, more accurate and comprehensive dataset, that was identified as the source of the dataset in Didion et al. 2022. It includes additional data, including newly digitized information. The dataset includes section-wise stem measurements on 40’349 felled individual trees and empirically derived coarse (diameter >= 7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range. The dataset has undergone quality controls and due to its origin from 768 plots of the Experimental Forest Management project long-term consistency is assured. proprietary a-ice-oxygen-k-edge-nexafs-spectroscopy-data-set-on-gas-phase-processing_1.0 An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing ENVIDAT STAC Catalog 2023-01-01 2023-01-01 8.2071304, 47.5210264, 8.2382011, 47.543743 https://cmr.earthdata.nasa.gov/search/concepts/C3226081770-ENVIDAT.umm_json Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice. proprietary -a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0 A numerical solver for heat and mass transport in snow based on FEniCS ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.8472494, 46.812044, 9.8472494, 46.812044 https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.umm_json This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics proprietary a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0 A numerical solver for heat and mass transport in snow based on FEniCS ALL STAC Catalog 2022-01-01 2022-01-01 9.8472494, 46.812044, 9.8472494, 46.812044 https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.umm_json This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics proprietary +a-numerical-solver-for-heat-and-mass-transport-in-snow-based-on-fenics_1.0 A numerical solver for heat and mass transport in snow based on FEniCS ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.8472494, 46.812044, 9.8472494, 46.812044 https://cmr.earthdata.nasa.gov/search/concepts/C2789814662-ENVIDAT.umm_json This python code uses the Finite Element library FEniCS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format. The dataset contains the code and the output data to reproduce the key Figure 5 from the related publication: _Schürholt, K., Kowalski, J., Löwe, H.; Elements of future snowpack modeling - Part 1: A physical instability arising from the non-linear coupling of transport and phase changes, The Cryosphere, 2022_ The code and potential updates can be accessed directly through git via: https://gitlabext.wsl.ch/snow-physics/snowmodel_fenics proprietary a0782135bcd04d77a1dae4aa71fba47c_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327360338-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection). proprietary a0d9764a3068439b997c42928ef739d2_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn glacier from ERS-1, ERS2 and ENVISAT data for 1992-2010, v1.2 FEDEO STAC Catalog 1992-01-27 2010-06-13 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142504-FEDEO.umm_json This dataset contains time series of ice velocities for the Jakobshavn Glacier in Greenland, which have been derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between between 1992 and 2010. It provides components of the ice velocity and the magnitude of the ice velocity and has been produced as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The dataset contains two time series: 'Greenland_Jakobshavn_TimeSeries_2002_2010' contains an older version of the time series kept for completeness and also to ensure the best temporal coverage. It is based on data from the ASAR instrument on ENVISAT, acquired between 10/11/2002 and 23/09/2010 and contains 47 maps of ice velocity. The second time series 'greenland_jakobshavn_timeseries_1992_2010' contains the latest version of the time serives based on ERS-1, ERS-2 and Envisat data acquired between 27/01/1992 and 13/06/2010 and contains 120 maps.The data is provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland) and ENVEO (Earth Observation Information Technology GmbH). proprietary a13994c5-8d10-4627-90b8-60077ab5de40_NA EnMAP HSI - Level 0 / Quicklook Images - Global FEDEO STAC Catalog 2022-04-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3326864967-FEDEO.umm_json The EnMAP HSI L0 Quicklooks collection contains the VNIR and SWIR quicklook images as well as the quality masks for haze, cloud, or snow; based on the latest atmospheric correction methodology of the land processor. It allows users to get an overview which L0 data has been acquired and archived since the operational start of the EnMAP mission and which data is potentially available for on-demand processing into higher level products with specific processing parameters via the EOWEB-GeoPortal. The database is constantly updated with newly acquired L0 data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth’s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making. For more information, please see the mission website: https://www.enmap.org/mission/ proprietary @@ -17207,8 +17275,8 @@ aad_ais_gz_modis_slope_break_1 Amery Ice Shelf Grounding Zone defined as interpr aad_ctd_database_1 Database of CTD data collected in the Southern Ocean by the AAD, ACE CRC and part of the Southern Ocean Atlas data set. AU_AADC STAC Catalog 1900-01-01 2003-03-09 -180, -80, 180, -15.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214311486-AU_AADC.umm_json Microsoft Access database containing a compilation of CTD data collected in the Southern Ocean from Australian Antarctic Division (AAD), Antarctic Climate and Ecosystems Co-operative Research Centre (ACE CRC) and Hydrographic Atlas of the Southern Ocean (SOA) data sources. This SOA data contains discrete CTD (Conductivity, Temperature and Depth) station data along with a 1 x 1 degree gridded CTD data set interpolated in space and time. Parameters include pressure, temperature, salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, and silicate). Ocean Tools software developed by AAD is available in conjunction with this database to manipulate, extract and visualise data (including station map, transect selection, xy plots, vertical cross sections, geostrophic velocity/transport calculations). The download file contains an access database of the compiled CTD data, a word document containing further information about the structure of the database and the data (AAD CTD Data.doc), and a folder of the original source data, including readmes providing reference details, and specific information. proprietary aae157df-5b91-4a49-b00b-d81729a566d7_NA TerraSAR-X - High Resolution Spotlight Images (TerraSAR-X High Resolution Spotlight) FEDEO STAC Catalog 2007-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457996-FEDEO.umm_json "This collection contains radar image products of the German national TerraSAR-X mission acquired in High Resolution Spotlight mode. High Resolution Spotlight imaging allows for a spatial resolution of up to 1 m at a scene size of 10 km (across swath) x 5 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html" proprietary aae643e1a7614c24b6b604dea82cad93_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Kangerlussuaq Glacier between 2017-07-21 and 2017-08-20, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-07-20 2017-08-20 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143151-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Kangerlussuaq Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-21 and 2017-08-20. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway. proprietary -aamhcpex_1 AAMH CPEX ALL STAC Catalog 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. proprietary aamhcpex_1 AAMH CPEX GHRC_DAAC STAC Catalog 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. proprietary +aamhcpex_1 AAMH CPEX ALL STAC Catalog 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. proprietary ab90030e26c54ba495b1cbec51e137e1_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ADV algorithm), Version 2.31 FEDEO STAC Catalog 2002-07-24 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142756-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31. Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation. proprietary above-and-below-ground-herbivore-communities-along-elevation_1.0 Above- and below-ground herbivore communities along elevation ALL STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. proprietary above-and-below-ground-herbivore-communities-along-elevation_1.0 Above- and below-ground herbivore communities along elevation ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. proprietary @@ -17217,79 +17285,79 @@ accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0 Accessibility accum-measurements-domec-traverse-1982_1 Accumulation Measurements from Pioneerskaya to Dome C, 1982-84 ALL STAC Catalog 1982-01-01 1984-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311710-AU_AADC.umm_json Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse. These documents have been archived in the records store at the Australian Antarctic Division. proprietary accum-measurements-domec-traverse-1982_1 Accumulation Measurements from Pioneerskaya to Dome C, 1982-84 AU_AADC STAC Catalog 1982-01-01 1984-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311710-AU_AADC.umm_json Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse. These documents have been archived in the records store at the Australian Antarctic Division. proprietary accumulation-movement-markers-mirny-domec_1 Detailed Notes on Accumulation/Movement Markers, Mirny-Dome C AU_AADC STAC Catalog 1977-01-01 1978-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311711-AU_AADC.umm_json Detailed notes about each of the markers used for movement (and accumulation) measurements along the Mirny-Dome C traverse line. Includes processing notes from the JMR position analysis. These documents have been archived in the records store at the Australian Antarctic Division. proprietary -accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 ALL STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 AU_AADC STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary -aces1am_1 ACES Aircraft and Mechanical Data ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary +accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 ALL STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary aces1am_1 ACES Aircraft and Mechanical Data GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary +aces1am_1 ACES Aircraft and Mechanical Data ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary aces1cont_1 ACES CONTINUOUS DATA V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary aces1cont_1 ACES CONTINUOUS DATA V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary aces1efm_1 ACES ELECTRIC FIELD MILL V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary aces1efm_1 ACES ELECTRIC FIELD MILL V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary -aces1log_1 ACES LOG DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary aces1log_1 ACES LOG DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary +aces1log_1 ACES LOG DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary aces1time_1 ACES TIMING DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. proprietary aces1time_1 ACES TIMING DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. proprietary aces1trig_1 ACES TRIGGERED DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary aces1trig_1 ACES TRIGGERED DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary -acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) ALL STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) AU_AADC STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary +acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) ALL STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary acoustic_doppler_current_profiler_data_-_2010 Acoustic Doppler Current Profiler Data - 2010 ALL STAC Catalog 2010-08-21 2010-09-17 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602088-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary acoustic_doppler_current_profiler_data_-_2010 Acoustic Doppler Current Profiler Data - 2010 SCIOPS STAC Catalog 2010-08-21 2010-09-17 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602088-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary -acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 SCIOPS STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 ALL STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary -active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 ALL STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary +acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 SCIOPS STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 SCIOPS STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary +active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 ALL STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 ALL STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary -active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary +active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary active_layer_arcss_grid_barrow_alaska_2010 Active Layer ARCSS grid Barrow, Alaska 2010 SCIOPS STAC Catalog 2010-06-30 2010-08-11 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600590-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary active_layer_arcss_grid_barrow_alaska_2010 Active Layer ARCSS grid Barrow, Alaska 2010 ALL STAC Catalog 2010-06-30 2010-08-11 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600590-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary -active_layer_arcss_grid_barrow_alaska_2011 Active Layer ARCSS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-07-25 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600390-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary active_layer_arcss_grid_barrow_alaska_2011 Active Layer ARCSS grid Barrow, Alaska 2011 SCIOPS STAC Catalog 2011-06-14 2011-07-25 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600390-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary +active_layer_arcss_grid_barrow_alaska_2011 Active Layer ARCSS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-07-25 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600390-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary active_layer_arcss_grid_barrow_alaska_2012 Active Layer ARCSS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600333-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary active_layer_arcss_grid_barrow_alaska_2012 Active Layer ARCSS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600333-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary -active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-05 2011-08-12 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600341-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alaska 2011 ALL STAC Catalog 2011-06-05 2011-08-12 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600341-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary -active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary +active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-05 2011-08-12 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600341-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary +active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 SCIOPS STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary -active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary +active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary ada968fd392d49fbbb07ac84eeb23ac6_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Zachariae Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-06-24 2017-08-10 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142710-FEDEO.umm_json This dataset contains an optical ice velocity time series and seasonal product of the Zachariae Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway. proprietary adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table Adaptive long-term fasting in land and ice-bound polar bears: Data Table ALL STAC Catalog 2008-01-01 2011-12-31 -155, 70, -122, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214602399-SCIOPS.umm_json The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover. proprietary adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table Adaptive long-term fasting in land and ice-bound polar bears: Data Table SCIOPS STAC Catalog 2008-01-01 2011-12-31 -155, 70, -122, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214602399-SCIOPS.umm_json The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover. proprietary adcp_2 Aurora Australis Southern Ocean ADCP data AU_AADC STAC Catalog 1994-12-13 1999-09-07 75, -69, 165, -41 https://cmr.earthdata.nasa.gov/search/concepts/C1214311719-AU_AADC.umm_json Acoustic Doppler current profiler (ADCP) measurements from a hull mounted 150 kHz narrow band ADCP unit were collected in the Southern Ocean from 1994 to 1999, on the following cruises: au9404, au9501, au9604, au9601, au9701, au9706, au9807 and au9901. The fields in this dataset are: Currents bottom depth cruise number ship speed time velocity GPS proprietary add104f4c4454b629dbc7648efaa1b50_NA ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR (544.6 GHz) Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2001-01-01 2013-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142584-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR (544.6 GHz) instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-MZM-SMR_ODIN-544_6_GHz-2008-fv0001.nc” contains monthly zonal mean data for ODIN/SMR at 544.6GHz in 2008. proprietary -adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT region AU_AADC STAC Catalog 1931-02-13 2006-12-08 38.2, -69.6, 89.5, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311746-AU_AADC.umm_json A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E). The census records have been collected and compiled from a literature search. proprietary adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT region ALL STAC Catalog 1931-02-13 2006-12-08 38.2, -69.6, 89.5, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311746-AU_AADC.umm_json A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E). The census records have been collected and compiled from a literature search. proprietary +adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT region AU_AADC STAC Catalog 1931-02-13 2006-12-08 38.2, -69.6, 89.5, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311746-AU_AADC.umm_json A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E). The census records have been collected and compiled from a literature search. proprietary adu_birp Animal Demography Unit - The Birds in Reserves Project (BIRP) CEOS_EXTRA STAC Catalog 1906-02-05 2007-05-20 16.46, -34.77, 32.86, -22.61 https://cmr.earthdata.nasa.gov/search/concepts/C2232477691-CEOS_EXTRA.umm_json BIRP is a joint project of BirdLife South Africa (BLSA), and the Animal Demography Unit (ADU), based at the University of Cape Town (UCT). The basic purpose of BIRP is to compile a comprehensive catalogue of the species of birds which occur and breed in South Africa’s many protected areas. A database of this kind will help to identify the species which are as yet not adequately protected and will also provide the managers of protected areas with information useful in setting management policies. proprietary adu_cwac Animal Demography Unit - Coordinated Waterbird Counts (CWAC) CEOS_EXTRA STAC Catalog 1983-07-15 2006-09-30 16.46, -34.72, 32.88, -22.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232477679-CEOS_EXTRA.umm_json The Coordinated Waterbird Counts (CWAC) project was launched in 1992. The objective of CWAC is to monitor South Africa's waterbird populations and the conditions of the wetlands which are important for waterbirds. This is being done by means of a programme of regular mid-summer and mid-winter censuses at a large number of South African wetlands. Regular six-monthly counts are conducted; however, we do encourage counters to survey their wetlands on a more regular basis as this provides better data. CWAC currently monitors over 400 wetlands around the country on a regular basis, and furthermore curates waterbird data for close to 600 wetlands. proprietary adu_safring Animal Demography Unit - South African Bird Ringing Unit (SAFRING) CEOS_EXTRA STAC Catalog 1899-12-30 2004-12-31 -76.33, -71.9, 73.5, 72.25 https://cmr.earthdata.nasa.gov/search/concepts/C2232477669-CEOS_EXTRA.umm_json The South African Bird Ringing Unit (SAFRING) administers bird ringing in southern Africa, supplying rings, ringing equipment and services to volunteer and professional ringers in South Africa and neighbouring countries. All ringing records are curated by SAFRING, which is an essential arm of the Animal Demography Unit. Contact is maintained by the SAFRING Project Coordinator with all ringers (banders in North American or Australian terminology). The Bird Ringing Scheme in South Africa was initiated in 1948, so 1998 saw the 50th anniversary of the scheme. During this period over 1.7 million birds of 852 species were ringed. There have been a total of 16 800 ring recoveries since the inception of the scheme. This gives an overall recovery rate for rings in southern Africa of marginally less than 1%, averaged across all species. This probability varies enormously across species. proprietary aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 ALL STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 AU_AADC STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary -aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 ALL STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 AU_AADC STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary -aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division ALL STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary +aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 ALL STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division AU_AADC STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary -aerial_photo_sea_ice_ARISE_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003 AU_AADC STAC Catalog 2003-09-10 2003-10-31 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611591-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ARISE from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary +aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division ALL STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary aerial_photo_sea_ice_ARISE_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003 ALL STAC Catalog 2003-09-10 2003-10-31 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611591-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ARISE from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary +aerial_photo_sea_ice_ARISE_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003 AU_AADC STAC Catalog 2003-09-10 2003-10-31 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611591-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ARISE from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary aerial_photo_sea_ice_ISPOL_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ISPOL voyage in 2004 AU_AADC STAC Catalog 2004-11-06 2005-01-19 -58.2, -69.67, -55.2, -67.57 https://cmr.earthdata.nasa.gov/search/concepts/C1292611592-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the voyage, Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05, 6 Nov 2004 to 19 Jan 2005. Flights were conducted around the edges of a triangular array of drifting buoys each transmitting GPS location. Flights throughout the experiment show changes in the surface properties (floe size, extent of surface flooding) with time. See the metadata record 'AAD buoy data collected during ISPOL 2004, Western Weddell Sea' for more information on the ISPOL project. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ISPOL from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ISPOL are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary aerial_photo_sea_ice_ISPOL_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ISPOL voyage in 2004 ALL STAC Catalog 2004-11-06 2005-01-19 -58.2, -69.67, -55.2, -67.57 https://cmr.earthdata.nasa.gov/search/concepts/C1292611592-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the voyage, Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05, 6 Nov 2004 to 19 Jan 2005. Flights were conducted around the edges of a triangular array of drifting buoys each transmitting GPS location. Flights throughout the experiment show changes in the surface properties (floe size, extent of surface flooding) with time. See the metadata record 'AAD buoy data collected during ISPOL 2004, Western Weddell Sea' for more information on the ISPOL project. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ISPOL from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ISPOL are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary -aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 AU_AADC STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 ALL STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary +aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 AU_AADC STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary aerial_photo_sea_ice_shapefiles_1 Flight lines and photo centres of aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE and ISPOL voyages in 2003 and 2004 AU_AADC STAC Catalog 2003-09-10 2005-01-19 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611653-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05. Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 The ARISE and ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. proprietary -aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 ALL STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

proprietary aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 SCIOPS STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

proprietary +aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 ALL STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

proprietary aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 ALL STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 AU_AADC STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 ALL STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 AU_AADC STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ALL STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary -aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ALL STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary +aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ALL STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary aerosol_properties_725_1 SAFARI 2000 Physical and Chemical Properties of Aerosols, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-17 2000-09-13 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2789011485-ORNL_CLOUD.umm_json SAFARI 2000 provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples. These aerosol particle samples were collected using the University of Washington Convair CV-580 research aircraft (Posfai et al., 2003). proprietary aes5davg_236_1 BOREAS AES Five-day Averaged Surface Meteorological and Upper Air Data ORNL_CLOUD STAC Catalog 1976-01-01 1997-01-01 -107.87, 52.17, -97.83, 57.35 https://cmr.earthdata.nasa.gov/search/concepts/C2807614663-ORNL_CLOUD.umm_json Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba. proprietary aes_upl1_238_1 BOREAS AFM-05 Level-1 Upper Air Network Data, R1 ORNL_CLOUD STAC Catalog 1993-08-16 1996-10-22 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2812433046-ORNL_CLOUD.umm_json Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region. proprietary @@ -17313,25 +17381,25 @@ agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricult agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary air_methane_lawdome_1 Dated Readings For Air Composition And Methane From Law Dome Ice Core AU_AADC STAC Catalog 1988-01-01 1993-12-31 112.8, -66.771, 112.81, -66.77 https://cmr.earthdata.nasa.gov/search/concepts/C1214311761-AU_AADC.umm_json "This work was completed as part of ASAC project 757 (ASAC_757). This file comprises three main records compiled for publication in the following: V. Morgan, M. Delmotte, T. van Ommen, J. Jouzel, J. Chappellaz, S. Woon, V. Masson-Delmotte and D. Raynaud. Relative Timing of Deglacial Climate Events in Antarctica and Greenland, Science, 13 September 2002, Vol 297 (5588), pp. 1862-1864, DOI: 10.1126/science.1074257. Supporting Material - http://www.sciencemag.org/cgi/content/full/sci;297/5588/1862/DC1 Law Dome is a small (200 km in diameter) ice sheet located at the edge of the Indian Ocean sector of East Antarctica. The core site, near the summit of Law Dome (66 degrees 46'S, 112 degrees 48'E), is characterised by a high rate of accumulation (late Holocene average, 0.68 m ice equivalent per year) that results in an ice core with a highly tapered time scale in which the Holocene represents some 93% of the ice thickness of 1200 m. However, the full Law Dome isotopic record generally matches the long records from Vostok and Byrd to at least 80 ka, indicating that the record is continuous and undisturbed over this period. The Law Dome record is suited to gas-synchronisation studies because the high accumulation rate and consequent rapid burial give a small age difference (Delta age) between trapped air and the older enclosing ice. Derivation of an age scale for the Law Dome core, is based upon a Dansgaard- Johnsen flow model (S1) matched to the observed layer thinning (S2). Continuously sampled seasonal cycles down to ~1/3 ice-thickness (~1ky) and spot measurements of seasonal layers to ~85% ice-thickness (~4 ky) constrain the ice-flow model through this period in which mean accumulation is assumed to be free of large trends. Chronological control in the lower portion of the ice-sheet prior to 4 ky is through ties to other records. For the period of discussion, namely 10 ky to 17 ky, ties at 9.6 ky, 11.0 ky, 11.6 ky, 12.5 ky, 12.8 ky, 14.3 ky and 16.3 ky, are obtained by matching air composition changes with those of GRIP. The 9.6 ky tie is obtained by matching to d18O of air in GRIP (S3) and GISP2 (S4) data, and the remainder synchronise with the Byrd and GRIP CH4 records (S5). Dust concentration data also provide additional constraints on the 16.3 ky tie. Beyond 16.3 ky control is by a tie at 32 ky (based on both dust and d18Oice matched to the Byrd ice core (S6) on the GRIP timescale (S5)). The mean temporal resolution of the LD isotope data is ~24y through this period. The air-composition age-ties require Delta age computations for sequencing events within the LD record and for synchronisation of the chronology with GRIP. The high accumulation at DSS results in a particularly small Delta age value. The modern difference between ice-age and gas-age is 60 plus or minus 2 years for methane (S7). Note that at such low Delta age values, the diffusive mixing time from the free atmosphere down to seal-off depth becomes significant and must be accounted for; in the case of CH4 this is ~8 years (S7). The absolute chronology derived for the LD record has contributions from both the LD and GRIP Delta age errors, but the relative timing between the LD CH4 and water isotope (d18Oice) signals is only uncertain to within the small errors associated with LD Delta age. While the present-day trapping age at LD is small, lower temperatures and accumulation rates during the deglaciation lead to longer trapping times. To estimate Delta age under past conditions, we use a model (S8) to compute trapping age from accumulation and temperature (this model agrees with precise experimentally determined present day values). Since we have no direct indicators for palaeoaccumulation and palaeotemperature, we adopt two scenarios that use alternative estimation methods. Estimation of palaeotemperature from the isotope data in both scenarios is by application of a calibration slope, ""Beta ppt/degrees C"". For the young chronology, which has minimum Delta age, the commonly applied spatial slope of Beta=0.67 ppt/degrees C is used, giving relatively warm temperatures. The default chronology uses a long-term temporal calibration (S9) for Law Dome, Beta=0.44 ppt/degrees C. This estimate, which is seasonally derived, gives greater temperature sensitivity for isotopic changes than the spatial slope. The use of this lower value for Beta is supported by direct comparisons between annual averages in d18O and temperature at the site and elsewhere on Law Dome. Over several years to a few decades, these yield coefficients of typically ~0.33 ppt/degrees C. We adopt the value 0.44 ppt/degrees C as a conservative choice, based on a longer-term calibration and because the incorporation of seasonal sea-ice variations may better capture glacial-to- Holocene environmental shifts. Estimation of palaeoaccumulation for the young chronology is via the commonly applied method (see, e.g. S5) that scales modern accumulation-rate using the derivative of saturation vapour-pressure versus temperature relationship (also using Beta=0.67 ppt/degrees C). This method explicitly assumes no non-thermodynamic changes to moisture transport during climate variations (such as, e.g., atmospheric circulation changes) that may be important at this near-coastal location. Our alternative palaeoaccumulation estimate used for the default chronology assumes that the flow-model is correct and infers accumulation from the known age-intervals between the gas ties. This leads to considerably larger changes in accumulation which may nonetheless be understandable given the distinctively high Holocene precipitation regime that prevails at Law Dome. In addition, dust concentration data show a larger LGM to Holocene decrease at LD than Vostok. If relative flux changes at the two sites are similar, then the exaggerated dilution at LD is consistent with a large interglacial accumulation shift. Trapped gas measurements were made in France: CH4 measurements at LGGE, Grenoble and d18Oair measurements at LSCE, Saclay. Both analyses were conducted using a wet extraction procedure to release the air of the ice and followed by an injection into a gas chromatograph (CH4 measurement) or by a mass spectrometer isotopic analysis (d18Oair measurements). Both analyses were conducted using established procedures (S10,S11). The methane analytical uncertainty is plus or minus 20 ppbv with values were obtained on a single measurement (in which the sample was exhausted) and are presented on the LGGE scale which differs slightly from the NOAA scale but is well calibrated against it: LGGE = 1.02*NOAA (S12). The d18Oair values arise from means of duplicate measurements (except for one point with an obvious experimental problem, 1127.492 m depth). The analytical precision for d18Oair is around 0.05 ppt with a mean reproducibility of about 0.1 ppt. d18Oice measurements were made in Hobart and have an analytical precision of approximately 0.1 ppt. The results are expressed using the conventional reference of VSMOW (Vienna Standard Mean Ocean Water). Supporting References and Notes S1. W. Dansgaard, S. J. Johnsen, J. Glaciol., 8, 215 (1969). S2. V. Morgan et al., J. Glaciol., 43, 3 (1997). S3. M. Cross, (Compiler) Greenland summit ice cores CD-ROM. Boulder, CO: National Snow and Ice Data Center in association with the World Data Center for Paleoclimatology at NOAA-NGDC, and the Institute of Arctic and Alpine Research (1997). S4. M. Bender et al., Nature 372, 663-666 (1994). S5. T. Blunier, et al., Nature 394, 739 (1998). S6. S. J. Johnsen, W. Dansgaard, H. B. Clausen, C. C. Langway, Nature, 235, 429 (1972). S7. D. M. Etheridge et al., J. Geophys. Res., 101, 4115 (1996). S8. J.-M. Barnola, P. Pimienta, D. Raynaud, Y. S. Korotkevich, Tellus Ser. B, 43, 83 (1991). S9. T. D. van Ommen, V. Morgan, J. Geophys. Res., 102, 9351 (1997). S10. J. Chappellaz, et al., J. Geophys. Res., 102, 15987, (1997). S11. B. Malaize, Analyse isotopique de l'oxygene de l'air piege dans les glaces de l'Antarctique et du Groenland: correlation inter-hemispheriques et effet Dole, PhD thesis, University Paris 6, (1998). S12. T. Sowers et al, J. Geophys. Res., 102, 26527, (1997)." proprietary air_sea_gas_exchange_xdeg_1208_1 ISLSCP II Air-Sea Carbon Dioxide Gas Exchange ORNL_CLOUD STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785340637-ORNL_CLOUD.umm_json This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959. Only the ocean water pCO2 values measured using direct gas-seawater equilibration methods were used. The results represent the climatological distributions under non-El Nino conditions. Since the measurements were made in different years, during which the atmospheric pCO2 was increasing, they were corrected to a single reference year (arbitrarily chosen to be 1995) on the basis of the following assumptions: -Surface waters in subtropical gyres mix vertically at slow rates with subsurface waters due to the presence of strong stratification at the base of the mixed layer. This will allow a long contact time with the atmosphere to exchange CO2. Therefore, their CO2 chemistry tends to follow the atmospheric CO2 increase. Accordingly, the pCO2 measured in a given month and year is corrected to the same month of the reference year 1995 using changes in the atmospheric CO2 concentration occurred during this period.-Oceanic pCO2 measurements made after the beginning of 1979 have been corrected to 1995 using the atmospheric CO2 concentration data from the GLOBALVIEW-CO2 database (2000), in which the zonal mean atmospheric concentrations (for each 0.05 in sine of latitude) within the planetary boundary layer are summarized for each month since 1979 to 2000.-Pre-1979 oceanic pCO2 data were corrected to 1979 using the annual mean trend for the global mean atmospheric CO2 concentration constructed from the Mauna Loa data of Keeling and Whorf (2000), and then from 1979 to 1995 using the GLOBALVIEW-CO2 database. -Measurements for pCO2 made in the following areas have been corrected for the time of observation; 45 degrees N, 50 degrees S, in the Atlantic Ocean, north of 50 degrees S in the Indian Ocean, 40 degrees N, 50 degrees S in the western Pacific west of the date line, and 40 degrees N, 60 degrees S, in the eastern Pacific east of the date line. proprietary -air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 SCIOPS STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 ALL STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary +air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 SCIOPS STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary airmoss_chamela_mexico USGS AirMOSS - Chamela, Mexico USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567952-USGS_LTA.umm_json North American ecosystems are critical components of the global carbon cycle, exchanging large amounts of carbon dioxide and other gases with the atmosphere. Net ecosystem exchange (NEE) quantifies these carbon fluxes, but current continental-scale estimates contain high levels of uncertainty. Root-zone soil moisture (RZSM) and its spatial and temporal hetergeneity influence NEE and contribute as much as 60-80 percent to the uncertainty. Energy and CO2 Fluxes have been monitored from 1997 to 2007 using Bowen Ratio technique, and since spring of 2004 with eddy covariance. proprietary airscm3b_448_1 BOREAS RSS-16 Level-3b DC-8 AIRSAR CM Images ORNL_CLOUD STAC Catalog 1993-08-12 1995-07-31 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2929127558-ORNL_CLOUD.umm_json Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). proprietary airscpex_1 Atmospheric Infrared Sounder (AIRS) CPEX GHRC_DAAC STAC Catalog 2017-05-11 2017-07-16 -130.881382, -18.2515803, -14.6008026, 64.1143891 https://cmr.earthdata.nasa.gov/search/concepts/C2721994875-GHRC_DAAC.umm_json The Atmospheric Infrared Sounder (AIRS) CPEX dataset contains products obtained from the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions from May through June 2017. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 11, 2017 through July 16, 2017 and are available in HDF-4 format. proprietary airssy3b_507_1 BOREAS RSS-16 Level-3b DC-8 AIRSAR SY Images ORNL_CLOUD STAC Catalog 1993-08-12 1995-07-31 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2929155651-ORNL_CLOUD.umm_json Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high resolution images that represent the radar backscatter of the imaged surface atdifferent frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). The level-3b AIRSAR SY data are the JPL synoptic product and contain 3 of the 12 total frequency and polarization combinations that are possible. proprietary airsunp_61_1 Optical Thickness Data: Aircraft (OTTER) ORNL_CLOUD STAC Catalog 1990-08-13 1990-08-15 -124.02, 43.97, -123.22, 46.13 https://cmr.earthdata.nasa.gov/search/concepts/C2804769299-ORNL_CLOUD.umm_json Airborne sunphotometer data collected on 8/13-15/90 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance proprietary ais_1970_log_1 Amery Ice Shelf Traverse Daily Log, 1970 AU_AADC STAC Catalog 1970-01-07 1970-02-12 65, -74, 74, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305702-AU_AADC.umm_json The Australian Antarctic Division carried out a traverse to the Amery Ice Shelf in the summer of 1970. A daily log of the activities carried out was maintained, noting what the traverse team did, and the problems they dealt with along the traverse. Records for this work have been archived at the Australian Antarctic Division. Logbook(s): Glaciology Amery Ice Shelf Traverse Summer 1970 - The daily log from the traverse. proprietary -alan---nature-sustainability_1.0 Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods ALL STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383774605-ENVIDAT.umm_json "Raw data and R Code to do all the analysis performed for the paper ""Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods"". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset." proprietary alan---nature-sustainability_1.0 Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383774605-ENVIDAT.umm_json "Raw data and R Code to do all the analysis performed for the paper ""Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods"". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset." proprietary +alan---nature-sustainability_1.0 Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods ALL STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383774605-ENVIDAT.umm_json "Raw data and R Code to do all the analysis performed for the paper ""Advancing sustainable LED solutions to mitigate light-pollution impacts on arthropods"". Including data for both flight-active insects and ground-dwelling arthropods combined (PT_TRF_all.csv), flight-active insects alone (ALANeX_PT_all_clean_control.csv) and ground-dwelling insects alone (ALANeX_TRF_all_clean_control.csv). For the NMDS analysis, there is a data set for the flight-active insects (ALANeX_PT_NMDS.csv) and the ground-dwelling arthropods (ALANeX_TRF_NMDS.csv). For the Morans I use the 'ALANeX_MoransI.csv' dataset." proprietary alaska_census_regional_database Alaska Census Regional Database SCIOPS STAC Catalog 1970-01-01 2000-01-01 -129, 50, 169, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602419-SCIOPS.umm_json 1970-2000 decennial census results by 27 census areas conformed to 2000 Census geography. Dataset consists of 611 variables covering demography, employment, education, income, mobility, and housing. proprietary alaska_census_regional_database Alaska Census Regional Database ALL STAC Catalog 1970-01-01 2000-01-01 -129, 50, 169, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602419-SCIOPS.umm_json 1970-2000 decennial census results by 27 census areas conformed to 2000 Census geography. Dataset consists of 611 variables covering demography, employment, education, income, mobility, and housing. proprietary -alaskan_air_ground_snow_and_soil_temperatures__1998-2005 Alaskan Air Ground Snow and Soil Temperatures 1998-2005 SCIOPS STAC Catalog 1998-08-29 2007-11-30 -164.761, 64.919, -148.6, 70.439 https://cmr.earthdata.nasa.gov/search/concepts/C1214600491-SCIOPS.umm_json This data set contains air and ground temperature measurements collected from three different regions, each with multiple sites. The regions sampled are North Slope, Council, and Ivotuk. Early measurements were taken as part of the Land-Atmosphere-Ice Interactions - Arctic Transitions in the Land-Atmosphere System (LAII-ATLAS) program. The research project was funded by the Arctic System Sciences (ARCSS) Program, grant numbers OPP-9721347, OPP-9870635, and OPP-9732126 proprietary alaskan_air_ground_snow_and_soil_temperatures__1998-2005 Alaskan Air Ground Snow and Soil Temperatures 1998-2005 ALL STAC Catalog 1998-08-29 2007-11-30 -164.761, 64.919, -148.6, 70.439 https://cmr.earthdata.nasa.gov/search/concepts/C1214600491-SCIOPS.umm_json This data set contains air and ground temperature measurements collected from three different regions, each with multiple sites. The regions sampled are North Slope, Council, and Ivotuk. Early measurements were taken as part of the Land-Atmosphere-Ice Interactions - Arctic Transitions in the Land-Atmosphere System (LAII-ATLAS) program. The research project was funded by the Arctic System Sciences (ARCSS) Program, grant numbers OPP-9721347, OPP-9870635, and OPP-9732126 proprietary +alaskan_air_ground_snow_and_soil_temperatures__1998-2005 Alaskan Air Ground Snow and Soil Temperatures 1998-2005 SCIOPS STAC Catalog 1998-08-29 2007-11-30 -164.761, 64.919, -148.6, 70.439 https://cmr.earthdata.nasa.gov/search/concepts/C1214600491-SCIOPS.umm_json This data set contains air and ground temperature measurements collected from three different regions, each with multiple sites. The regions sampled are North Slope, Council, and Ivotuk. Early measurements were taken as part of the Land-Atmosphere-Ice Interactions - Arctic Transitions in the Land-Atmosphere System (LAII-ATLAS) program. The research project was funded by the Arctic System Sciences (ARCSS) Program, grant numbers OPP-9721347, OPP-9870635, and OPP-9732126 proprietary albedo_line_snow_depths Albedo Line Snow Depths ALL STAC Catalog 2009-04-27 2009-04-28 -157, 71, -156, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600343-SCIOPS.umm_json Snow depth measurements recorded every half meter along the transects used for albedo measurements using a GPS magnaprobe. Included in the file are latitude, longitude, and snow depth. The first set of columns are at the south site, the second set are at the north site. Note that the south site was surveyed first along the line every half meter, and then a large dune field north of the line was extensively surveyed. Data Citation: Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Sturm. (Nov. 29, 2009, Updated May 9, 2012). Albedo Line Snow Depths (SIZONET). UCAR/NCAR - CISL - ACADIS. http://dx.doi.org/10.5065/D6057CV2 proprietary albedo_line_snow_depths Albedo Line Snow Depths SCIOPS STAC Catalog 2009-04-27 2009-04-28 -157, 71, -156, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600343-SCIOPS.umm_json Snow depth measurements recorded every half meter along the transects used for albedo measurements using a GPS magnaprobe. Included in the file are latitude, longitude, and snow depth. The first set of columns are at the south site, the second set are at the north site. Note that the south site was surveyed first along the line every half meter, and then a large dune field north of the line was extensively surveyed. Data Citation: Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Sturm. (Nov. 29, 2009, Updated May 9, 2012). Albedo Line Snow Depths (SIZONET). UCAR/NCAR - CISL - ACADIS. http://dx.doi.org/10.5065/D6057CV2 proprietary ali_etm_tandem_821_1 SAFARI 2000 ALI/ETM+ Tandem Image Pair for Skukuza, South Africa, May 2001 ORNL_CLOUD STAC Catalog 2001-05-30 2001-05-30 30.76, -25.5, 33.12, -23.59 https://cmr.earthdata.nasa.gov/search/concepts/C2789740161-ORNL_CLOUD.umm_json A tandem pair of Advanced Land Imager (ALI) and Landsat Enhanced Thematic Mapper Plus (ETM+) scenes covering the same part of Kruger National Park (KNP), South Africa (including the Skukuza tower site and rest camp), were acquired about a minute apart on May 30, 2001. The ALI is one of three instruments aboard NASA's first New Millennium Program Earth Observing 1 (EO-1) satellite. ALI is a technology validation testbed that employs novel wide-angle optics and a highly integrated multispectral and panchromatic spectroradiometer.The tandem pair was produced to evaluate the differences between ALI and ETM+ and determine if technology similar to that of the ALI is suitable for future land imaging that will continue the observations begun by the Landsat satellites in 1972.The ALI and ETM+ images are false color composites combining shortwave infrared, near infrared, and visible wavelengths, displayed as red, green, and blue, respectively. Dense vegetation appears green. The similarity of the images demonstrates the ability of the ALI to produce data comparable to ETM+. Several SAFARI 2000 field campaigns conducted in KNP provided ground-based data needed to evaluate measurements from the satellite sensors.Each band is stored as an individual binary file. A metadata file accompanies each set of ALI and ETM+ band files to document the path and row number, sample and line counts, band file names, and sun azimuth and elevation angles. There is also a calibration parameter file that was used for 1R processing. proprietary -allADCP_GB Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC. ALL STAC Catalog 1995-04-25 1995-06-16 -68, 40.5, -67, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json Acoustic Doppler Current Profiler (ADCP) observations, were collected from the R/V Seward Johnson on two cruises to the Georges Bank region, April-June 1995. Three different ADCP units were used: two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The broadband 150 kHz unit was used at anchor stations with data reported at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz units collected data in the along track mode with data reported at five minute intervals. For each time interval, the u and v components of currents are reported at uniform depth intervals throughout the water column. Ship cruise dates R/V Seward Johnson 9506 1995 04 25 1995 05 02 R/V Seward Johnson 9508 1995 06 06 1995 06 16 proprietary allADCP_GB Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC. SCIOPS STAC Catalog 1995-04-25 1995-06-16 -68, 40.5, -67, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json Acoustic Doppler Current Profiler (ADCP) observations, were collected from the R/V Seward Johnson on two cruises to the Georges Bank region, April-June 1995. Three different ADCP units were used: two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The broadband 150 kHz unit was used at anchor stations with data reported at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz units collected data in the along track mode with data reported at five minute intervals. For each time interval, the u and v components of currents are reported at uniform depth intervals throughout the water column. Ship cruise dates R/V Seward Johnson 9506 1995 04 25 1995 05 02 R/V Seward Johnson 9508 1995 06 06 1995 06 16 proprietary +allADCP_GB Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC. ALL STAC Catalog 1995-04-25 1995-06-16 -68, 40.5, -67, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json Acoustic Doppler Current Profiler (ADCP) observations, were collected from the R/V Seward Johnson on two cruises to the Georges Bank region, April-June 1995. Three different ADCP units were used: two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The broadband 150 kHz unit was used at anchor stations with data reported at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz units collected data in the along track mode with data reported at five minute intervals. For each time interval, the u and v components of currents are reported at uniform depth intervals throughout the water column. Ship cruise dates R/V Seward Johnson 9506 1995 04 25 1995 05 02 R/V Seward Johnson 9508 1995 06 06 1995 06 16 proprietary alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0 Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the “Flavescence dorée” epidemics ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4484863, 45.8115721, 9.4372559, 46.4586735 https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.umm_json Flavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dorée phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees. proprietary alos-prism-l1c_8.0 ALOS PRISM L1C ESA STAC Catalog 2006-08-01 2011-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280661-ESA.umm_json "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C data acquired by ESA stations (Kiruna, Maspalomas, Matera, Tromsoe) in the _$$ADEN zone$$ https://earth.esa.int/eogateway/documents/20142/37627/Information-on-ALOS-AVNIR-2-PRISM-Products-for-ADEN-users.pdf , in addition to worldwide data requested by European scientists. The ADEN zone was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East. The full mission archive is included in this collection, though with gaps in spatial coverage outside of the; with respect to the L1B collection, only scenes acquired in sensor mode, with Cloud Coverage score lower than 70% and a sea percentage lower than 80% are published: • Time window: from 2006-08-01 to 2011-03-31 • Orbits: from 2768 to 27604 • Path (corresponds to JAXA track number): from 1 to 665 • Row (corresponds to JAXA scene centre frame number): from 310 to 6790. The L1C processing strongly improve accuracy compared to L1B1 from several tenths of meters in L1B1 (~40 m of northing geolocation error for Forward views and ~10-20 m for easting errors) to some meters in L1C scenes (< 10 m both in north and easting errors). The collection is composed by only PSM_OB1_1C EO-SIP product type, with PRISM sensor operating in OB1 mode and having the three views (Nadir, Forward and Backward) at 35km width. The most part of the products contains all the three views, but the Nadir view is always available and is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the JAXA view ID naming convention." proprietary alos.prism.l1c.european.coverage.cloud.free_12.0 ALOS PRISM L1C European Coverage Cloud Free ESA STAC Catalog 2007-03-26 2011-03-31 -25, 27, 46, 72 https://cmr.earthdata.nasa.gov/search/concepts/C3325394222-ESA.umm_json This collection is composed of a subset of ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C products from the _$$ALOS PRISM L1C collection$$ https://earth.esa.int/eogateway/catalog/alos-prism-l1c (DOI: 10.57780/AL1-ff3877f) which have been chosen so as to provide a cloud-free coverage over Europe. 70% of the scenes contained within the collection have a cloud cover percentage of 0%, while the remaining 30% of the scenes have a cloud cover percentage of no more than 20%. The collection is composed of PSM_OB1_1C EO-SIP products, with the PRISM sensor operating in OB1 mode with three views (Nadir, Forward and Backward) at 35 km width. proprietary @@ -17359,12 +17427,12 @@ ams_cs93_403_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological D ams_cs94_404_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1994 ORNL_CLOUD STAC Catalog 1994-01-01 1994-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090015-ORNL_CLOUD.umm_json Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary ams_cs95_405_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1995 ORNL_CLOUD STAC Catalog 1995-01-01 1995-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090046-ORNL_CLOUD.umm_json Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary ams_cs96_406_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1996 ORNL_CLOUD STAC Catalog 1996-01-01 1996-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090091-ORNL_CLOUD.umm_json Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary -amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC STAC Catalog 1998-08-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 ALL STAC Catalog 1998-08-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary +amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC STAC Catalog 1998-08-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 GHRC_DAAC STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 ALL STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary -amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 GHRC_DAAC STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 ALL STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary +amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 GHRC_DAAC STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary anezet-analysing-net-zero-transformations_1.0 ANEZET: Analysing Net-Zero Transformations ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081289-ENVIDAT.umm_json We have analysed past transformations in Switzerland in four environmental domains, with the aim to draw conclusions for current challenges, such as the net‐zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research. proprietary angle-of-repose-of-snow_1.0 Angle of repose experiments with natural and spherical snow ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814625-ENVIDAT.umm_json Angle of repose experiments were performed with different snow types at temperatures between -2 and -40°C. They were used to examine granular snow dynamics on the grain-scale with focus on the role of grain shape and cohesion. The angle of repose was observed by sieving snow onto a round, freestanding base until a stationary heap was formed. This dataset consists of 1) the images of the experimental heaps that were taken to determine the angle of repose, 2) one binary 3D micro computed tomography image of each snow type. The CT images were taken with the SLF micro-CT40 to characterize snow properties and grain shape. The experiments with natural snow types (rounded and faceted grains) and spherical model snow allowed for an examination of the differences in granular properties between natural grain shapes and spherical particles in view of Discrete Element Modelling. With the chosen temperatures, the effect of sintering could be observed that increases the angle of repose with increasing temperature. proprietary ant_dist_1 Antarctic Distances AU_AADC STAC Catalog 1996-11-01 1996-11-01 45, -90, 160, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311734-AU_AADC.umm_json Spreadsheet of distances between Antarctic locations (eg. Mawson Station, Prince Edward Island) and world locations (eg. Melbourne, Santiago). proprietary @@ -17387,15 +17455,15 @@ asas Advanced Solid-state Array Spectroradiometer (ASAS) USGS_LTA STAC Catalog 1 asas Advanced Solid-state Array Spectroradiometer (ASAS) ALL STAC Catalog 1988-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566261-USGS_LTA.umm_json The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle. The ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.). Data in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994. proprietary asas_l1b_562_1 BOREAS RSS-02 Level-1b ASAS Image Data: At-sensor Radiance in BSQ Format ORNL_CLOUD STAC Catalog 1994-04-19 1996-07-20 -106.32, 53.24, -97.23, 56.25 https://cmr.earthdata.nasa.gov/search/concepts/C2813527156-ORNL_CLOUD.umm_json The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude. The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996. proprietary asasrefl_287_1 BOREAS RSS-02 Extracted Reflectance Factors Derived from ASAS Imagery ORNL_CLOUD STAC Catalog 1994-05-24 1996-07-20 -106.2, 53.24, -104.62, 53.99 https://cmr.earthdata.nasa.gov/search/concepts/C2813382300-ORNL_CLOUD.umm_json Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS. proprietary -ascatcpex_1 Advanced Scatterometer (ASCAT) CPEX GHRC_DAAC STAC Catalog 2017-05-24 2017-07-16 160.241, 3.9062, -25.0958, 42.5176 https://cmr.earthdata.nasa.gov/search/concepts/C2428509185-GHRC_DAAC.umm_json The Advanced Scatterometer (ASCAT) CPEX dataset consists of ice probability, wind speed, and wind direction estimates collected by the ASCAT. The ASCAT is onboard the MetOp-A and MetOp-B satellites and uses radar to measure the electromagnetic backscatter from the wind-roughened ocean surface, from which data on wind speed and direction can be derived. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017 through July 16, 2017 in netCDF-3 format. proprietary ascatcpex_1 Advanced Scatterometer (ASCAT) CPEX ALL STAC Catalog 2017-05-24 2017-07-16 160.241, 3.9062, -25.0958, 42.5176 https://cmr.earthdata.nasa.gov/search/concepts/C2428509185-GHRC_DAAC.umm_json The Advanced Scatterometer (ASCAT) CPEX dataset consists of ice probability, wind speed, and wind direction estimates collected by the ASCAT. The ASCAT is onboard the MetOp-A and MetOp-B satellites and uses radar to measure the electromagnetic backscatter from the wind-roughened ocean surface, from which data on wind speed and direction can be derived. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017 through July 16, 2017 in netCDF-3 format. proprietary +ascatcpex_1 Advanced Scatterometer (ASCAT) CPEX GHRC_DAAC STAC Catalog 2017-05-24 2017-07-16 160.241, 3.9062, -25.0958, 42.5176 https://cmr.earthdata.nasa.gov/search/concepts/C2428509185-GHRC_DAAC.umm_json The Advanced Scatterometer (ASCAT) CPEX dataset consists of ice probability, wind speed, and wind direction estimates collected by the ASCAT. The ASCAT is onboard the MetOp-A and MetOp-B satellites and uses radar to measure the electromagnetic backscatter from the wind-roughened ocean surface, from which data on wind speed and direction can be derived. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017 through July 16, 2017 in netCDF-3 format. proprietary asosimpacts_1 Automated Surface Observing System (ASOS) IMPACTS GHRC_DAAC STAC Catalog 2019-12-29 2023-03-01 -89.694, 36.571, -67.791, 47.467 https://cmr.earthdata.nasa.gov/search/concepts/C1995871063-GHRC_DAAC.umm_json The Automated Surface Observing Systems (ASOS) IMPACTS dataset consists of a variety of ground-based observations during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This ASOS dataset consists of 176 stations within the IMPACTS domain. Each station provides observations of surface temperature, dew point, precipitation, wind direction, wind speed, wind gust, sea level pressure, and the observed weather code. The ASOS data are available from December 29, 2019, through March 1, 2023, in netCDF-4 format. proprietary aspas_asmas_aat_3 Antarctic Specially Protected Areas and Antarctic Specially Managed Areas in the Australian Antarctic Territory - GIS polygon dataset. AU_AADC STAC Catalog 1998-01-01 2008-01-01 60.867, -72.967, 142.7, -66.217 https://cmr.earthdata.nasa.gov/search/concepts/C1457769795-AU_AADC.umm_json This record describes GIS polygon data (a shapefile) representing the boundaries of Antarctic Specially Protected Areas (ASPAs) and an Antarctic Specially Managed Area (ASMA) in the Australian Antarctic Territory for which Australia was the proponent or co-proponent. Also included is the boundary of ASPA 168 for which China was the proponent. The following is a list of the ASPAs and ASMA: ASPA 101 Taylor Rookery ASPA 102 Rookery Islands ASPA 103 Ardery Island and Odbert Island ASPA 135 North-east Bailey Peninsula ASPA 136 Clark Peninsula ASPA 143 Marine Plain ASPA 160 Frazier Islands ASPA 162 Mawson's Huts ASPA 164 Scullin and Murray Monoliths ASPA 167 Hawker Island ASPA 168 Mt Harding ASPA 169 Amanda Bay ASPA 174 Stornes ASMA 6 Larsemann Hills The data is available from a link in this metadata record and also, as a separate shapefile for each ASPA or ASMA, from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database (see related url). GIS data representing the boundaries of other ASPAs and ASMAs is also available from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database. proprietary asrb-dav_1.0 ASRB_DAV: Shortwave and longwave radiation measurements (2 min) in Davos Dorf ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.84827, 46.81277, 9.84827, 46.81277 https://cmr.earthdata.nasa.gov/search/concepts/C2789814851-ENVIDAT.umm_json Incoming and outgoing shortwave and longwave 2 min radiation measurements in Davos Dorf, CH. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf. proprietary asrb-vf_1.0 ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos ENVIDAT STAC Catalog 2016-01-01 2016-01-01 9.809204, 46.829631, 9.809204, 46.829631 https://cmr.earthdata.nasa.gov/search/concepts/C2789814947-ENVIDAT.umm_json Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf. proprietary asrb-wfj_1.0 ASRB_WFJ: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.809204, 46.829631, 9.809204, 46.829631 https://cmr.earthdata.nasa.gov/search/concepts/C2789814987-ENVIDAT.umm_json Corrected incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf. proprietary -aster_1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre AU_AADC STAC Catalog 2000-10-08 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included. proprietary aster_1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre ALL STAC Catalog 2000-10-08 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included. proprietary +aster_1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre AU_AADC STAC Catalog 2000-10-08 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included. proprietary aster_global_dem ASTER Global DEM USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567908-USGS_LTA.umm_json ASTER is capable of collecting in-track stereo using nadir- and aft-looking near infrared cameras. Since 2001, these stereo pairs have been used to produce single-scene (60- x 60-kilomenter (km)) digital elevation models (DEM) having vertical (root-mean-squared-error) accuracies generally between 10- and 25-meters (m). The methodology used by Japan's Sensor Information Laboratory Corporation (SILC) to produce the ASTER GDEM involves automated processing of the entire ASTER Level-1A archive. Stereo-correlation is used to produce over one million individual scene-based ASTER DEMs, to which cloud masking is applied to remove cloudy pixels. All cloud-screened DEMS are stacked and residual bad values and outliers are removed. Selected data are averaged to create final pixel values, and residual anomalies are corrected before partitioning the data into 1 degree (°) x 1° tiles. The ASTER GDEM covers land surfaces between 83°N and 83°S and is comprised of 22,702 tiles. Tiles that contain at least 0.01% land area are included. The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude). The data are posted on a 1 arc-second (approximately 30–m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid. proprietary atlas_buildings_gis_1 Differential GPS survey of the Atlas Cove ANARE Station ruins on Heard Island AU_AADC STAC Catalog 2000-01-01 2000-02-28 73.3, -53.1, 73.5, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313143-AU_AADC.umm_json Alistair Grinbergs (Heritage Officer) was on Heard island in January and February 2000) as part of the 2000 ANARE, to make an assessment of the heritage value of the old ANARE station ruins. This GPS survey data of the corners of buildings and other artefacts will form part of the record of the station site, together with drawings and other measurements. The assessment will be used to formulate a conservation management plan for the site. proprietary atlas_cove_photos_1 Atlas Cove Terrestrial Photos - historic ANARE Base AU_AADC STAC Catalog 2008-03-26 2008-03-26 73.391, -53.02, 73.394, -53.018 https://cmr.earthdata.nasa.gov/search/concepts/C1214313131-AU_AADC.umm_json Photographs and photo locations of the historic Australian National Antarctic Research Expedition (ANARE) base at Atlas Cove on Heard Island. The station was established 11 December 1947 and was closed down on 9 March 1955. Photos were taken in March of 2008 by Kerry Steinberner during a visit to Heard Island. The map used to locate the images is described in the following metadata record: Topographic Survey at Atlas Cove, Heard Island, November 2000 [atlas_survey2000_gis] The images include shots of the remains of ANARE buildings, vehicles, tanks, debris, fences, artefacts and flora. The dataset includes a copy of the images, an excel spreadsheet cataloguing the images, and shapefiles showing the image locations. proprietary @@ -17406,8 +17474,8 @@ atree-forest-owner-clearances-offsetting_1.0 ATREE forest owners survey about fo atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0 ATREE forest owners survey about climate regulation services of forests ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814546-ENVIDAT.umm_json Forest owners of the Canton of Lucerne were survey about their willingness to employ different forest management measures to provicde climate regulation services by forests. Of the nearly 3000 forest owners that received an invitation to a online-survey and the 900 forest owners that received a paper and pencil survey, 1055 valid responses were received. The questionnaire contained a survey experiment in which 9 choice situations were presented to the respondents in which they had the choice between two options and the status quo. This survey experiment part of the survey was completed by 990 respondents. proprietary atree-q-methodology-forest-clearances-offsetting_1.0 ATREE Q-methodology statement sorts on forest clearances offsetting in the forest ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814556-ENVIDAT.umm_json "In Novdember 2019 about 19 experts on forest surface protection and forest clearances were invited to a workshop in order to discuss policy design and implementation problems regarding the offsetting of forest clearances. In Switzerland such offsetting can be provided under certain circumstances by implementing forest nature conservation measures in the forest instead of providing in-kind compensation, i.e. reafforestation on agricultural land. The workshop included the sorting of 34 statements – that were elaborated beforehand, partially also with help of the participants – according to the ""Q-methodology"" survey technique (participants arrange given statements about a certain subject into boxes that are normally distributed over a ""agree - do not agree"" answer scale). The participants included representatives from cantonal and national forest administrations, nature conservation NGOs, forest NGOs, spatial planning NGOs, private counseling enterprises as well as national, cantonal and regional forest owner organizations. The data allows a factor analytical differentiation of actors into groups with distinct positions towards forest clearance compensation as well as a positioning of these groups relative to each statement." proprietary atree-social-network-analysis-carbon-sequestration-lucerne_1.0 ATREE Social Network Analysis survey on policy options regarding CO2 mitigation and sequestration in wood and forest ENVIDAT STAC Catalog 2022-01-01 2022-01-01 8.0859375, 46.9348859, 8.470459, 47.2191951 https://cmr.earthdata.nasa.gov/search/concepts/C2789814569-ENVIDAT.umm_json "In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the ""actor coalition framework"" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations." proprietary -atrs Airborne Coherant Radar Sounding Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json "Developmental airborne coherent radar sounding data collected over a variety of sounding targets in Antarctica, including a full gridded survey of subglacial Lake Vostok and its environs. This was an instrument development award, so the data are not of ""production"" quality. Receiver sensitivity documents are provided with the data. The data resides in 6, DLT 4 tapes (~30 Gb each)." proprietary atrs Airborne Coherant Radar Sounding Data ALL STAC Catalog 1970-01-01 -180, -90, 180, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json "Developmental airborne coherent radar sounding data collected over a variety of sounding targets in Antarctica, including a full gridded survey of subglacial Lake Vostok and its environs. This was an instrument development award, so the data are not of ""production"" quality. Receiver sensitivity documents are provided with the data. The data resides in 6, DLT 4 tapes (~30 Gb each)." proprietary +atrs Airborne Coherant Radar Sounding Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json "Developmental airborne coherent radar sounding data collected over a variety of sounding targets in Antarctica, including a full gridded survey of subglacial Lake Vostok and its environs. This was an instrument development award, so the data are not of ""production"" quality. Receiver sensitivity documents are provided with the data. The data resides in 6, DLT 4 tapes (~30 Gb each)." proprietary au0103_1 Aurora Australis marine science cruise au0103 (CLIVAR_SR3) - CTD and ADCP data AU_AADC STAC Catalog 2001-10-29 2002-12-13 139, -68, 148, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214306658-AU_AADC.umm_json Oceanographic measurements were conducted along CLIVAR Southern Ocean meridional repeat transect SR3 between Tasmania and Antarctica from October to December 2001. A total of 135 CTD vertical profile stations were taken, more than half to within 20 m of the bottom. Over 2200 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, CFC's, CCl4, dissolved inorganic carbon, alkalinity, 13C, DMS/DMSP/DMSO, halocarbons, barium, barite, ammonia, del30Si, dissolved and particulate organic carbon, particulate silica, 15N-nitrate, 18O, 234Th, 230Th, 231Pa, primary productivity and biological parameters, using a 24 bottle rosette sampler. Near surface current data were collected using a ship mounted ADCP. Two sediment trap moorings were serviced, and a third mooring was deployed at a new location. A summary of all CTD data and data quality is presented in the data report. This work was completed as part of ASAC project 1335. proprietary au0106_1 Aurora Australis Southern Ocean oceanographic data, voyage 6, 2000-2001 - KACTAS AU_AADC STAC Catalog 2001-01-01 2001-03-09 61.875, -68.26939, 148.11719, -43.61071 https://cmr.earthdata.nasa.gov/search/concepts/C1709216539-AU_AADC.umm_json Oceanographic measurements conducted on voyage 6 of the Aurora Australis of the 2000-2001 season. These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data. These data were collected by Mark Rosenberg. This metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise. Basic information about voyage 6: The voyage will complete a range of Marine Science activities off the Mawson Coast, and off the Amery Ice Shelf before calling at Davis to retrieve summer personnel and helicopters prior to returning to Hobart. Science equipment calibration will be undertaken at Mawson. (Marine Science activities were interrupted when the Aurora Australis was required to provide assistance in the Polar Bird's attempt to reach Casey, complete the station resupply and return to open water.) Leader: Dr Graham Hosie Deputy Leader: Mr Andrew McEldowney See the readme files in the downloads for more information. proprietary au0201_1 Aurora Australis Southern Ocean oceanographic data, voyage 1, 2002-2003 - ADCP data AU_AADC STAC Catalog 2002-10-13 2002-11-18 137.6, -66.6, 159.1, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1834759929-AU_AADC.umm_json "Oceanographic measurements conducted on voyage 7 of the Aurora Australis of the 2002-2003 season. These data are ADCP (Acoustic Doppler Current Profiler) data. These data were collected/collated by Mark Rosenberg. Final ADCP data for voyage au0201 (SAZ mooring turnaround and iceberg B9B experiment), Aurora Australis Voyage 1 2002/2003, 17th Oct 2002 to 18th Nov 2002. * The complete ADCP data for cruise au0201 are in the file: au020101.cny (ascii format) a0201dop.mat (matlab format) * The ""on station"" ADCP data (specifically, the data for which the ship speed was less than or equal to 0.35 m/s) are in the files: au0201_slow35.cny (ascii format) a0201dop_slow35.mat (matlab format) * The file bindep.dat shows the water depths (in metres) that correspond to the centre of each vertical bin. * The data are 30 minute averages. Each 30 minute averageing period starts from the time indicated. (so, e.g., an ensemble with time 120000 is the average from 120000 to 123000). * ADCP currents are absolute - i.e. ship's motion has been subtracted out. * Note that the top few bins can have bad data from water dragged along by the ship. * Beware of data when the ship is underway - it's often suspect. MATLAB VECTORS AND MATRICES: ============================ header info ----------- for header info: column number corresponds to 30 minute average number botd = mean bottom depth (m) over the 30 minute period cnav = GPS info: don't worry about it cruise = cruise number date = ddmmyy (UTC) ibcover = a bottom track parameter: don't worry about it icover = percentage of 30 minute averageing period covered by acceptable 3 minute ensembles lastgd = deepest accepted bin in this profile lat = mean latitude over the 30 minute period (decimal degrees) lon = mean longitude over the 30 minute period (decimal degrees) nbins = no. of bins logged (=60) shipspeed = scalar resultant of shipu and shipv shipu = ship's E/W velocity over the ground over 30 minute period (m/s, +ve east) shipv = ship's N/S velocity over the ground over 30 minute period (m/s, +ve north) time = hhmmss, time (UTC) at start of 30 minute averageing period dectime = time in decimal days from start of year 2002 (e.g. midday on January 2nd = 1.5000) adcp data --------- for adcp data matrices: row number corresponds to bin number column number corresponds to 30 min. average no. bindep = depth (m) to centre of each bin in the profile (will be the same for all profiles) ipcok = percentage of the profile period for which there was good data in this bin (N.B. data=NaN when ipcok=0) qc = a quality control value for each bin - see below speed = scalar resultant of u and v u = east/west current (m/s, +ve east) v = north/south current (m/s, +ve north) ASCII FORMAT FILE: ================== * The file starts with a 3 line header. * Then comes each 30 min. ensemble, as follows: First, a 1 line header, containing date (UTC) (dd-mmm-yyyy) time (UTC) (hh:mm:ss) % of 30 min average covered by acceptable 3 min. ensembles deepest accepted bin in the profile ship's E/W velocity over the ground over the 30min (m/s) ship's N/S velocity over the ground over the 30min (m/s) P= GPS position-derived velocity (D=direct GPS vel.; B=bottom track vel.) mean longitude over the 30 min. mean latitude over the 30 min. % of interfix period for which there was bottom depth information mean bottom depth over the 30 min. 0 0 Next, the data, from the shallowest bin to the deepest bin: for each bin, there's 4 parameters: u = east/west current (m/s, +ve east) v = north/south current (m/s, +ve north) qc = quality control value - see below ipcok = percentage of the profile period for which there was good data in this bin Note that the data are written left to right across each line, then onto the next line, etc. (so 4 bins on a full line) quality control value: ---------------------- qc = %good / (Verr+0.05) where: %good = percent good pings after logging system screening Verr = RMS error velocity (m/s). Possible range of qc is 0-20, with an expected range of 0-10; values of 0-4 indicate very poor data; values above 8 indicate very good data." proprietary @@ -17443,8 +17511,8 @@ avalanche-fatalities-european-alps-1969-2015_1.0 Avalanche fatalities in the Eur avalanche-fatalities-per-calendar-year-since-1936_1.0 Number of avalanche fatalities per calendar year in Switzerland since 1937 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814645-ENVIDAT.umm_json Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset. If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936] The following information is contained (by column and column title): - year - number of fatalities in the backcountry (=tour) - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs) - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors) - number of fatalities in or around buildings or in settlements (= buildings) - sum (of all four categories) The definitions for these four categories, as described in the guidelines to the avalanche accident database are: __tour:__ activities include back-country ski, snowboard or snow-shoe touring __offpiste:__ access from ski area, generally from the top of a skilift with short hiking distances __transportation.corridors__ (Techel et al., 2016): people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams) __buildings__ (Techel et al., 2016): people inside or just outside buildings, and workers on high alpine building sites proprietary avalanche-fatalities-switzerland-1936_1.0 Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814658-ENVIDAT.umm_json Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset. The following information is contained (by column and column title): - hydrological year - number of fatalities in the backcountry (=tour) - number of fatalities in terrain close to ski areas (=offpiste) - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors) - number of fatalities in or around buildings or in settlements (= buildings) - sum (of all four categories) The definition for these four categories as described in the guidelines to the avalanche accident database: **tour**: activities include back-country ski, snowboard or snow-shoe touring **offpiste**: access from ski area, generally from the top of a skilift with short hiking distances **transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )): people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams) **buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )): people inside or just outside buildings, and workers on high alpine building sites proprietary avalanche-prediction-snowpack-simulations_1.0 Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081494-ENVIDAT.umm_json The data set contained in this repository was used in the analysis by Mayer et al. (2023): Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023. proprietary -avapsimpacts_1 Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS ALL STAC Catalog 2020-01-12 2023-02-28 -77.815, 33.54, -65.44, 44.17 https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format. proprietary avapsimpacts_1 Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS GHRC_DAAC STAC Catalog 2020-01-12 2023-02-28 -77.815, 33.54, -65.44, 44.17 https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format. proprietary +avapsimpacts_1 Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS ALL STAC Catalog 2020-01-12 2023-02-28 -77.815, 33.54, -65.44, 44.17 https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format. proprietary avhrr_822_1 SAFARI 2000 AVHRR Daily Site (1.5 km) and 15-Day Regional (1.5- and 6-km) Imagery ORNL_CLOUD STAC Catalog 1998-07-01 2000-10-31 8.73, -35.26, 43.2, -7.49 https://cmr.earthdata.nasa.gov/search/concepts/C2804805089-ORNL_CLOUD.umm_json The Global Inventory Mapping and Modeling (GIMMS) group at NASA/GSFC provided SAFARI 2000 with remotely sensed satellite data products at the site and regional level. These AVHRR data contain two main sets of data: site extracts of SAFARI core sites (Mongu, Etosha, Kasungu, Maun, Skukuza, and Tshane), and regional 15-day composites from sets of single-day images. These AVHRR data contain four main sets of data:1.5 km daily site extracts of SAFARI core sites (2000)1.5 km 15-day composites of SAFARI core sites (1998-2000)1.5 km 15-day composites of the southern African region (Mar, Sept 2000)6 km 15-day composites of the southern African region (1998-2000)The primary data layers for site extracts and regional composites are fire pixel counts and maximum NDVI. The fire product is different for the daily and for the composited products (see readme file) and a fire product is not included in the 1.5 km regional data set. NDVI composite-associated data layers for the regional data sets include land surface temperature, reflectance, solar zenith angle, view zenith angle, and relative azimuth angle. NDVI composite-associated data layers for the site extracts include these same variables as well as brightness temperature, fire mask composite, latitude, and longitude. The data are stored in binary image format files. There is a metadata file for each site and date/compositing period, in ASCII format. proprietary avhrr_albedo_1995_xdeg_928_1 ISLSCP II AVHRR Albedo and BRDF, 1995 ORNL_CLOUD STAC Catalog 1995-02-01 1995-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784840966-ORNL_CLOUD.umm_json This Albedo and BRDF (Bidirectional Reflectance Distribution Function) data set contains three files containing BRDF parameters, white- sky albedo and black-sky albedo at solar noon for three bands ((350-680nm, 680-3000nm, and 350-30000nm)derived from AVHRR (Advanced Very High Resolution Radiometer). These data are available at spatial resolutions of quarter, half, and one degree. Black-sky albedo (direct beam contribution) and white-sky (Completely diffuse contribution) can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon. proprietary avhrrl3b_481_1 BOREAS Level-3b AVHRR-LAC Imagery: Scaled At-Sensor Radiance in LGSOWG Format ORNL_CLOUD STAC Catalog 1994-01-30 1996-09-18 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2929133860-ORNL_CLOUD.umm_json Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived. A few winter acquisitions are available, but the archive contains primarily growing season imagery. These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996. Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region. proprietary @@ -17532,8 +17600,8 @@ brok_5k_gis_1 Broknes Peninsula 1:5000 Topographic GIS Dataset AU_AADC STAC Cata broknes_lake_catchments_gis_1 Lake catchments on Broknes, Larsemann Hills AU_AADC STAC Catalog 1997-05-06 2001-08-14 76.285, -69.4193, 76.42, -69.3698 https://cmr.earthdata.nasa.gov/search/concepts/C1214313378-AU_AADC.umm_json Catchment boundaries of the the lakes on Broknes, Larsemann Hills. These catchments were generated using the FLOWDIRECTION and BASINS routines in the GRID module of ArcInfo GIS. proprietary bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 SCIOPS STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 ALL STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary -brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary +brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary bryophyte-observer-bias_1.0 Greater observer expertise leads to higher estimates of bryophyte species richness ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081769-ENVIDAT.umm_json This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relevés conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise. Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon NA, Bergamini A (2024) Greater observer expertise leads to higher estimates of bryophyte species richness. _Journal of Vegetation Science_. (submitted) proprietary bunger_east_sat_1 Bunger Hills East Satellite Image Map 1:50 000 AU_AADC STAC Catalog 1992-06-01 1992-06-30 101, -66, 102, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313379-AU_AADC.umm_json Satellite image map of Bunger Hills East/Wilkes Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG Commercial (now Geoscience Australia), in Australia, in 1992. The map is at a scale of 1:50000, and was produced from four multispectral space imagery SPOT 1 scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary bunger_geology_gis_1 Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset AU_AADC STAC Catalog 1980-01-01 1997-12-31 98, -67.5, 102, -65.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313380-AU_AADC.umm_json Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset. For additional information, see the published map 'Bunger Hills - Denman Glacier Bedrock Geology', published in 1994, and available at the provided URL. proprietary @@ -17603,8 +17671,8 @@ calishto_1.0 CALISHTO campaign dataset for the publication Biological and Dust A canopychem_422_1 Seedling Canopy Chemistry, 1992-1993 (ACCP) ORNL_CLOUD STAC Catalog 1992-11-06 1993-03-15 -122.05, 37.4, -122.05, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776831590-ORNL_CLOUD.umm_json The nitrogen and chlorophyll concentrations of constructed Douglas-fir and bigleaf maple seedling canopies were determined. Canopy reflectance spectra were measured before foliage samples were collected. proprietary canopyspec_423_1 Seedling Canopy Reflectance Spectra, 1992-1993 (ACCP) ORNL_CLOUD STAC Catalog 1992-11-06 1993-03-15 -122.05, 37.4, -122.05, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776849767-ORNL_CLOUD.umm_json The reflectance spectra of Douglas-fir and bigleaf maple seedling canopies were measured. Canopies varied in fertilizer treatment and leaf area density respectively. proprietary capeden_management_gis_1 Cape Denison Management Zone GIS Dataset AU_AADC STAC Catalog 2004-01-01 2004-12-31 142.651, -67.014, 142.691, -67.003 https://cmr.earthdata.nasa.gov/search/concepts/C1214313393-AU_AADC.umm_json This GIS dataset is comprised of the boundary of the Visual Protection Zone at Cape Denison, Antarctica. The data were created for the Management Plan for Historic Site and Monument No 77 and Antarctic Specially Managed Area (ASMA) No 3 produced by the Australian Antarctic Division in 2004. The data are formatted according to the SCAR Feature Catalogue and are available for download (see Related URLS). proprietary -capeden_sat_ikonos_1 A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001 AU_AADC STAC Catalog 2001-01-26 2001-01-31 142.5153, -67.0697, 143.03, -66.9478 https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json The following was done by a contractor for the Australian Antarctic Division: A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and (ii) georeferenced. See the Quality section for details about the satellite images and the georeferencing. The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details. Three satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details. proprietary capeden_sat_ikonos_1 A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001 ALL STAC Catalog 2001-01-26 2001-01-31 142.5153, -67.0697, 143.03, -66.9478 https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json The following was done by a contractor for the Australian Antarctic Division: A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and (ii) georeferenced. See the Quality section for details about the satellite images and the georeferencing. The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details. Three satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details. proprietary +capeden_sat_ikonos_1 A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001 AU_AADC STAC Catalog 2001-01-26 2001-01-31 142.5153, -67.0697, 143.03, -66.9478 https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json The following was done by a contractor for the Australian Antarctic Division: A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and (ii) georeferenced. See the Quality section for details about the satellite images and the georeferencing. The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details. Three satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details. proprietary capillary-rise-rise-experiments-in-snow-using-neutron-radiography_1.0 Capillary rise rise experiments in snow using neutron radiography ENVIDAT STAC Catalog 2025-01-01 2025-01-01 8.219919, 47.5334, 8.224511, 47.538557 https://cmr.earthdata.nasa.gov/search/concepts/C3383774625-ENVIDAT.umm_json This dataset consists of data related to capillary rise experiments performed with neutron radiography. There are 4 videos of capillary rise experiments as well as the files used to perform the inverse fitting with Hydrus. The videos show the upward flow of water in glass columns filled with sand and snow or sand, gravel, and snow. The videos show the 2D evolution of the unitless optical density with time. The Hydrus files were used to fit the parameter values of the Mualem-van Genuchten model. The experiments were performed at the Paul Scherrer Institute (PSI) in Villigen, Switzerland. proprietary carabid-beetles-in-forests_2.0 Carabid beetles in forests ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814572-ENVIDAT.umm_json Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types. Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG. Data are provided on request to contact person against bilateral agreement. proprietary case-study-applications-demonstrating-the-use-and-potential-of-the-treemig-frame_1.0 Case study applications demonstrating the use and potential of the TreeMig framework v1 ENVIDAT STAC Catalog 2024-01-01 2024-01-01 8.448486, 46.946512, 9.146118, 47.435519 https://cmr.earthdata.nasa.gov/search/concepts/C3383774663-ENVIDAT.umm_json The [TreeMig framework](https://treemig.wsl.ch/en/) allows for an easy application of the forest landscape model TreeMig for simulating forest dynamics in space under changing environmental and land use conditions. Here, case study examples in Switzerland are given that simulate the dynamics and spatial spread of competing tree species in a region in Switzerland, the invasion of a hypothetical invasive tree species, and the control of this species via a management model coupled to TreeMig. The datasets consist of the installation- and GUI starting-script, example environmental input data for Switzerland, the simulation environment, and further R-scripts for running the simulations and plotting the simulation results directly from R and for coupling TreeMig to the forest management model. proprietary @@ -17657,8 +17725,8 @@ climate_iceberg_1 Antarctic CRC and Australian Antarctic Division Climate Data S climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure ALL STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure AU_AADC STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climate_sea_ice_1 Antarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice AU_AADC STAC Catalog 1973-01-18 1996-12-19 -180, -80, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214313423-AU_AADC.umm_json This dataset contains the digitisation of one U.S. Navy/NOAA Joint Ice Facility sea ice extent and concentration map monthly to give the latitude and longitude of the northern extent of the Antarctic sea ice. Maps were produced weekly, but have been digitised monthly, since distribution began in January 1973 (except August 1985), until December 1996. Maps were digitised at each 10 degrees of longitude, and the longitude, distance from the south pole to the northern edge of the sea ice at that longitude, and latitude of that edge is given, as well as the mean distance and latitude for that map. Summary tabulations (sea ice northern extent latitudes at each 10 degree of longitude each year, grouped by month) and mean monthly sea ice extent statistics are also available. proprietary -climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures ALL STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures AU_AADC STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary +climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures ALL STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climatological-snow-data-1998-2022-oshd_1.0 Climatological snow data since 1998, OSHD ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081762-ENVIDAT.umm_json This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model). proprietary climwat CLIMWAT, A Climatic Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283619-CEOS_EXTRA.umm_json CLIMWAT is a climatic database to be used in combination with the computer program CROPWAT and allows the ready calculation of crop water requirements, irrigation supply and irrigation scheduling for various crops for a range of climatological stations worldwide. The CLIMWAT database includes data from a total of 3262 meteorological stations from 144 countries. CLIMWAT is published as Irrigation and Drainage paper No 49 in 1994 and includes a Manual with description of the use of the database with CROPWAT The data are contained in five diskettes included in the publication and can be ordered as FAO Irrigation and Drainage Paper 49 through the FAO Sales and Marketing Group. [Summary provided by the FAO.] proprietary clm5-snow-gpp-evapo-switzerland_1.0 Multi-resolution CLM5 simulations across Switzerland ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383774658-ENVIDAT.umm_json This dataset contains Community Land Model 5 (CLM5) simulation output over the spatial extent of Switzerland at different resolutions and based on a range of input datasets. It further contains land-use surface data used for the CLM5-simulations. **Detailed description of the CLM5 simulation setup and the various input datasets can be found in the accompanying publication: https://doi.org/10.5194/egusphere-2023-1832.** # CLM5 simulation output This dataset includes gridded CLM5 simulations of snow depth, gross primary productivity (GPP) and evapotranspiration at different resolutions ( 1km, 0.25° and 0.5°) and based on a range of input datasets over the spatial extent of Switzerland (see folder *gridded_CLM5_simulations*). Additionally, point-scale CLM5 simulations of snow depth and snow-water-equivalent at 36 snow-station locations (see folder *point_scale_CLM5_simulations*) are included. Latitude, longitude and elevation for these station locations can be found in table A1 of the above-mentioned publication. All simulation output spans from 01/01/2015 - 31/12/2019. Included CLM5 simulation results are based on 3 different meteorological forcing datasets: * Clim_CRU: standard global dataset, we used the recent state-of-the-art standrd global dataset CRU-JRA (https://catalogue.ceda.ac.uk/uuid/aed8e269513f446fb1b5d2512bb387ad) * Clim_CRU*: ClimCRU upraded by downscaling temperature data using a temperature lapse rate of -6.5K/1000m and a high-resolution DEM * Clim_OSHD: highest level of detail, meteorological forcing generated according to methods developed by the Operational Snow Hydrological Service (OSHD), at 1km spatial and 1hour temporal resolution # Land-use surface data This dataset further includes forcing land surface datasets used for the CLM5 simulations at 1km, 0.25° and 0.5° resolution (see folder *surface_landuse_datasets*). For the 1km resolution both the standard global (LU_Gl) and the high-resolution dataset (LU_HR), which includes a higher level of detail and is based on a more up-to-date land use datase, are provided. More details on these two datasets can be found in the above-mentioned publication. proprietary @@ -17707,12 +17775,12 @@ daily-solute-and-isotope-of-stream-water-and-precipitation_1.0 Daily data of sol daily_precip_est_793_1 SAFARI 2000 Daily Rainfall Estimates, 0.1-Deg, Southern Africa, 1993-2001 ORNL_CLOUD STAC Catalog 1993-01-01 2001-12-31 10, -34, 50, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2789731186-ORNL_CLOUD.umm_json The Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km. This approach accounts for the limitations of both data types in estimating precipitation. Rainfall estimates are produced at the high spatial and temporal frequency of the IR data using rainfall information from the PMW data. An IR/rain rate relationship, variable in space and time, is derived from coincident observations of IR and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IR/rain rate relationship is then applied to IR imagery at full temporal resolution. The results presented here are the daily means of those derived rain rates at 0.1 degree spatial resolution.The rainfall data sets are flat binary images with no headers. They are compressed band sequential (bsq) files that contain all of the daily images for the given year. Each image is an array of 401 lines, each with 341 binary floating-point numbers, containing rainfall at 0.1 degree resolution for the area 10 to 50 degrees longitude and 0 to -34 degrees latitude. The number of band sequential images in each annual file and the associated dates can be found in the file MIRA_data_dates.csv. proprietary dalmolin_thurmodeling1_1.0 Data for: Understanding dominant controls on streamflow spatial variability to set-up a semi-distributed hydrological model: the case study of the Thur catchment ENVIDAT STAC Catalog 2020-01-01 2020-01-01 8.5830688, 47.1112614, 9.6377563, 47.6246779 https://cmr.earthdata.nasa.gov/search/concepts/C2789814894-ENVIDAT.umm_json This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre–alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability, and build a model that reflects them, we follow a two–stages approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgment to identify the most plausible cause-effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow related processes, and landscape features such as geology, produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in numerous regions around the globe. proprietary danger_descriptions_avalanche_bulletin_switzerland_1.0 How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.8886719, 45.7984239, 10.5908203, 47.6804285 https://cmr.earthdata.nasa.gov/search/concepts/C2789814949-ENVIDAT.umm_json The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020. proprietary -darling_sst_00 2000 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 2000-01-01 2000-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json 2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine proprietary darling_sst_00 2000 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 2000-01-01 2000-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json 2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine proprietary +darling_sst_00 2000 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 2000-01-01 2000-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json 2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine proprietary darling_sst_01 2001 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 2001-01-01 2001-04-20 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214612276-SCIOPS.umm_json 2001 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center Walpole, Maine. proprietary darling_sst_01 2001 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 2001-01-01 2001-04-20 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214612276-SCIOPS.umm_json 2001 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center Walpole, Maine. proprietary -darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine proprietary darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine proprietary +darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine proprietary data-amphibian-monitoring_1.0 Data from: Estimation of breeding probbability can make monitoring data more revealing: a case study of amphibians ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814986-ENVIDAT.umm_json "This dataset includes data from 15 native pond breeding species in Switzerland in addition to observations of any species within the Pelophylax genus of water frogs. 233 sites (obnr) sampled during the 2011-2016 round of the WBS survey, which are listed as the ""first"" round of surveys. Data are also provided at 73 sites which were resurveyed in 2017 or 2018 (""second"" surveyround). The data are filtered as described in Cruickshank et al. (2021) to remove data from surveys carried out after the final sighting of a species within a year, and before the first observation of the species within a year. Observational data are provided as one of 3 observation types; 1 denotes a survey where the species was not detected, 2 denotes surveys where the species was detected but no life stages indicating successful breeding (e.g. the presence of eggs or larvae) were observed. Observation type 3 denotes a survey where evidence of successful breeding was observed (i.e. eggs or larvae). Survey protocols and full descriptions of the data are provided in Cruickshank et al (2021)." proprietary data-analysis-toolkits_1.0 Data analysis toolkits ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814544-ENVIDAT.umm_json "These are condensed notes covering selected key points in data analysis and statistics. They were developed by James Kirchner for the course ""Analysis of Environmental Data"" at Berkeley in the 1990's and 2000's. They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education! License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license." proprietary data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0 Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs ENVIDAT STAC Catalog 2023-01-01 2023-01-01 8.9761734, 46.5670779, 8.9761734, 46.5670779 https://cmr.earthdata.nasa.gov/search/concepts/C3226081971-ENVIDAT.umm_json "## Summary * Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland * R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis ## Data The dataset included here is the ""natural"" reservoir inflow for the Luzzone reservoir. Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html). ## Codes R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels: * GEV (Generalized Extreme Value) * GPD and GPDd (Generalized Pareto Distribution including declustered version) * eGPD (extended Generalized Pareto Distribution) * MEV (Metastatistical Extreme Value)" proprietary @@ -17783,15 +17851,15 @@ distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0 Distributi diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0 Diversity of ground beetles and spiders as well as cynipid oak gall formation on irrigated and non-irrigated plots in a dry mixed Scots pine forest ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.6136971, 46.3021928, 7.6136971, 46.3021928 https://cmr.earthdata.nasa.gov/search/concepts/C2789814550-ENVIDAT.umm_json In the dry Pfynwald forest a long-term experiment of WSL was initiated in 2003 with a set of irrigated and non-irrigated plots. Forest Entomologie WSL made several investigations, one of them on the effect of irrigation (or conversely of drought) on the biodiversity of epigaeic arthropods such as ground beetles and spiders. In addition, its effects were also assessed by counting galls formed by gall wasps on pubescent oak. proprietary diversity_of_woody_species-36_1.0 Diversity of woody species ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814561-ENVIDAT.umm_json Index based on the number of tree and shrub species starting at 12 cm dbh in the upper layer and the occurrence of especially ecologically valuable tree and shrub species starting at 12 cm dbh in the upper layer. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary dlhimpacts_1 Diode Laser Hygrometer (DLH) IMPACTS GHRC_DAAC STAC Catalog 2023-01-13 2023-02-28 -95.243, 35.753, -67.878, 48.237 https://cmr.earthdata.nasa.gov/search/concepts/C3247876662-GHRC_DAAC.umm_json The Diode Laser Hygrometer (DLH) dataset is comprised of water vapor mixing ratio measurements as well as relative humidities (both concerning liquid water and ice) which are derived from the water vapor mixing ratio and ambient static temperature and pressure provided by the TAMMS instrument suite. These measurements were made using two separate DLH instruments installed on the NASA P-3B research aircraft, and the data from these instruments were combined to provide the best combination of accuracy, dynamic range, and data coverage. The two DLH instruments are (1) the zenith-mounted system which utilizes an optical path between the zenith port and the aircraft’s vertical tail, and (2) the short-path system, which utilizes an optical path between two fuselage-mounted fins. This dataset was measured during the 2023 campaign of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Earth Venture Suborbital 3 project. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The project aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The DLH data files are available for flights from January 13, 2023, through February 28, 2023, and are in the ASCII format. proprietary -doi:10.25921/sta3-3b95_Not Applicable 2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection NOAA_NCEI STAC Catalog 2014-09-08 2015-05-08 -84.4, 27.7, -83.4, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico. proprietary doi:10.25921/sta3-3b95_Not Applicable 2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection ALL STAC Catalog 2014-09-08 2015-05-08 -84.4, 27.7, -83.4, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico. proprietary +doi:10.25921/sta3-3b95_Not Applicable 2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection NOAA_NCEI STAC Catalog 2014-09-08 2015-05-08 -84.4, 27.7, -83.4, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico. proprietary doi:10.25921/v3a2-m248_Not Applicable Archival and Discovery of November 27, 1945 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1945-11-15 1945-12-01 66.97, 24.804, 66.97, 24.804 https://cmr.earthdata.nasa.gov/search/concepts/C2105865668-NOAA_NCEI.umm_json These water level data were digitized from a scanned marigram image associated with the tsunami event of 1945-11-27 at a tide gauge located at Karachi, Pakistan, and referenced to station datum. The Karachi marigram is one of the two instrumental records existing of the 1945 Makran tsunami and spans most of the 16 days between November 15 and December 1. The original Karachi analog record belongs to the Survey of India (SOI) and was collected and digitized by the National Institute of Oceanography (NIO) and Indian National Center for Ocean Information Services (INCOIS) for use in the publication of a few scientific papers. This digital marigram scan was reformatted into the accompanying digital, numerical time series by the Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO. Acknowledgement of SOI, NIO, and INCOIS should be included in any future scientific works using this record. proprietary doi:10.7289/V51R6NQJ_Not Applicable Archival and Discovery of May 22, 1960 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1960-05-18 1960-05-27 144.6539, 8.966667, -149.426667, 60.12 https://cmr.earthdata.nasa.gov/search/concepts/C2105865673-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary doi:10.7289/V54X564T_Not Applicable Archival and Discovery of May 16, 1968 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1968-05-13 1968-05-19 141, 13.4387, -124.18333, 41.745 https://cmr.earthdata.nasa.gov/search/concepts/C2105865675-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary doi:10.7289/V55H7DGQ_Not Applicable Archival and Discovery of November 4, 1952 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1952-10-29 1952-11-08 167.7383, -18.4758, -159.5916666, 54.317 https://cmr.earthdata.nasa.gov/search/concepts/C2105865672-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary doi:10.7289/V57H1GW8_Not Applicable Archival and Discovery of June 15, 1896 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1896-06-13 1896-06-21 -157.86667, 21.30667, -122.47834, 37.85 https://cmr.earthdata.nasa.gov/search/concepts/C2105865667-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary -doi:10.7289/V5862DPB_Not Applicable Airborne Magnetic Trackline Database ALL STAC Catalog 1958-12-06 2011-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107121616-NOAA_NCEI.umm_json The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center. proprietary doi:10.7289/V5862DPB_Not Applicable Airborne Magnetic Trackline Database NOAA_NCEI STAC Catalog 1958-12-06 2011-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107121616-NOAA_NCEI.umm_json The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center. proprietary +doi:10.7289/V5862DPB_Not Applicable Airborne Magnetic Trackline Database ALL STAC Catalog 1958-12-06 2011-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107121616-NOAA_NCEI.umm_json The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center. proprietary doi:10.7289/V598856F_Not Applicable Archival and Discovery of April 1, 1946 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1946-04-01 1946-04-04 145.583333, 35.017222, -123.3707, 48.424666 https://cmr.earthdata.nasa.gov/search/concepts/C2105865670-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary doi:10.7289/V5C827KJ_Not Applicable Archival and Discovery of August 27, 1883 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1883-08-24 1883-09-01 -157.86444, 21.30333, -122.47833, 57.7833 https://cmr.earthdata.nasa.gov/search/concepts/C2105865669-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary doi:10.7289/V5GX48VS_Not Applicable Archival and Discovery of December 23, 1854 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1854-12-21 1854-12-27 -122.4375, 32.70059, -117.22565, 37.69944 https://cmr.earthdata.nasa.gov/search/concepts/C2105865663-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary @@ -17939,8 +18007,8 @@ f0580e34da524770b0a5d43c033b33dc_NA ESA Soil Moisture Climate Change Initiative f1445bde2f1249c99bb5a59b71e9a9d7_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00 FEDEO STAC Catalog 1995-08-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327358896-FEDEO.umm_json This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary f17f146a31b14dfd960cde0874236ee5_NA ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1 FEDEO STAC Catalog 2002-05-31 2017-05-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142738-FEDEO.umm_json The dataset provides a Climate Data Record of Sea Ice Concentration (SIC) for the polar regions, derived from medium resolution passive microwave satellite data from the Advanced Microwave Scanning Radiometer series (AMSR-E and AMSR-2). It is processed with an algorithm using medium resolution (19 GHz and 37 GHz) imaging channels, and has been gridded at 25km grid spacing. This version of the product is v2.1, which is an extension of the v2.0 Sea_Ice_cci data and has identical data until 2015-12-25.This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project. The EUMETSAT OSI SAF contributed with access and re-use of part of its processing software and facilities.A SIC CDR at 50 km grid spacing is also available. proprietary f1ab07b5292f4813bd3090b51d270aa8_NA ESA Cloud Climate Change Initiative (Cloud_cci): MODIS-TERRA monthly gridded cloud properties, version 2.0 FEDEO STAC Catalog 2000-02-01 2014-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143056-FEDEO.umm_json The Cloud_cci MODIS-Terra dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MODIS (onboard Terra) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval system. The core cloud properties contained in the Cloud_cci MODIS-Terra dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures. proprietary -f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d 3 year daily average solar exposure map Mali 3Km GRAS September 2008-2011 ALL STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603974-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for September. proprietary f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d 3 year daily average solar exposure map Mali 3Km GRAS September 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603974-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for September. proprietary +f1b95e1fcf2df596f19f033fd766fa15b8f3ba5d 3 year daily average solar exposure map Mali 3Km GRAS September 2008-2011 ALL STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603974-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for September. proprietary f31e8e988c4144bebe13892b53d08e42_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the 79Fjord Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-06-24 2017-08-10 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142547-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the 79Fjord Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The data have been produced by S[&]T Norway proprietary f3865cc7-d9ce-43e5-802c-f115bcf8c67e_NA IRS-P6 Resourcesat-1 - Multispectral Images (LISS-IV) - Europe, Multispectral Mode FEDEO STAC Catalog 2004-01-29 2009-01-31 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458036-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 23.5 km x 23.5 km IRS LISS-IV multispectral data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary f428fffb26cf4cd5b97dfb6381cb16bb_NA ESA Ozone Climate Change Initiative (Ozone CCI): OSIRIS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2001-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143180-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for OSIRIS in 2008. proprietary @@ -17994,8 +18062,8 @@ field-based-edna-analysis-to-detect-the-threatened-and-elusive-african-manatee_1 field-observations-of-snow-instabilities_1.0 Field observations of snow instabilities ENVIDAT STAC Catalog 2021-01-01 2021-01-01 9.7084808, 46.6864249, 10.0174713, 46.8979737 https://cmr.earthdata.nasa.gov/search/concepts/C2789815084-ENVIDAT.umm_json This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019. These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height. The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file. These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021. ### Acknowlegements Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged. proprietary fieldsunp_65_1 Optical Thickness Data: Ground (OTTER) ORNL_CLOUD STAC Catalog 1990-02-22 1991-06-10 -123.95, 44.29, -121.33, 45.07 https://cmr.earthdata.nasa.gov/search/concepts/C2804770437-ORNL_CLOUD.umm_json Field sunphotometer data collected on 8/13-15/90 used to provide quantitative atmospheric correction to remotely sensed data of forest reflectance and radiance proprietary fieldwork_lawdome_1964_1 Field work results carried out on Law Dome and Wilkes Land, 1964 AU_AADC STAC Catalog 1964-01-01 1964-12-31 110, -70, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313469-AU_AADC.umm_json A collection of notes and field data collected in traverse work on Law Dome/Wilkes Land in 1964. Includes data on gravity, air pressure (barometric levelling), air temperature, wind, snow accumulation stakes, ice movement. Also includes results from S2 pit measurements. proprietary -fife_AF_dtrnd_nae_3_1 Aircraft Flux-Detrended: NRCC (FIFE) ALL STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968494372-ORNL_CLOUD.umm_json Detrended boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_dtrnd_nae_3_1 Aircraft Flux-Detrended: NRCC (FIFE) ORNL_CLOUD STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968494372-ORNL_CLOUD.umm_json Detrended boundary layer fluxes recorded on aircraft flights over the Konza proprietary +fife_AF_dtrnd_nae_3_1 Aircraft Flux-Detrended: NRCC (FIFE) ALL STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968494372-ORNL_CLOUD.umm_json Detrended boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_dtrnd_ncar_5_1 Aircraft Flux-Detrended: Univ. Col. (FIFE) ALL STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968514600-ORNL_CLOUD.umm_json Detrended boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_dtrnd_ncar_5_1 Aircraft Flux-Detrended: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968514600-ORNL_CLOUD.umm_json Detrended boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_dtrnd_wyo_4_1 Aircraft Flux-Detrended: U of Wy. (FIFE) ALL STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968504925-ORNL_CLOUD.umm_json Detrended boundary layer fluxes recorded on aircraft flights over the Konza proprietary @@ -18004,12 +18072,12 @@ fife_AF_filtr_nae_6_1 Aircraft Flux-Filtered: NRCC (FIFE) ORNL_CLOUD STAC Catalo fife_AF_filtr_nae_6_1 Aircraft Flux-Filtered: NRCC (FIFE) ALL STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968516479-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_filtr_ncar_8_1 Aircraft Flux-Filtered: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968522986-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_filtr_ncar_8_1 Aircraft Flux-Filtered: Univ. Col. (FIFE) ALL STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968522986-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary -fife_AF_filtr_wyo_7_1 Aircraft Flux-Filtered: U of Wy. (FIFE) ORNL_CLOUD STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968521064-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_filtr_wyo_7_1 Aircraft Flux-Filtered: U of Wy. (FIFE) ALL STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968521064-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary +fife_AF_filtr_wyo_7_1 Aircraft Flux-Filtered: U of Wy. (FIFE) ORNL_CLOUD STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968521064-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_raw_nae_9_1 Aircraft Flux-Raw: NRCC (FIFE) ORNL_CLOUD STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968531540-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_nae_9_1 Aircraft Flux-Raw: NRCC (FIFE) ALL STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968531540-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary -fife_AF_raw_ncar_11_1 Aircraft Flux-Raw: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968534531-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_ncar_11_1 Aircraft Flux-Raw: Univ. Col. (FIFE) ALL STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968534531-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary +fife_AF_raw_ncar_11_1 Aircraft Flux-Raw: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968534531-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_wyo_10_1 Aircraft Flux-Raw: U of Wy. (FIFE) ORNL_CLOUD STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968533497-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_wyo_10_1 Aircraft Flux-Raw: U of Wy. (FIFE) ALL STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968533497-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_atmos_brut_drv_14_1 Atmos. Profile: Std. Press. Level (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-08-12 -96.56, 39.12, -96.56, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2978502225-ORNL_CLOUD.umm_json Derived (5mb interval) radiosonde observations from Wilf Brutsaert's data proprietary @@ -18037,8 +18105,8 @@ fife_biology_soil_gas_106_1 Soil Gas Fluxes Using Soil Cores (FIFE) ORNL_CLOUD S fife_biology_veg_biop_135_1 Vegetation Biophysical Data (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-08-18 -96.61, 38.98, -96.45, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980707152-ORNL_CLOUD.umm_json Measurements of leaf area index and biomass of different canopy components proprietary fife_biology_veg_ref_137_1 Vegetation Species Reference (FIFE) ORNL_CLOUD STAC Catalog 1989-10-31 1989-10-31 -97, 39, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980719966-ORNL_CLOUD.umm_json LTER species names, codes, types, and other reference information proprietary fife_biology_veg_spec_136_1 Vegetation Species Data (FIFE) ORNL_CLOUD STAC Catalog 1984-05-07 1989-08-18 -96.61, 38.98, -96.45, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980708363-ORNL_CLOUD.umm_json Species composition data, by site and date proprietary -fife_hydrology_strm_15m_1_1 15 Minute Stream Flow Data: USGS (FIFE) ORNL_CLOUD STAC Catalog 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.umm_json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie proprietary fife_hydrology_strm_15m_1_1 15 Minute Stream Flow Data: USGS (FIFE) ALL STAC Catalog 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.umm_json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie proprietary +fife_hydrology_strm_15m_1_1 15 Minute Stream Flow Data: USGS (FIFE) ORNL_CLOUD STAC Catalog 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.umm_json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie proprietary fife_hydrology_strm_day_119_1 Stream Flow Daily Data: USGS (FIFE) ORNL_CLOUD STAC Catalog 1979-04-01 1988-09-02 -97, 39, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980681974-ORNL_CLOUD.umm_json USGS daily stream flow data for Kings Creek on the Konza Prairie proprietary fife_hydrology_strm_st_120_1 Stream Flow Storm Data (FIFE) ORNL_CLOUD STAC Catalog 1987-01-01 1988-01-01 -96.58, 39.07, -96.56, 39.09 https://cmr.earthdata.nasa.gov/search/concepts/C2980689463-ORNL_CLOUD.umm_json USGS stream flow during storm events around Kings Creek on the Konza Prairie proprietary fife_optical_ot_brug_62_1 Optical Thickness Data: Bruegge (FIFE) ORNL_CLOUD STAC Catalog 1987-05-30 1989-08-08 -96.62, 38.98, -96.54, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980489715-ORNL_CLOUD.umm_json Optical thickness data from Dr. Carol Bruegge, JPL proprietary @@ -18104,8 +18172,8 @@ fife_sur_refl_soilrefl_114_1 Soil Reflectance Data (FIFE) ORNL_CLOUD STAC Catalo fife_sur_refl_unl_long_49_1 Longwave Radiation Data: UNL (FIFE) ORNL_CLOUD STAC Catalog 1987-06-03 1989-08-11 -96.59, 38.98, -96.47, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980474531-ORNL_CLOUD.umm_json Average incoming longwave radiation measured by University of Nebraska proprietary fife_sur_refl_unl_surf_123_1 Surface Radiance Data: UNL (FIFE) ORNL_CLOUD STAC Catalog 1987-05-30 1989-08-11 -96.59, 38.98, -96.47, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980692342-ORNL_CLOUD.umm_json Canopy IR & air temperature, albedo, incoming and reflected shortwave, humidity proprietary figures-perspective-urban-beekeeping_1.0 Figures perspective urban beekeeping ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383775202-ENVIDAT.umm_json "Data and code from the perspective paper ""When honeybees comes to town"" The .r file provides the code to generate the figures. In addition, this repository contains the data for the figures 1-3. For Figure 4, part of the data is confidential, Please, refer to the contacts provided: - For the distribution of hives in Zurich (2018): https://www.zh.ch/de/gesundheitsdirektion/veterinaeramt.html - For the overheating map of Zurich: Prof. Dr. Eberhard Parlow / Stadt Zurich (https://www.zh.ch/de/umwelt-tiere/umweltschutz/umweltpraxis/definitionsseite/2012/68/zup068_2012_a0030_klimaanalyse-pdf.html)" proprietary -finnarp_aerosols Aerosol measurements at ABOA / FINNARP 2009 ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) proprietary finnarp_aerosols Aerosol measurements at ABOA / FINNARP 2009 SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) proprietary +finnarp_aerosols Aerosol measurements at ABOA / FINNARP 2009 ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) proprietary fire-randomizer-first-release_1.0 fire-randomizer: first release ENVIDAT STAC Catalog 2016-01-01 2016-01-01 8.4545978, 47.3606372, 8.4545978, 47.3606372 https://cmr.earthdata.nasa.gov/search/concepts/C3226082141-ENVIDAT.umm_json Tool to assess fire selectivity for topographic (e.g. alitiude, slope, aspect) or land use (forest or vegetation type, distance to infrastructures) categories with Monte Carlo simulations. proprietary fire-weather-index-from-1980-2099-derived-from-the-50-member-crcm5-le_1.0 Fire Weather Index for Hydrological Bavaria from 1980-2099 derived from the 50 member CRCM5-LE ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.449522, 47.497709, 13.985682, 49.488069 https://cmr.earthdata.nasa.gov/search/concepts/C3383775300-ENVIDAT.umm_json "This dataset contains the Fire Weather Index for Hydrological Bavaria from 1980 - 2099 as stated in the paper ""Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe"" published in Natural Hazards and Earth System Sciens (NHESS) 2023. The dataset contains daily Fire Weather Index values for all 50 members (subfolders of the dataset) of the CRCM5-LE (11 km spatial resolution) from 1980 to 2099 over the domain of Hydrological Bavaria. Please cite this dataset as the publication: Miller, J., Böhnisch, A., Ludwig, R., & Brunner, M. I. (2023). Climate change impacts on regional fire weather in heterogeneous landscapes of Central Europe. Natural Hazards and Earth System Sciences Discussions, 1-25. doi: 10.5194/nhess-2023-51." proprietary fire_emissions_724_1 SAFARI 2000 Fire Emission Data, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-14 2000-09-14 12, -27, 36, -14 https://cmr.earthdata.nasa.gov/search/concepts/C2788974415-ORNL_CLOUD.umm_json As part of the SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively. During the airborne campaign, stable and reactive trace gases were measured over southern Africa with an airborne Fourier transform infrared spectroscopy (AFTIR) onboard the University of Washington Convair-580 research aircraft in August-September of 2000. The measurements included vertical profiles of CO2, CO, H2O, and CH4 up to 5.5 km on 6 occasions above instrumented ground sites and below the TERRA satellite and ER-2 high-flying research aircraft as well as trace gas emissions from ten African savanna fires. These measurements are the first broad characterization of the most abundant trace gases in nascent smoke from African savanna fires (i.e., including oxygen- and nitrogen-containing species). proprietary @@ -18116,8 +18184,8 @@ flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0 F fltrepepoch_1 Flight Reports EPOCH GHRC_DAAC STAC Catalog 2017-07-27 2017-08-31 -130, 10, -80, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2175817241-GHRC_DAAC.umm_json The Flight Reports EPOCH dataset consists of flight number, purpose of flight, and flight hours logged during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The mission reports are available from July 27, 2017 through August 31, 2017 in PDF format. proprietary flu-a-bh_1.0 Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland ENVIDAT STAC Catalog 2016-01-01 2016-01-01 9.9451, 46.7479, 9.9451, 46.7479 https://cmr.earthdata.nasa.gov/search/concepts/C2789815125-ENVIDAT.umm_json Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied. proprietary fluxnet_point_1029_1 ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites ORNL_CLOUD STAC Catalog 1992-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785312311-ORNL_CLOUD.umm_json This International Satellite Land Surface Climatology Project (ISLSCP II) data set, ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites, contains gapp-filled flux and meterological data for half-hourly, daily, weekly, monthly, and annual time intervals presented for each site and year. The 1992-1995 Harvard Forest, MA site, and the 1994-95 Old Black Spruce, Alberta, Canada site are members of the FLUXNET global network of micrometeorological towers that use eddy covariance methods to measure the excahanges of carbon dioxide (CO2), water vapor, and energy between terrestrial ecosystem and atmosphere. proprietary -foraging_trip_duration_BI_1 Adelie penguin foraging trip duration, Bechervaise Island, Mawson ALL STAC Catalog 1991-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season. Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season. proprietary foraging_trip_duration_BI_1 Adelie penguin foraging trip duration, Bechervaise Island, Mawson AU_AADC STAC Catalog 1991-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season. Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season. proprietary +foraging_trip_duration_BI_1 Adelie penguin foraging trip duration, Bechervaise Island, Mawson ALL STAC Catalog 1991-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season. Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season. proprietary forclim_4.0 ForClim ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815136-ENVIDAT.umm_json "ForClim is a cohort-based model that was developed to analyze successional pathways of various forest types in Central Europe. Following the standard approach of gap models ForClim simulates the establishment; growth and mortality of trees on multiple independent patches (typically n = 200) in annual time steps to derive regional-scale stand dynamics. ForClim is currently parameterized for ca. 180 tree species dominant of temperate forests worldwide. The model has been tested comprehensively for the representation of natural forest dynamics of temperate forests of the Northern Hemisphere, with an emphasis on European forests. ForClim may be freely used under the terms of the ""GNU GENERAL PUBLIC LICENSE v3"" license. ![alt text](https://www.envidat.ch/dataset/a049e6ad-caac-492a-9771-90856c48ed03/resource/e1c9f03a-2e55-444b-afee-fa1f7f50dee0/download/forclim_4submodels.jpg ""ForClim structure"")" proprietary forecast-avalanche-danger-level-european-alps-2011-2015_1.0 Forecast avalanche danger level European Alps 2011 - 2015 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 4.8779297, 43.2761391, 16.2597656, 48.179762 https://cmr.earthdata.nasa.gov/search/concepts/C2789815158-ENVIDAT.umm_json This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci). For details on the data please refer to this publication. The dataset contains the following: - shape files for the warning regions in the Alps - highest forecast danger level for each warning region and day proprietary forecomon-proceedings_v14 Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7–9 June 2021, Birmensdorf, Switzerland ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4549183, 47.3607533, 8.4549183, 47.3607533 https://cmr.earthdata.nasa.gov/search/concepts/C2789815176-ENVIDAT.umm_json Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7-9 June 2021, WSL, Birmensdorf, Switzerland The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed. Copyright © 2021 by WSL, Birmensdorf The authors are responsible for the content of their contribution. proprietary @@ -18215,8 +18283,8 @@ geodata_0261 Groundwater Produced Internally CEOS_EXTRA STAC Catalog 1958-01-01 geodata_0271 Fishery Production - Marine CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848156-CEOS_EXTRA.umm_json TOTAL PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary geodata_0278 Exclusive Fishing Zone (EFZ) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848807-CEOS_EXTRA.umm_json The exclusive fishing zone or fishery zone refers to an area beyond the outer limit of the territorial sea (12 nautical miles from the coast) in which the coastal State has the right to fish, subject to any concessions which may be granted to foreign fishermen. Some countries have made no claim beyond the territorial sea. Some States have claimed an exclusive fishing zone instead of the more encompassing 200 nautical mile Exclusive Economic Zone (EEZ). The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil, and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). Some States have not ratified UNCLOS and many have not yet claimed their EEZ. Given the uncertainties surrounding much of the delimitation of the fishing zones, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ proprietary geodata_0279 Claimed Exclusive Economic Zone (EEZ) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848800-CEOS_EXTRA.umm_json "The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). In cases where a country's low-water lines is within 400 nautical miles of each other the EEZ boundaries are generally established by treaty, though there are many cases where these are in dispute. Under UNCLOS, ""land-locked and geographically disadvantaged States have the right to participate on an equitable basis in exploitation of an appropriate part of the surplus of the living resources of the EEZ's of coastal States of the same region or sub-region."" Some States have not ratified UNCLOS and many have not yet claimed their EEZ. These areas of unclaimed EEZ are the areas that a State has the right to claim under UNCLOS, but has not done so yet. Given the uncertainties surrounding much of the delimitation of the EEZ, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ " proprietary -geodata_0290 Administrative Boundaries - First Level (ESRI) CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary geodata_0290 Administrative Boundaries - First Level (ESRI) ALL STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary +geodata_0290 Administrative Boundaries - First Level (ESRI) CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary geodata_0291 Dams CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848575-CEOS_EXTRA.umm_json Construction of reservoirs became a worldwide activity in the second half of the twentieth century. The total storage capacity of the large reservoirs is more than 100 million cubic meters, which makes up more than 95% of water accumulated in all the reservoirs of the world. The total area of the more than 60,000 reservoirs that have been built in the last 50 years exceeds more than 100,000 square kilometers. This is an area equivalent to 11 water bodies the size of the Sea of Azov or five the size of Lake Superior. These man-made lakes affect natural and economic conditions over an area of 1.5 million square kilometers. Many of the world's large rivers, such as the Volga, Angara, Missouri, Colorado, and Parana Rivers, have been transformed into cascades of reservoirs. Construction and use of reservoirs cause inevitable changes in the environment, both positive and negative. Environmental changes can include overflowing and swamping; transformation of coasts; changes of soil, vegetation, and fauna; and changes of reproduction and habitat conditions of various aquatic organisms, especially fish and blue-green algae. The impact of reservoirs on the environment is diverse and contradictory. proprietary geodata_0295 Global Vegetation Index 1983-1990 CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848459-CEOS_EXTRA.umm_json "The NOAA/GVI (Global Vegetation Index; see reference pg. 3) Eight-Year Mean Maximum data set was developed in the following manner. First, eight years of NOAA/GVI Monthly Maximum data were obtained from GRID's Geneva archive of these data*. At GRID-Nairobi, an analyst then used these data files (12 per year) to calculate yearly mean maximum images, and the eight yearly mean images were averaged in their turn, in order to create a single eight-year mean maximum image. The original idea had been to produce an eight-year :hp2.maximum:ehp2. value image, but this was abandoned due to the accretion of ""noise"" from spurious maximum-value pixels in the individual data files (UNEP/GRID, 1990). * - GRID-Geneva has compiled an archive of NOAA/GVI Weekly data from the U.S. National Oceanic and Atmospheric Administration / National Environmental Satellite Data and Information Service / National Climate Data Center / Satellite Data Services Division (or the NOAA / NESDIS / NCDC / SDSD). This collection covers the period from April 1982 to present. At GRID-Geneva, the Weekly data are used to create Monthly, Seasonal and Annual Maximum images, in addition to the archived NOAA/GVI Weekly data. " proprietary geodata_0331 Agriculture Value Added - Percent of GDP ALL STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848745-CEOS_EXTRA.umm_json Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary @@ -18578,8 +18646,8 @@ goesrpltsolma_1 GOES-R PLT Southern Ontario Lightning Mapping Array (LMA) V1 GHR goesrpltwtlma_1 GOES-R PLT West Texas Lightning Mapping Array (LMA) V1 GHRC_DAAC STAC Catalog 2017-03-01 2017-06-01 -101.833, 33.597, -101.813, 33.617 https://cmr.earthdata.nasa.gov/search/concepts/C1977516629-GHRC_DAAC.umm_json The GOES-R PLT West Texas Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the West Texas LMA (WTXLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through June 1, 2017. proprietary goeswvt_1 GOES WATER VAPOR TRANSPORT V1 GHRC_DAAC STAC Catalog 1987-05-05 1988-11-30 -120, -30, -30, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1995554230-GHRC_DAAC.umm_json The GOES Water Vapor Transport CD contains nineteen months of geostationary satellite-derived products from the GOES-8 satellite spanning the 1987-1988 El Nino Southern Oscillation (ENSO) cycle. Water vapor transport variables was derived using the Marshall Automated Winds (MAW) tracking algorithm from GOES data are provided in daily and monthly gridded and non-gridded formats. Relative humidity was calculated using a modified version of the brightness temperature to relative humidity conversion technique. Pressure heights were assigned to each wind vector using the simple IR window technique. Data are available in binary and McIDAS format. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov proprietary gom_bathymetry Digital Bathymetric Data for the Gulf of Maine CEOS_EXTRA STAC Catalog 1970-01-01 -71.5, 39.5, -63, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231551983-CEOS_EXTRA.umm_json Gridded bathymetry and topography at 15 arc second (~1/2 km grid cell size) and a 30 arc second (~1 km grid cell size) resolution were constructed for the Gulf of Maine (Longitude = 71.5 - 63 W, Latitude = 39.5 - 46 N) using available digital bathymety datasets. In addition to the grids themselves, valuable ancillary products such as corrected sounding data, digital bathymetric contour lines and shaded-relief maps were generated and are available in a variety of formats, including Arc, Matlab, GMT and ASCII. See http://pubs.usgs.gov/of/1998/of98-801/ proprietary -gomc_156 Adopt-a-Tide Pool ALL STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary gomc_156 Adopt-a-Tide Pool SCIOPS STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary +gomc_156 Adopt-a-Tide Pool ALL STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary gomc_162 Circulation and Contaminant Transport in Massachusetts Coastal Waters CEOS_EXTRA STAC Catalog 1977-01-01 -70.95037, 42.09017, -70.26193, 42.61774 https://cmr.earthdata.nasa.gov/search/concepts/C2231548638-CEOS_EXTRA.umm_json U.S. Geological Survey studies show that the concentrations of metals in surface sediments of Boston Harbor are decreasing with time. This conclusion is supported by analysis of (1) surface sediments collected at monitoring stations in the outer harbor between 1977 and 1993, (2) sediment cores from depositional areas of the harbor, and (3) historical data from a contaminated-sediment data base, which includes information on metal and organic contaminants and sediment texture. During the 16 years of the continuing study, chromium, lead, mercury, silver, and zinc concentrations in surface sediments have decreased by about 50 percent. Although these trends are indeed encouraging, concentrations of some metals in harbor sediments are still above levels considered toxic to certain bottom-dwelling organisms. Type: Bay Waterbody or Watershed Names: Boston Harbor proprietary gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program SCIOPS STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program ALL STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary @@ -18594,8 +18662,8 @@ gov.noaa.ncdc:C01381_Not Applicable AVHRR/HIRS Longwave Radiation Budget Data (R gov.noaa.ncdc:C01560_V3 Blended Global Biomass Burning Emissions Product - Extended (GBBEPx) from Multiple Satellites NOAA_NCEI STAC Catalog 2018-01-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107094570-NOAA_NCEI.umm_json The Blended Global Biomass Burning Emissions Product version 3 (GBBEPx V3) system produces global biomass burning emissions. The product contains daily global biomass burning emissions (PM2.5, BC, CO, CO2, OC, and SO2) blended fire observations from MODIS Quick Fire Emission Dataset (QFED), VIIRS (NPP and JPSS-1) fire emissions, and Global Biomass Burning Emission Product from Geostationary satellites (GBBEP-Geo), which are in a grid cell of 0.25 × 0.3125 degree and 0.1 x 0.1 degree. It also produces hourly emissions from geostationary satellites, which is at individual fire pixels. The product output also include fire detection record in a HMS format, quality flag in biomass burning emissions, spatial pattern of PM2.5 emissions, and statistic PM2.5 information at continental scale. In Version3, daily biomass burning emissions at a FV3 C384 grid in binary format and daily biomass burning emissions at a 0.1 x 0.1 degree grid that include all the emissions species are added as new output. proprietary gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary -gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary +gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ngdc.mgg.photos:12_Not Applicable April 1906 San Francisco, USA Images NOAA_NCEI STAC Catalog 1906-04-18 1906-04-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705777-NOAA_NCEI.umm_json The 1906 San Francisco earthquake was the largest event (magnitude 8.3) to occur in the conterminous United States in the 20th Century. Recent estimates indicate that as many as 3,000 people lost their lives in the earthquake and ensuing fire. In terms of 1906 dollars, the total property damage amounted to about $24 million from the earthquake and $350 million from the fire. The fire destroyed 28,000 buildings in a 520-block area of San Francisco. proprietary gov.noaa.ngdc.mgg.photos:16_Not Applicable April 1992 Cape Mendocino, USA Images NOAA_NCEI STAC Catalog 1992-04-25 1992-04-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705735-NOAA_NCEI.umm_json On April 25, 1992 at 11:06 am local time (April 25 at 18:06 GMT), a magnitude 7.1 earthquake occurred in the Cape Mendocino area. Two additional earthquakes, magnitudes 6.6 and 6.7 occurred the next morning (April 26 at 00:41 and 04:18 am local time). The first earthquake was located six miles north of Petrolia, California, in a sparsely populated part of southwestern Humboldt County. Five small communities were located within a 50-mile radius of these events: Honeydew, Petrolia, Rio Dell, Scotia, and Ferndale. proprietary gov.noaa.ngdc.mgg.photos:1_Not Applicable August 1959 Hebgen Lake, USA Images NOAA_NCEI STAC Catalog 1959-08-18 1959-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705741-NOAA_NCEI.umm_json The magnitude 7.1 earthquake killed 28 people and caused $11 million property damage. Affected area: 1,554,000 sq km proprietary @@ -18613,8 +18681,8 @@ gov.noaa.ngdc.mgg.photos:52_Not Applicable April 2007 Solomon Islands, Papua New gov.noaa.nodc:0000015_Not Applicable Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015) NOAA_NCEI STAC Catalog 1958-01-15 1990-03-02 6.05, -70.233333, -47.033333, -26.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089372155-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000015_Not Applicable Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015) ALL STAC Catalog 1958-01-15 1990-03-02 6.05, -70.233333, -47.033333, -26.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089372155-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000028_Not Applicable Benthic species - TAXA counts, identities, and wet weights collected by sediment grab from multiple cruises in Prince William Sound, Alaska, from 10/22/1985 - 8/31/1988 (NCEI Accession 0000028) NOAA_NCEI STAC Catalog 1985-10-22 1998-08-31 -146.597, 61.0802, -146.2983, 61.13 https://cmr.earthdata.nasa.gov/search/concepts/C2089372272-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) NOAA_NCEI STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) ALL STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) NOAA_NCEI STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) NOAA_NCEI STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) ALL STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) NOAA_NCEI STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary @@ -18662,11 +18730,11 @@ gov.noaa.nodc:0000820_Not Applicable Bacteria Biomass and Chlorophyll-a depth pr gov.noaa.nodc:0000829_Not Applicable Broward County Florida thermographic data collected at twelve locations along four eastward lines that cross three offshore reef Tracks during the time period July 2000 to the present using self-recording temperature gauges (NCEI Accession 0000829) NOAA_NCEI STAC Catalog 2000-07-01 2002-11-30 -80.112007, 26.020458, -80.077343, 26.159952 https://cmr.earthdata.nasa.gov/search/concepts/C2089373393-NOAA_NCEI.umm_json "Broward County Florida has responsibility for the resource management of coral reefs in marine waters adjacent to Broward County. The Department of Planning and Environmental Protection is assigned the duties of monitoring the health of the coral reefs. Environmental stresses are a limiting factor in the biomass and diversity, and maintaining these populations of coral species requires an understanding of the environmental factors. One of these factors is the water temperature. Visual surveys are conducted by divers, and the staff has implemented an environmental monitoring program with water temperature as the first measured parameter. The monitoring program is on a ""not to interfere basis"" using self-recording thermographs for data acquisition. The thermographs are placed along coral reef tracks located in three separate bands near the northern most extent of the natural range for corals. The raw data are captured from the recorder by means of a laptop computer using transfer and conversion software provided by the instrument's vendor. Upon return to the office, the raw data are transferred to separate files that are then loaded into spreadsheet files. Each spreadsheet file corresponds to a single location and only one instrument. Twelve spreadsheet files are updated every sixty days for the dynamic raw data; the static geographical information is stored in a separate spreadsheet file." proprietary gov.noaa.nodc:0000861_Not Applicable A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861) ALL STAC Catalog 1999-03-15 1999-04-22 -68.260333, -67.576667, -2.296667, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2089373502-NOAA_NCEI.umm_json CTD and chemical data were collected using CTD and bottle casts in the Drake Passage and Scotia Sea from the JAMES CLARK ROSS. Data were collected from 15 March 1999 to 22 April 1999. Data were collected and submitted by the University of East Anglia with support of the Antarctic Large-scale Box Analysis and the Role of the Scotia Sea (ALBATROSS) project. proprietary gov.noaa.nodc:0000861_Not Applicable A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861) NOAA_NCEI STAC Catalog 1999-03-15 1999-04-22 -68.260333, -67.576667, -2.296667, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2089373502-NOAA_NCEI.umm_json CTD and chemical data were collected using CTD and bottle casts in the Drake Passage and Scotia Sea from the JAMES CLARK ROSS. Data were collected from 15 March 1999 to 22 April 1999. Data were collected and submitted by the University of East Anglia with support of the Antarctic Large-scale Box Analysis and the Role of the Scotia Sea (ALBATROSS) project. proprietary -gov.noaa.nodc:0000879_Not Applicable Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879) ALL STAC Catalog 2001-01-26 2001-05-18 -158.14, 19.27, -155.05, 21.37 https://cmr.earthdata.nasa.gov/search/concepts/C2089373608-NOAA_NCEI.umm_json Abundance data represent estimates of percent cover of species type (coral or algal) in 10 randomly placed quadrats along two 50 meter transect lines of each site. Data are available for 10 sites from Oahu to the Island of Hawaii taken in 2001 in support of the Macroalgal Ecology and Taxonomic Assessment (TEAM) Project. The species for abundance estimates include 11 corals, 5 invertebrates, 33 algals, and 2 benthic types (turf or sand). The role that marine algae play in a coral reef system is often overlooked because of lack of knowledge that they are the primary producers in the system. The coral reef ecosystem in Hawaii contains about ten times more algal species than coral species, some of them regulating space that permits coral recruitment. The primary purpose of the TEAM research program is to provide taxonomic and ecological algal expertise for the Coral Reef Monitoring and Assessment Program (CRAMP). Our group also seeks to develop, implement and assess new methodologies for quantitatively surveying benthic algal communities in the Hawaiian Islands. proprietary gov.noaa.nodc:0000879_Not Applicable Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879) NOAA_NCEI STAC Catalog 2001-01-26 2001-05-18 -158.14, 19.27, -155.05, 21.37 https://cmr.earthdata.nasa.gov/search/concepts/C2089373608-NOAA_NCEI.umm_json Abundance data represent estimates of percent cover of species type (coral or algal) in 10 randomly placed quadrats along two 50 meter transect lines of each site. Data are available for 10 sites from Oahu to the Island of Hawaii taken in 2001 in support of the Macroalgal Ecology and Taxonomic Assessment (TEAM) Project. The species for abundance estimates include 11 corals, 5 invertebrates, 33 algals, and 2 benthic types (turf or sand). The role that marine algae play in a coral reef system is often overlooked because of lack of knowledge that they are the primary producers in the system. The coral reef ecosystem in Hawaii contains about ten times more algal species than coral species, some of them regulating space that permits coral recruitment. The primary purpose of the TEAM research program is to provide taxonomic and ecological algal expertise for the Coral Reef Monitoring and Assessment Program (CRAMP). Our group also seeks to develop, implement and assess new methodologies for quantitatively surveying benthic algal communities in the Hawaiian Islands. proprietary +gov.noaa.nodc:0000879_Not Applicable Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879) ALL STAC Catalog 2001-01-26 2001-05-18 -158.14, 19.27, -155.05, 21.37 https://cmr.earthdata.nasa.gov/search/concepts/C2089373608-NOAA_NCEI.umm_json Abundance data represent estimates of percent cover of species type (coral or algal) in 10 randomly placed quadrats along two 50 meter transect lines of each site. Data are available for 10 sites from Oahu to the Island of Hawaii taken in 2001 in support of the Macroalgal Ecology and Taxonomic Assessment (TEAM) Project. The species for abundance estimates include 11 corals, 5 invertebrates, 33 algals, and 2 benthic types (turf or sand). The role that marine algae play in a coral reef system is often overlooked because of lack of knowledge that they are the primary producers in the system. The coral reef ecosystem in Hawaii contains about ten times more algal species than coral species, some of them regulating space that permits coral recruitment. The primary purpose of the TEAM research program is to provide taxonomic and ecological algal expertise for the Coral Reef Monitoring and Assessment Program (CRAMP). Our group also seeks to develop, implement and assess new methodologies for quantitatively surveying benthic algal communities in the Hawaiian Islands. proprietary gov.noaa.nodc:0000918_Not Applicable Chemical data from bottle casts in the Arctic Ocean and other Sea areas by the University of Alaska, from 16 April 1948 to 17 September 2000 (NCEI Accession 0000918) NOAA_NCEI STAC Catalog 1948-04-16 2000-09-17 -71, 16, -80.123, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089373877-NOAA_NCEI.umm_json Chemical data were collected using bottle casts from multiple vessels in the Arctic Ocean and other Sea areas from 16 April 1948 to 17 September 2000. Data were submitted by the University of Alaska in Fairbanks, Alaska. Chemical data include alkalinity, nitrate, nitrite, oxygen, silicate, and phosphate. proprietary -gov.noaa.nodc:0000931_Not Applicable Aerial surveys of ringed seals (Phoca hispida) on fast and pack ice in the central Beaufort Sea of Alaska, 1985-1987 and 1996-1999 (NCEI Accession 0000931) ALL STAC Catalog 1985-05-28 1999-06-04 -156.9983, 69.6517, -141.025, 71.865 https://cmr.earthdata.nasa.gov/search/concepts/C2089373928-NOAA_NCEI.umm_json These datasets include counts of ringed seals (Phoca hispida) and other marine mammals made during aerial surveys of ringed seals on fast and pack ice of the central Alaskan Beaufort Sea during 1985-1987 and 1996-1999. The datasets includes counts of seals, by group; designation of whether seals were at holes or along cracks; ice conditions including ice deformation and ice type (fast ice or pack ice); weather conditions; time of observations, and location of observations. proprietary gov.noaa.nodc:0000931_Not Applicable Aerial surveys of ringed seals (Phoca hispida) on fast and pack ice in the central Beaufort Sea of Alaska, 1985-1987 and 1996-1999 (NCEI Accession 0000931) NOAA_NCEI STAC Catalog 1985-05-28 1999-06-04 -156.9983, 69.6517, -141.025, 71.865 https://cmr.earthdata.nasa.gov/search/concepts/C2089373928-NOAA_NCEI.umm_json These datasets include counts of ringed seals (Phoca hispida) and other marine mammals made during aerial surveys of ringed seals on fast and pack ice of the central Alaskan Beaufort Sea during 1985-1987 and 1996-1999. The datasets includes counts of seals, by group; designation of whether seals were at holes or along cracks; ice conditions including ice deformation and ice type (fast ice or pack ice); weather conditions; time of observations, and location of observations. proprietary +gov.noaa.nodc:0000931_Not Applicable Aerial surveys of ringed seals (Phoca hispida) on fast and pack ice in the central Beaufort Sea of Alaska, 1985-1987 and 1996-1999 (NCEI Accession 0000931) ALL STAC Catalog 1985-05-28 1999-06-04 -156.9983, 69.6517, -141.025, 71.865 https://cmr.earthdata.nasa.gov/search/concepts/C2089373928-NOAA_NCEI.umm_json These datasets include counts of ringed seals (Phoca hispida) and other marine mammals made during aerial surveys of ringed seals on fast and pack ice of the central Alaskan Beaufort Sea during 1985-1987 and 1996-1999. The datasets includes counts of seals, by group; designation of whether seals were at holes or along cracks; ice conditions including ice deformation and ice type (fast ice or pack ice); weather conditions; time of observations, and location of observations. proprietary gov.noaa.nodc:0000999_Not Applicable Chlorophyll data collected by the research vessels Nathaniel B. Palmer and Laurence M. Gould in support of the Southern Ocean studies of the GLOBEC project, May - September 2002 (NCEI Accession 0000999) NOAA_NCEI STAC Catalog 2002-04-14 2002-09-12 -77.76, -69.44, -65.5, -65.12 https://cmr.earthdata.nasa.gov/search/concepts/C2089374535-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0001063_Not Applicable Anthropogenic and natural stresses on coral reefs in Hawaii: a multi-decade synthesis of impact and recovery from 1973 to 2002 (NCEI Accession 0001063) NOAA_NCEI STAC Catalog 1973-01-01 2002-12-31 -155.95, 19.48, -155.5, 22.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089374816-NOAA_NCEI.umm_json In 2002, quantitative photo-transect surveys documenting coral community structure off six coastal sites in Hawaii were repeated to complete longterm data sets of 12 to 30 years duration. Study sites included areas fronting resort development, active and inactive sewage outfalls, and an area where there is no anthropogenic activity, but has been subjected to a variety of storm events. At the only site within a semi-enclosed embayment erosion from surrounding pineapple fields resulted in a decrease in living coral. Such periodic sedimentation in the Bay drives a cycle of damage and recovery that results in coral community structure different than other sheltered embayments in Hawaii. At the other five sites, located in open coastal waters, coral community structure was not adversely affected by shoreline development or discharge of treated sewage effluent. Long-term studies of pristine reefs under natural stress from episodic storms indicate that recovery along the successional continuum varies with time in the different reef zones. The results of these studies provide a framework for effective and efficient coral reef management in Hawaii. Understanding patterns of natural and maninduced stress and recovery can provide a good model for management strategies, as anthropogenic impacts are superimposed over natural stresses. Our results provide good evidence that management efforts should be concentrated in embayments and areas with restricted circulation. Because such areas comprise less than 10% of the coastal areas, it is concluded that the overall condition of coral reefs in Hawaii is good, and should remain so. While concerns of catastrophic loss from anthropogenic impact to coral reefs are valid in some areas of the world, they do not accurately depict the overall health of coral reefs in Hawaii. proprietary gov.noaa.nodc:0001078_Not Applicable Bacteria, carbon dioxide and methane measurements in the Cariaco Basin on the continental shelf of Venezuela, April 2001 - January 2002 (NCEI Accession 0001078) NOAA_NCEI STAC Catalog 2001-04-30 2002-01-17 -64.66, 10.48, -64.66, 10.48 https://cmr.earthdata.nasa.gov/search/concepts/C2089374867-NOAA_NCEI.umm_json Bacteria, carbon dioxide and methane measurements were collected using bottle casts in the Cariaco Basin on the continental shelf of Venezuela from 30 April 2001 to 17 January 2002. Data were submitted by Dr. Mary Scranton of State University of New York in Stony Brook with support from the CArbon Retention In A Colored Ocean (CARIACO) project. proprietary @@ -18691,10 +18759,10 @@ gov.noaa.nodc:0002013_Not Applicable A mesoscale hydrographic survey off Northwe gov.noaa.nodc:0002013_Not Applicable A mesoscale hydrographic survey off Northwest Africa to examine the horizontal mixing by eddies, March - April 2003 (NCEI Accession 0002013) NOAA_NCEI STAC Catalog 2003-03-26 2003-04-16 -31.5, 6.6, -25, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2089373546-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0002170_Not Applicable 22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170) ALL STAC Catalog 2004-05-27 2004-05-27 9.106, 31.684, 33.058, 44.043 https://cmr.earthdata.nasa.gov/search/concepts/C2089373990-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0002170_Not Applicable 22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170) NOAA_NCEI STAC Catalog 2004-05-27 2004-05-27 9.106, 31.684, 33.058, 44.043 https://cmr.earthdata.nasa.gov/search/concepts/C2089373990-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0002192_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192) ALL STAC Catalog 1999-09-01 2002-08-25 -96.01, 23.49, -85.47, 29.38 https://cmr.earthdata.nasa.gov/search/concepts/C2089374092-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002192_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-25 -96.01, 23.49, -85.47, 29.38 https://cmr.earthdata.nasa.gov/search/concepts/C2089374092-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary -gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary +gov.noaa.nodc:0002192_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192) ALL STAC Catalog 1999-09-01 2002-08-25 -96.01, 23.49, -85.47, 29.38 https://cmr.earthdata.nasa.gov/search/concepts/C2089374092-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) ALL STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary +gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) NOAA_NCEI STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) ALL STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002198_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.49, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374298-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary @@ -18727,8 +18795,8 @@ gov.noaa.nodc:0051848_Not Applicable Biomass measurements collected in the Pacif gov.noaa.nodc:0053277_Not Applicable Biomass measurements collected using net in the North and South Atlantic from several platforms from 1950 to 989 (NCEI Accession 0053277) NOAA_NCEI STAC Catalog 1950-01-01 1989-12-31 -86.367, -42.78, 14.175, 53.683 https://cmr.earthdata.nasa.gov/search/concepts/C2089373850-NOAA_NCEI.umm_json Zooplankton biomass data collected by Institute of Biology of the Southern Seas from the Atlantic Ocean in 1950-1989 years and received from the NMFS. proprietary gov.noaa.nodc:0057319_Not Applicable Arctic Freshwater Switchyard Project: Spring temperature and Salinity data collected by aircraft in the Arctic Ocean, May 2006 - May 2007 (NCEI Accession 0057319) NOAA_NCEI STAC Catalog 2003-05-06 2008-05-07 15, 83, -20, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089374588-NOAA_NCEI.umm_json "A program to study freshwater circulation (sea ice + upper ocean) in the ""freshwater switchyard"" between Alert (Ellesmere Island) and the North Pole. The project uses aircraft to take hydrographic stations on sections across the continental slope northwest of Alert." proprietary gov.noaa.nodc:0058268_Not Applicable Beaufort Gyre hydrographic data: Temperature, salinity and transmissivity data from the Louis S St. Laurent in the Arctic Ocean, 2003 - 2008 (NCEI Accession 0058268) NOAA_NCEI STAC Catalog 2003-10-11 2008-10-20 -150, 75, -140, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2089374751-NOAA_NCEI.umm_json The major goal of the observational program is to determine the variability of different components of the Beaufort Gyre fresh water (ocean and sea ice) system and to assess the partial concentrations of fresh water of different origin (rivers, Pacific Ocean, precipitation, ice/snow melt, etc). Using moorings, drifting buoys, shipboard, and remote sensing measurements we have been measuring time series of temperature, salinity, currents, geochemical tracers, sea ice draft, and sea level since August 2003, to determine freshwater content and freshwater fluxes in the Beaufort Gyre during a complete seasonal cycle and beyond. proprietary -gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) ALL STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) NOAA_NCEI STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) ALL STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) NOAA_NCEI STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) ALL STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary gov.noaa.nodc:0066319_Not Applicable Benthic data for corals, macroalgae, invertebrates, and non-living bottom types from Fagatele Bay, Pago Pago, and Fagasa, American Samoa, 2004-2008 (NCEI Accession 0066319) NOAA_NCEI STAC Catalog 2004-01-01 2008-08-01 -170.76892, -14.37023, -170.63047, -14.27847 https://cmr.earthdata.nasa.gov/search/concepts/C2089376136-NOAA_NCEI.umm_json This data set was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type. Summaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland. Fish data were collected by Dr. Alison Green on the same dates and transects and are available in a separate NODC accession. proprietary @@ -18880,8 +18948,8 @@ gov.noaa.nodc:0118720_Not Applicable Biological, chemical, and physical data col gov.noaa.nodc:0124257_Not Applicable Baseline characterization of benthic and coral communities of the Flower Garden Banks, Texas from 2010-05-01 to 2012-08-31 (NCEI Accession 0124257) NOAA_NCEI STAC Catalog 2010-05-01 2012-08-31 -93.87, 27.82, -93.57, 27.99 https://cmr.earthdata.nasa.gov/search/concepts/C2089375884-NOAA_NCEI.umm_json This study utilized ROV photograph transects to quantify benthic habitat and coral communities among the five habitat types (algal nodule, coralline algal reefs, deep reefs and soft bottom) identified in the Flower Garden Banks National Marine Sanctuary (FGBNMS). ROV surveys were conducted in the mid and lower mesophotic zone of the sanctuary (17-150 m) on both the East Bank and the West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary’s management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project is to provide baseline data for all benthic habitats and coral communities. proprietary gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) NOAA_NCEI STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) ALL STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) NOAA_NCEI STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) ALL STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) NOAA_NCEI STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) NOAA_NCEI STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) ALL STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary gov.noaa.nodc:0128996_Not Applicable Benthic and biological data in the New York Bight from 2010-06-01 to 2012-05-31 (NCEI Accession 0128996) NOAA_NCEI STAC Catalog 2010-06-01 2012-05-31 -75, 37, -69, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2089376996-NOAA_NCEI.umm_json These data sets show the distribution of key species and habitats, such as seabirds, bathymetry, surficial sediments, deep sea corals, and oceanographic habitats. NOAA’s Biogeography Branch worked with the New York Department of State (DOS) to interpret existing ecological information and create these new data sets. New York plans to integrate this information with other ecological and human use data compiled by others (for example, The Nature Conservancy, Northeast Fisheries Science Center) and apply ecosystem-based management and plan for ocean uses. Many academic, state and federal and non-governmental organization partners contributed to this project with data, analyses and reviews. Project partners included: the University of Alaska, Biology and Wildlife Department; University of Texas, Institute for Geophysics; The Nature Conservancy, Mid-Atlantic Marine Program; the National Marine Fisheries Service (NMFS), Northeast Fisheries Science Center, and the NMFS, Deep-Sea Coral Research and Technology Program. proprietary @@ -18907,21 +18975,21 @@ gov.noaa.nodc:0146259_Not Applicable Capture and resight data of California sea gov.noaa.nodc:0146680_Not Applicable Benthic Surveys in Vatia, American Samoa: benthic images collected during belt transect surveys from 2015-11-2 to 2015-11-12 (NCEI Accession 0146680) NOAA_NCEI STAC Catalog 2015-11-02 2015-11-12 -170.674, -14.2501, -170.667, -14.2432 https://cmr.earthdata.nasa.gov/search/concepts/C2089378606-NOAA_NCEI.umm_json Jurisdictional managers have expressed concerns that nutrients from the village of Vatia, Tutuila, American Samoa, are having an adverse effect on the coral reef ecosystem in Vatia Bay. Excess nutrient loads promote increases in algal growth that can have deleterious effects on corals, such as benthic algae outcompeting and overgrowing corals. Nitrogen and phosphorus can also directly impact corals by lowering fertilization success, and reducing both photosynthesis and calcification rates. Land-based contributions of nutrients come from a variety of sources; in Vatia the most likely sources are poor wastewater management from piggeries and septic systems. NOAA scientists conducted benthic surveys to establish a baseline against which to compare changes in the algal and coral assemblages in response to nutrient fluxes. The data described here were collected via belt transect surveys of coral demography (adult and juvenile corals) by the NOAA Coral Reef Ecosystem Program (CREP) according to protocols established by the NOAA National Coral Reef Monitoring Program (NCRMP). In 2015 data were collected at 18 stratified randomly selected sites in Vatia Bay. These data include photoquadrat benthic images. proprietary gov.noaa.nodc:0146682_Not Applicable Benthic Surveys in Faga'alu, American Samoa: benthic images collected during belt transect surveys in 2012 and 2015 (NCEI Accession 0146682) NOAA_NCEI STAC Catalog 2012-03-28 2015-11-11 -170.681, -14.2952, -170.673, -14.287 https://cmr.earthdata.nasa.gov/search/concepts/C2089378626-NOAA_NCEI.umm_json The data described herein are part of a NOAA Coral Reef Conservation Program (CRCP) funded project aimed at establishing baseline data for coral demographics and benthic cover and composition via Rapid Ecological Assessment (REA) surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) at Faga'alu Bay, Tutuila, American Samoa between 2012 and 2015. Photoquadrat benthic images were collected in 2012 and 2015 only, via belt transect surveys of coral demography according to protocols established by CREP in 2012 and by the NOAA National Coral Reef Monitoring Program (NCRMP) in 2015. proprietary gov.noaa.nodc:0147683_Not Applicable Bottom longline analytical data collected in Gulf of Mexico from 1995-01-01 to 2013-12-30 (NCEI Accession 0147683) NOAA_NCEI STAC Catalog 1995-01-01 2013-12-30 -97.3473, 24.3627, -81.5875, 30.3677 https://cmr.earthdata.nasa.gov/search/concepts/C2089378649-NOAA_NCEI.umm_json NOAA NMFS does not approve, recommend, or endorse any proprietary product or proprietary material mentioned in this publication. No reference shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion which would indicate or imply that NMFS approves, recommends, or endorses any proprietary product or proprietary material mentioned herein or which has as its purpose any intent to cause directly or indirectly the advertised product to be used or purchased because of this NMFS publication. NMFS is not responsible for any uses of these datasets beyond those for which they were intended, and NMFS makes no claims regarding the accuracy of any data provided by agencies or individuals outside NMFS. Acknowledgment of NOAA NMFS and SEAMAP would be appreciated in products derived or publications generated from this data. proprietary -gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) NOAA_NCEI STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) ALL STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary +gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) NOAA_NCEI STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0148760_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Jakobshavn Glacier Ice Front from 2007-10-13 to 2016-02-14 (NCEI Accession 0148760) NOAA_NCEI STAC Catalog 2007-10-13 2016-02-14 -49.815, 69.222, -49.815, 69.222 https://cmr.earthdata.nasa.gov/search/concepts/C2089378750-NOAA_NCEI.umm_json The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0148760_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Jakobshavn Glacier Ice Front from 2007-10-13 to 2016-02-14 (NCEI Accession 0148760) ALL STAC Catalog 2007-10-13 2016-02-14 -49.815, 69.222, -49.815, 69.222 https://cmr.earthdata.nasa.gov/search/concepts/C2089378750-NOAA_NCEI.umm_json The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0155488_Not Applicable Bottom Dissolved Oxygen Maps From SEAMAP Summer and Fall Groundfish/Shrimp Surveys from 1982 to 1998 (NCEI Accession 0155488) NOAA_NCEI STAC Catalog 1982-01-01 1998-01-01 -98, 18, -74, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2089380245-NOAA_NCEI.umm_json Bottom dissolved oxygen (DO) data was extracted from environmental profiles acquired during the Southeast Fisheries Science Center Mississippi Laboratories summer groundfish trawl surveys of the Western and North-central Gulf of Mexico from 1982-1998. The data were distributed to hypoxia researchers in near real time and used to generate bottom DO maps as part of the Hypoxia Watch Project (http://www.ncddc.noaa.gov/hypoxia/). The profiles were acquired with a Sea-Bird Model SB9 profiler equipped with pressure, temperature, conductivity, fluorescence, and beam transmission sensors. The data were processed with Sea-Bird software using the standard processing protocol developed by the Mississippi Laboratories. Water temperature, beam transmission, and derived salinity, DO and DO percent saturation, and density were retained in the processed files. SAS software was used to extract the bottom DO and other relevant data (e.g., date, time, position, and station number) and format the data as comma-delimited ASCII files. proprietary gov.noaa.nodc:0155948_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ and Palmyra EEZ from 2011-10-20 to 2011-11-17 (NCEI Accession 0155948) NOAA_NCEI STAC Catalog 2011-10-20 2011-11-17 -165.19666, 4.1355, -156.3175, 21.221 https://cmr.earthdata.nasa.gov/search/concepts/C2089376252-NOAA_NCEI.umm_json Water samples were collected from the ocean surface using a bucket and from below the surface using bottles attached to the CTD during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID: SE 11-08). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Surface water samples were also collected opportunistically during some cetacean sightings. CTD samples were collected once each morning. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary gov.noaa.nodc:0155964_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ and Papahanaumokuakea Marine National Monument from 2013-05-08 to 2013-06-03 (NCEI Accession 0155964) NOAA_NCEI STAC Catalog 2013-05-08 2013-06-03 -177, -14.2446, -157.92, 28.79 https://cmr.earthdata.nasa.gov/search/concepts/C2089376312-NOAA_NCEI.umm_json Water samples were collected from the ocean surface using a bucket and from below the surface using bottles attached to the CTD during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 13-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Surface water samples were also collected opportunistically during some cetacean sightings. CTD samples were collected once each morning. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary gov.noaa.nodc:0155998_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ, Palmyra EEZ, and American Samoa EEZ from 2012-04-23 to 2012-05-15 (NCEI Accession 0155998) NOAA_NCEI STAC Catalog 2012-04-23 2012-05-15 -169.9633, -14.2446, -157.2218, 19.2698 https://cmr.earthdata.nasa.gov/search/concepts/C2089376410-NOAA_NCEI.umm_json Surface water samples were collected during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 12-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Samples were also collected opportunistically during some cetacean sightings. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary -gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) ALL STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) NOAA_NCEI STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary -gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) NOAA_NCEI STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary +gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) ALL STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) ALL STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary +gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) NOAA_NCEI STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary gov.noaa.nodc:0156692_Not Applicable Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea from 2013-01-18 to 2014-11-10 (NCEI Accession 0156692) NOAA_NCEI STAC Catalog 2013-01-18 2014-11-10 150.775, -9.875, 150.925, -9.725 https://cmr.earthdata.nasa.gov/search/concepts/C2089377345-NOAA_NCEI.umm_json "Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea. Methodologies, results, and analysis may be found in ""Enhanced macroboring and depressed calcification drive net dissolution at high-CO2 coral reef"" which is published in the Proceedings of the Royal Society, Series B" proprietary -gov.noaa.nodc:0156765_Not Applicable Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765) NOAA_NCEI STAC Catalog 1994-05-06 1996-08-30 -87.6, 29.6, -84.7, 30.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089377384-NOAA_NCEI.umm_json These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems. proprietary gov.noaa.nodc:0156765_Not Applicable Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765) ALL STAC Catalog 1994-05-06 1996-08-30 -87.6, 29.6, -84.7, 30.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089377384-NOAA_NCEI.umm_json These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems. proprietary +gov.noaa.nodc:0156765_Not Applicable Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765) NOAA_NCEI STAC Catalog 1994-05-06 1996-08-30 -87.6, 29.6, -84.7, 30.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089377384-NOAA_NCEI.umm_json These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems. proprietary gov.noaa.nodc:0156869_Not Applicable Captive sea turtle rearing inventory, feeding, and water chemistry in sea turtle rearing tanks at NOAA Galveston, Texas from 1995 to 2015 (NCEI Accession 0156869) NOAA_NCEI STAC Catalog 2005-01-03 2015-12-31 -94.819688, 29.274811, -94.81456, 29.278028 https://cmr.earthdata.nasa.gov/search/concepts/C2089377448-NOAA_NCEI.umm_json The database contains Excel and CSV spreadsheets monitoring captive Sea Turtle rearing program. Daily feeding logs as well as water chemistry were recorded. proprietary gov.noaa.nodc:0156913_Not Applicable Carbonate Budget data of the Southeast Florida Coral Reef Initiative (SEFCRI) region from 2014-09-29 to 2014-10-17 (NCEI Accession 0156913) NOAA_NCEI STAC Catalog 2014-09-29 2014-10-17 -80.104, 25.6519, -80.077, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089377484-NOAA_NCEI.umm_json This data set includes census based carbonate budget data that was collected in coral reef habitats located within the SEFCRI region. Surveys (based on Perry et al 2012) were collected over the course of several weeks at four major sites: Emerald, Oakland Ridge, Barracuda, and Pillars. Within each of these sites, six transect surveys (10m each) were conducted to quantify benthic cover, macrobioerosion, and microbioerosion. Ten parrotfish surveys were also conducted to account for parrotfish erosion rates at each site. This carbonate budget data along with other sea water chemistry data collected were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA. We measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the carbonate budget surveys that were collected to identify the sensitivity of the SEFCRI region to OA. proprietary gov.noaa.nodc:0157022_Not Applicable Carbonate data collected from R/V Hildebrand in the SEFCRI region of the Florida Reef Tract from 2014-05-27 to 2015-09-02 (NCEI Accession 0157022) NOAA_NCEI STAC Catalog 2014-05-27 2015-09-02 -80.1328, 25.5906, -80.077, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089377840-NOAA_NCEI.umm_json This data set includes seawater chemistry that was collected in coral reef habitats located within the SEFCRI region as well as inlets and outfalls that release nutrient rich and/or sediment laden freshwater to the coastal waters South Florida. Freshwater runoff and riverine inputs are known to be enriched in dissolved inorganic carbon, and diluted lower saline waters are known to have elevated pCO2 (e.g., Manzello et al. 2013) which is why those areas in addition to the reef sites were included in our analyses. This data along with other data collected in the field were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA. We measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the seawater samples that were collected and analyzed to identify the carbonate chemistry in this region. proprietary @@ -18930,22 +18998,22 @@ gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of t gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074) NOAA_NCEI STAC Catalog 1995-03-20 1997-03-28 143.63333, -52.08133, 143.805, -47.99867 https://cmr.earthdata.nasa.gov/search/concepts/C2089378023-NOAA_NCEI.umm_json Inverted echo sounder (IES) data were collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) during March 1995 -- March 1997 conducted south of Australia. The collection, processing and calibration of the IES data are described in the report provided. These are the highest quality versions of the data after the least amount of processing. Also provided are low-passed filtered versions that have been calibrated to a common pressure level in order that the data may be used together more conveniently. The measurements were made under the support of the National Science Foundation grants OCE9204041 and OCE9912320. proprietary gov.noaa.nodc:0157087_Not Applicable Behavior of parrotfishes (Labridae, Scarinae) in St. Croix from 2015-07-06 to 2015-07-26 (NCEI Accession 0157087) NOAA_NCEI STAC Catalog 2015-07-06 2015-07-26 -64.813, 17.759, -64.608, 17.787 https://cmr.earthdata.nasa.gov/search/concepts/C2089378063-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on coral reefs in the Caribbean this project documented the foraging behavior and diets of six species of parrotfishes (Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) at three locations (Long Reef, Cane Bay, and Buck Island) on the north shore of St. Croix, U. S. Virgin Islands. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, (5) ledge, or (6) sand. In order to quantify the relative abundance of different substrates and food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the six substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, ledge, and sand) in 0.5 m x 0.5 m photoquadrats. Photographs were taken at 2.5 m intervals on 30 m transects, with a total of 10 haphazardly placed transects sampled at each site. Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary gov.noaa.nodc:0157611_Not Applicable Benthic Images from Towed-Diver Surveys in the Main Hawaiian Islands to Assess the Mass Coral Bleaching Event from 2015-11-03 to 2015-11-18 (NCEI Accession 0157611) NOAA_NCEI STAC Catalog 2015-11-03 2015-11-18 -157.9472292, 19.748537, -155.829342, 21.3030689 https://cmr.earthdata.nasa.gov/search/concepts/C2089376905-NOAA_NCEI.umm_json A team from the Pacific Islands Fisheries Science Center (PIFSC), Coral Reef Ecosystem Program (CREP) deployed on a two-week research cruise in November 2015 to evaluate the impacts of the 2015 mass coral bleaching event in the Main Hawaiian Islands via towed-diver surveys. Areas surveyed included south Oahu, west Maui, Lana’i, and west Hawaii island. Over the course of 10 survey days, the team surveyed approximately 90 km of 15-m wide transects at depths ranging from 2 to 10 m. Data provided in this dataset include benthic images that were collected during the towed-diver surveys from a camera that was mounted to the towboard. A downward-facing DSLR camera with strobes collected these photographic quadrat data by capturing an image of the benthos at 15-second intervals during the surveys. Two additional datasets were collected during the surveys and are documented separately. Towed divers recorded visual estimates of percentage of live coral that was pale and bleached, as well as presence/absence data of condition by generic composition. Oceanographic data was collected continuously throughout each survey with a suite of sensors mounted to the towboard recording conductivity, temperature, depth, flourometry (chlorophyll-a), turbidity and dissolved oxygen. proprietary -gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) ALL STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) NOAA_NCEI STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary -gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) ALL STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary +gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) ALL STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) NOAA_NCEI STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary +gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) ALL STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary gov.noaa.nodc:0159850_Not Applicable Burrowing behavior of penaeid shrimps in the Gulf of Mexico from 1984-10-01 to 1985-12-06 (NCEI Accession 0159850) NOAA_NCEI STAC Catalog 1984-10-01 1985-12-06 -94.815127, 29.275417, -94.815127, 29.275417 https://cmr.earthdata.nasa.gov/search/concepts/C2089377792-NOAA_NCEI.umm_json This data set contains hourly visual observations of burrowing behavior in penaeid shrimp. proprietary gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) NOAA_NCEI STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) ALL STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary gov.noaa.nodc:0161523_Not Applicable Biological, chemical, physical and time series data collected from station WQB04 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2010-10-23 to 2016-12-31 (NCEI Accession 0161523) NOAA_NCEI STAC Catalog 2010-10-23 2016-12-31 -155.082, 19.7341, -155.082, 19.7341 https://cmr.earthdata.nasa.gov/search/concepts/C2089378474-NOAA_NCEI.umm_json NCEI Accession 0161523 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB04: PacIOOS Water Quality Buoy 04 (WQB-04): Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary -gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) ALL STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) NOAA_NCEI STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary +gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) ALL STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary gov.noaa.nodc:0162828_Not Applicable Benthic cover derived from analysis of benthic images collected at coral reef sites in Batangas, Philippines from 2015-05-23 to 2015-06-03 (NCEI Accession 0162828) NOAA_NCEI STAC Catalog 2015-05-23 2015-06-03 120.872, 13.6586, 120.895, 13.7281 https://cmr.earthdata.nasa.gov/search/concepts/C2089380438-NOAA_NCEI.umm_json The benthic cover data described here result from benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in 2015 along transects at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) over time. Benthic habitat photographs were quantitatively analyzed using a web-based annotation tool called CoralNet (Beijbom et al. 2016). Images were analyzed to produce three functional group levels of benthic cover: Tier 1 (e.g., hard coral, soft coral, macroalgae, turf algae, etc.), Tier 2 (e.g., Hard Coral = massive, branching, foliose, encrusting, etc.; Macroalgae = upright macroalgae, encrusting macroalgae, bluegreen macroalgae, and Halimeda, etc.), and Tier 3 (e.g., Hard Coral = Astreopora sp, Favia sp, Pocillopora, etc.; Macroalgae = Caulerpa sp, Dictyosphaeria sp, Padina sp, etc.). These benthic cover data for the Philippines provide an estimate of the benthic community composition at each climate survey site, and give context to the results from the other climate survey components (archived separately). proprietary gov.noaa.nodc:0162829_Not Applicable Assessing cryptic reef diversity of colonizing marine invertebrates using Autonomous Reef Monitoring Structures (ARMS) deployed at coral reef sites in Batangas, Philippines from 2012-03-12 to 2015-05-31 (NCEI Accession 0162829) NOAA_NCEI STAC Catalog 2012-03-12 2015-05-31 120.871943, 13.658594, 120.895127, 13.728054 https://cmr.earthdata.nasa.gov/search/concepts/C2089380450-NOAA_NCEI.umm_json Autonomous Reef Monitoring Structures (ARMS) are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is that biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time. The data described here were collected by CREP from ARMS moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and three ARMS units were deployed by SCUBA divers at each survey site. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. Each ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms and scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 µm, and 100 µm. The 500 µm and 100 µm fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies. The DNA sequencing data are not included in this archival package. proprietary gov.noaa.nodc:0162830_Not Applicable Benthic images collected at coral reef sites in Batangas, Philippines from 2012-03-13 to 2012-03-15 and from 2015-05-24 to 2015-06-03 (NCEI Accession 0162830) NOAA_NCEI STAC Catalog 2012-03-13 2015-06-03 120.872, 13.6586, 120.895, 13.7281 https://cmr.earthdata.nasa.gov/search/concepts/C2089380458-NOAA_NCEI.umm_json Photographs of the seafloor were collected during benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in 2012 and 2015 along transects at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) over time. The imagery from 2015 has been quantitatively analyzed using image analysis software to derive an estimate of percent benthic cover (archived separately). proprietary gov.noaa.nodc:0162831_Not Applicable Calcification rates of crustose coralline algae (CCA) derived from Calcification Accretion Units (CAUs) deployed at coral reef sites in Batangas, Philippines in 2012 and recovered in 2015 (NCEI Accession 0162831) NOAA_NCEI STAC Catalog 2012-03-13 2015-06-03 120.872, 13.6586, 120.895, 13.7281 https://cmr.earthdata.nasa.gov/search/concepts/C2089380467-NOAA_NCEI.umm_json Laboratory experiments reveal calcification rates of crustose coralline algae (CCA) are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry. Calcification accretion units, or CAUs, are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae. CAUs, constructed in-house by CREP, are composed of two 10 x 10 cm flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos by SCUBA divers using stainless steel threaded rods. Deployed on the seafloor for a period of time, calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over time. By measuring the change in weight of the CAUs, the reef carbonate accretion rate can be calculated for that time period. The calcification rate data described here were collected by CREP from CAUs moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines, in accordance with protocols developed by Price et al. (2012). Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and five CAUs were deployed at each survey site. In conjunction with benthic community composition data (archived separately), these data serve as a baseline for detecting changes associated with changing seawater chemistry due to ocean acidification within coral reef environments. proprietary -gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) NOAA_NCEI STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) ALL STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary +gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) NOAA_NCEI STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) ALL STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying 
heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) NOAA_NCEI STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying 
heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary gov.noaa.nodc:0163750_Not Applicable Biological, chemical and other data collected from station Humboldt Bay Pier by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2012-12-13 to 2018-03-07 (NCEI Accession 0163750) NOAA_NCEI STAC Catalog 2012-12-13 2018-03-07 -124.19652, 40.7775, -124.19652, 40.7775 https://cmr.earthdata.nasa.gov/search/concepts/C2089376545-NOAA_NCEI.umm_json NCEI Accession 0163750 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary @@ -18987,28 +19055,28 @@ gov.noaa.nodc:0171331_Not Applicable Biological, chemical and other data collect gov.noaa.nodc:0171332_Not Applicable Biological, chemical and other data collected from station Indian River Lagoon - Jensen Beach (IRL-JB) by Florida Atlantic University and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida from 2015-10-07 to 2020-06-18 (NCEI Accession 0171332) NOAA_NCEI STAC Catalog 2015-10-07 2020-06-18 -80.20233, 27.22439, -80.20233, 27.22439 https://cmr.earthdata.nasa.gov/search/concepts/C2089377488-NOAA_NCEI.umm_json NCEI Accession 0171332 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Jensen Beach (IRL-JB) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0171345_Not Applicable Chemical, meteorological and other data collected from station Pilot's Cove, Apalachicola Bay, by Florida Department of Environmental Protection and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida and Gulf of Mexico from 2015-11-09 to 2020-03-09 (NCEI Accession 0171345) NOAA_NCEI STAC Catalog 2015-11-09 2020-03-09 -85.0277, 29.60139, -85.0277, 29.60139 https://cmr.earthdata.nasa.gov/search/concepts/C2089377631-NOAA_NCEI.umm_json NCEI Accession 0171345 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Pilot's Cove, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0171346_Not Applicable Chemical, meteorological and other data collected from station Dry Bar, Apalachicola Bay, by Florida Department of Environmental Protection and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida and Gulf of Mexico from 2015-12-01 to 2018-10-10 (NCEI Accession 0171346) NOAA_NCEI STAC Catalog 2015-12-01 2018-10-10 -85.05807, 29.67431, -85.05807, 29.67431 https://cmr.earthdata.nasa.gov/search/concepts/C2089377641-NOAA_NCEI.umm_json NCEI Accession 0171346 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Dry Bar, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary -gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) ALL STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) NOAA_NCEI STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary -gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) ALL STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary +gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) ALL STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) NOAA_NCEI STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary +gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) ALL STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary gov.noaa.nodc:0172588_Not Applicable Biological, chemical, and other data collected from station Humboldt Bay Pier by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2012-12-13 to 2021-06-09 (NCEI Accession 0172588) NOAA_NCEI STAC Catalog 2012-12-13 2021-06-09 -124.19652, 40.7775, -124.19652, 40.7775 https://cmr.earthdata.nasa.gov/search/concepts/C2089378189-NOAA_NCEI.umm_json NCEI Accession 0172588 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0172612_Not Applicable Biological, chemical and other data collected from station Monterey Bay Commercial Wharf by Moss Landing Marine Laboratory and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2015-05-05 to 2020-01-03 (NCEI Accession 0172612) NOAA_NCEI STAC Catalog 2015-05-05 2020-01-03 -121.88935, 36.60513, -121.88935, 36.60513 https://cmr.earthdata.nasa.gov/search/concepts/C2089378278-NOAA_NCEI.umm_json NCEI Accession 0172612 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Moss Landing Marine Laboratory collected the data from their in-situ moored station named Monterey Bay Commercial Wharf in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Moss Landing Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0172613_Not Applicable Biological, chemical and other data collected from station Indian Island by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2016-04-05 to 2019-10-28 (NCEI Accession 0172613) NOAA_NCEI STAC Catalog 2016-04-05 2019-10-28 -124.15754, 40.81503, -124.15754, 40.81503 https://cmr.earthdata.nasa.gov/search/concepts/C2089378289-NOAA_NCEI.umm_json NCEI Accession 0172613 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Indian Island in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0173246_Not Applicable Benthic Fauna and Hydrography at Four Sites in the Mobile-Tensaw River Delta, Alabama (1981-1982) (NCEI Accession 0173246) NOAA_NCEI STAC Catalog 1981-11-17 1982-09-29 -88.004, 30.411, -87.562, 31.055 https://cmr.earthdata.nasa.gov/search/concepts/C2089378543-NOAA_NCEI.umm_json Bimonthly surveys of benthic fauna were conducted at four sites in the Mobile-Tensaw River Delta from November 1981 to September 1982. Two sites were at the upper reaches of the river delta, and two were at the mouth. Fauna were enumerated and identified to lowest taxon possible. Hydrographic data were also collected, including temperature, conductivity, and dissolved oxygen. proprietary gov.noaa.nodc:0173316_Not Applicable Carbon dioxide, hydrographic and chemical data collected from profile discrete samples during the R/V Nathaniel B. Palmer 2015 OOISO; NBP15_11, SOCCOM cruise (EXPOCODE 320620151206) in the South Pacific Ocean from 2015-12-06 to 2016-01-04 (NCEI Accession 0173316) NOAA_NCEI STAC Catalog 2015-12-06 2016-01-04 -89.72, -54.6, -80.11, -52.93 https://cmr.earthdata.nasa.gov/search/concepts/C2089378635-NOAA_NCEI.umm_json This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, total alkalinity and pH on Total scale obtained during the R/V Nathaniel B. Palmer 2015 OOISO; NBP15_11, SOCCOM cruise (EXPOCODE 320620151206) in the South Pacific Ocean from 2015-12-06 to 2016-01-02. proprietary -gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) NOAA_NCEI STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) ALL STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary -gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) NOAA_NCEI STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary +gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) NOAA_NCEI STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) ALL STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary -gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) NOAA_NCEI STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary +gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) NOAA_NCEI STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) ALL STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary +gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) NOAA_NCEI STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary gov.noaa.nodc:0176496_Not Applicable Biological Baseline Studies of Mobile Bay (MESC-CAB 1980-1981): Hydrography, Sediments, Benthic Fauna, Pelagic Fauna, Phytoplankton, and Zooplankton (NCEI Accession 0176496) NOAA_NCEI STAC Catalog 1980-04-03 1981-08-26 -88.17333, 30.23833, -87.85167, 30.61333 https://cmr.earthdata.nasa.gov/search/concepts/C2089376767-NOAA_NCEI.umm_json Data from a monthly survey of Mobile Bay between April 1980 and August 1981. Extant data from the MESC Data Management System include sediment particle size distribution, discrete hydrography, identification and enumeration of benthic fauna, and identification and enumeration of water column biota. proprietary gov.noaa.nodc:0185741_Not Applicable Carbonate Chemistry Dynamics on Southeast Florida coral reefs from 2014-05-27 to 2015-09-03 (NCEI Accession 0185741) NOAA_NCEI STAC Catalog 2014-05-27 2015-09-03 -80.132778, 25.6519, -80.076975, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089379082-NOAA_NCEI.umm_json These data are from the article “Seasonal carbonate chemistry dynamics on southeast Florida coral reefs: localized acidification hotspots from navigational inlets” published in Frontiers in Marine Science. The data in this package were collected from inlets and reefs along the coast of Southeast Florida. Water was collected bi-monthly from four reefs (Oakland Ridge, Barracuda, Pillars, and Emerald) and three closely-associated inlets (Port Everglades, Bakers Haulover, and Port of Miami). Water samples were collected at these locations either at the surface (~1m depth) or immediately above the benthos measured using a rosette sampler (ECO 55, Seabird). Temperature was recorded at each depth using a CTD (SBE 19V2, Seabird). Turbidity (NTU) was measured at time of water collection. Once collected, water samples were transferred to borosilicate glass bottles, samples were fixed using 200 µL of HgCl2 and sealed using Apiezon grease and a glass stopper. Salinity was measured using a densitometer (DMA 5000M, Anton Paar), while total alkalinity (TA) and dissolved inorganic carbon (DIC) were determined using Apollo SciTech instruments (AS-ALK2 and AS-C3, respectively). All values were measured in duplicate and corrected using certified reference materials following recommendations in Dickson et al. (2007). Aragonite saturation state (ΩArag.), Calcite saturation state (ΩCa), pH (Total scale), and the partial pressure of CO2 (pCO2) were calculated with CO2SYS (Lewis and Wallace, 1998) using the dissociation constants of Mehrbach et al. (1973) as refit by Dickson and Millero (1987) and Dickson (1990). Water samples were reserved for nutrient analyzed at time of collection to determine Total Kjeldahl Nitrogen, Total Phosphorous, and fluorescence of Chlorophyll-a. This research was supported through NOAA’s Coral Reef Conservation Program. proprietary gov.noaa.nodc:0185742_Not Applicable Climatology for NOAA Coral Reef Watch (CRW) Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 for 1985-01-01 to 2012-12-31 (NCEI Accession 0185742) NOAA_NCEI STAC Catalog 1985-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379091-NOAA_NCEI.umm_json This package contains a set of 12 monthly mean (MM) climatologies, one for each calendar month, and the maximum monthly mean (MMM) climatology. Each climatology has global coverage at 0.05-degree (5km) spatial resolution. The climatologies were derived from NOAA Coral Reef Watch's (CRW) CoralTemp Version 1.0 product and are based on the 1985-2012 time period of the CoralTemp data. They are used in deriving CRW's Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1. MMs are used to derive the SST Anomaly product, and the MMM is used to derive CRW's Coral Bleaching HotSpot, Degree Heating Week, and Bleaching Alert Area products. proprietary -gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) NOAA_NCEI STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) ALL STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary -gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) NOAA_NCEI STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary +gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) NOAA_NCEI STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) ALL STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary +gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) NOAA_NCEI STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary gov.noaa.nodc:0191401_Not Applicable Biogeochemical and microbiological measurements in the Cariaco Basin, a truly marine anoxic system in the southeastern Caribbean Sea, from 1995-11-13 to 2015-11-14 by the CARIACO Ocean Time Series Program (formerly known as CArbon Retention In A Colored Ocean) aboard the B/O Hermano Gines (NCEI Accession 0191401) NOAA_NCEI STAC Catalog 1995-11-13 2015-11-14 -64.66, 10.5, -64.66, 10.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089377738-NOAA_NCEI.umm_json Biogeochemical and microbiological variables were measured by Stony Brook University participants (see Author List) in the CARIACO Ocean Time-Series Program in order to study the microbial cycling of carbon, sulfur, and nitrogen occurring at depths where waters transition from oxic to anoxic to sulfidic. Samples were collected by Nikson bottles from 1995-11-13 to 2015-11-14 in the Cariaco Basin (southeastern Caribbean Sea off northeastern Venezuelan coast) aboard the B/O Hermano Gines, operated by the Fundacion La Salle, Venezuela. proprietary gov.noaa.nodc:0194300_Not Applicable ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300) NOAA_NCEI STAC Catalog 2012-04-11 2012-04-24 -90.5895, 27.2111, -87.42629, 30.35717 https://cmr.earthdata.nasa.gov/search/concepts/C2089378330-NOAA_NCEI.umm_json This dataset contains ADCP, CTD, water and sediment chemistry, and other underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24. The CTD profiles were done at 4 locations using Sea-Bird SBE 911plus from 2012-04-11 to 2012-04-14 and include seawater conductivity, temperature, pressure, salinity, density, oxygen concentration, sound velocity, dissolved oxygen, beam attenuation, light transmission, fluorescence, surface irradiance, and depth parameters. The current velocity data was measured by a hull-mounted mounted Acoustic Doppler Current Profiler (ADCP) and other underway sensor data was measured with a Sea-Bird SBE 21 (tsg), Sea-Bird SBE 45 (tsg) and underway sensors/navigational instruments. All data records include sampling time (UTC), position (Latitude, Longitude) and water depth. In addition, the dataset also includes the water column and sediment chemistry data and the measurements include the concentration of dissolved nutrients, dissolved gases, total particulate nitrogen (TPN), total particulate carbon (TPN), particulate organic carbon (POC), and particulate inorganic carbon acquired from 8 CTD casts and 6 multiple corer drops. The objective of this cruise was to study the impact of the Deepwater Horizon (DWH) blowout on the water column and benthic communities of the Gulf of Mexico and compare these impacts to naturally occurring oil and gas seeps. These data are also available at Rolling Deck to Repository (R2R) under cruise https://doi.org/10.7284/902570. proprietary gov.noaa.nodc:0194300_Not Applicable ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300) ALL STAC Catalog 2012-04-11 2012-04-24 -90.5895, 27.2111, -87.42629, 30.35717 https://cmr.earthdata.nasa.gov/search/concepts/C2089378330-NOAA_NCEI.umm_json This dataset contains ADCP, CTD, water and sediment chemistry, and other underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24. The CTD profiles were done at 4 locations using Sea-Bird SBE 911plus from 2012-04-11 to 2012-04-14 and include seawater conductivity, temperature, pressure, salinity, density, oxygen concentration, sound velocity, dissolved oxygen, beam attenuation, light transmission, fluorescence, surface irradiance, and depth parameters. The current velocity data was measured by a hull-mounted mounted Acoustic Doppler Current Profiler (ADCP) and other underway sensor data was measured with a Sea-Bird SBE 21 (tsg), Sea-Bird SBE 45 (tsg) and underway sensors/navigational instruments. All data records include sampling time (UTC), position (Latitude, Longitude) and water depth. In addition, the dataset also includes the water column and sediment chemistry data and the measurements include the concentration of dissolved nutrients, dissolved gases, total particulate nitrogen (TPN), total particulate carbon (TPN), particulate organic carbon (POC), and particulate inorganic carbon acquired from 8 CTD casts and 6 multiple corer drops. The objective of this cruise was to study the impact of the Deepwater Horizon (DWH) blowout on the water column and benthic communities of the Gulf of Mexico and compare these impacts to naturally occurring oil and gas seeps. These data are also available at Rolling Deck to Repository (R2R) under cruise https://doi.org/10.7284/902570. proprietary @@ -19030,24 +19098,24 @@ gov.noaa.nodc:0209222_Not Applicable Abundance, biomass, and density of benthic gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) ALL STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) NOAA_NCEI STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary gov.noaa.nodc:0209247_Not Applicable Benthic cover derived from structure from motion images collected during marine debris surveys at coral reef sites entangled with derelict fishing nets at Pearl and Hermes Atoll in the Northwestern Hawaiian Islands from 2018-09-24 to 2018-10-03 (NCEI Accession 0209247) NOAA_NCEI STAC Catalog 2018-09-24 2018-10-03 -175.8211335, 27.8274571, -175.7880926, 27.8940486 https://cmr.earthdata.nasa.gov/search/concepts/C2089378869-NOAA_NCEI.umm_json The benthic cover and fishing-net related data described in this dataset are derived from the GIS analysis of benthic orthophotos. The source imagery was collected using a Structure from Motion (SfM) approach during in-water marine debris swim surveys conducted by snorkelers in search of derelict fishing nets. Surveys were conducted by the NOAA Fisheries, Ecosystem Sciences Division (ESD) from September 24 to October 3, 2018 at Pearl and Hermes Atoll during an ESD-led marine debris mission to the Northwestern Hawaiian Islands (NWHI) aboard NOAA Ship Oscar Elton Sette. The lagoon at Pearl and Hermes was surveyed equally across the spatial gradient, from locations where derelict fishing nets are common to locations where derelict fishing nets have never been observed. During the 2018 mission, only a subset of marine debris surveys resulted in a SfM survey. Fishing nets were located during swim surveys and selected for SfM if the net was interacting with coral or hard substrate, the depth of the net was within ~1–4 m of the surface, and the area of the net fit within the 9 sq. meter SFM survey plot. During a SFM survey, a permanent 3 x 3 m plot was established around the center of the fishing net, and the net was photographed using a back and forth swim pattern (“before” photos) for later processing using a SfM approach. The net was then removed, the volume of net removed was estimated and recorded, and the same area was photographed again in the same way (“after” photos). A nearby (>50 m distant) paired control site was also photographed using the same method (“control” photos). The photographs were processed using Agisoft Metashape software to generate orthomosaic images that were analyzed in ArcGIS for benthic cover using a random point approach. The number of points at net-impacted sites were constrained to the net coverage area and were scaled to the net area to ensure an equal point density among replicate net-impact sites. The same number of points were randomly assigned to the 3 × 3 m paired control site. Each point was classified into one of seven benthic categories: turf algae, macroalgae, sand, bare substrate, Porites compressa, sponge, or crustose coralline algae (CCA). The annotated points for each site were converted to percent cover for each benthic category. Fishing net size (sq m) and degree of fouling were also calculated from the orthophotos. Analyses were conducted to compare the benthic composition of net sites to control sites and to determine if fouling or net size contributed to these differences. proprietary -gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) NOAA_NCEI STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) ALL STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary -gov.noaa.nodc:0210577_Not Applicable Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577) ALL STAC Catalog 2014-07-15 2018-11-11 -162, 11, -50, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2089380393-NOAA_NCEI.umm_json Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF. proprietary +gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) NOAA_NCEI STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary gov.noaa.nodc:0210577_Not Applicable Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577) NOAA_NCEI STAC Catalog 2014-07-15 2018-11-11 -162, 11, -50, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2089380393-NOAA_NCEI.umm_json Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF. proprietary +gov.noaa.nodc:0210577_Not Applicable Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577) ALL STAC Catalog 2014-07-15 2018-11-11 -162, 11, -50, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2089380393-NOAA_NCEI.umm_json Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF. proprietary gov.noaa.nodc:0210808_Not Applicable Assessment of coral reef fish and benthic communities in the West Hawaii Habitat Focus Area from 2015-10-13 to 2015-10-23 (NCEI Accession 0210808) NOAA_NCEI STAC Catalog 2015-10-13 2015-10-23 -156.048008, 19.568405, -155.828939, 20.059629 https://cmr.earthdata.nasa.gov/search/concepts/C2089380539-NOAA_NCEI.umm_json This archive package contains data on species composition, density, size, and abundance for coral reef fish as well as coral counts, benthic cover, and macroalga cover in the West Hawaii Habitat Focus Area along the Kona coast of the island of Hawaii. Data provided in this collection were gathered as part of the NOAA Habitat Blueprint initiative with support from the Coral Reef Conservation Program. Data were collected primarily by The Nature Conservancy Hawaii. Data were collected in 2015 using the Belt Transect method. This is the first year in a series of monitoring efforts which have taken place in subsequent years to evaluate the resilience of coral reefs in the Focus Area. This dataset serves as a baseline as it was collected during the 2015 coral bleaching event. The dataset accompanies the NOAA technical report Maynard et al. 2016. proprietary gov.noaa.nodc:0213517_Not Applicable Black Sea High Resolution SST L4 Analysis 0.0625 deg Resolution for 2019-09-18 (NCEI Accession 0213517) NOAA_NCEI STAC Catalog 2019-09-18 2019-09-18 26.375, 38.75, 42.375, 48.8125 https://cmr.earthdata.nasa.gov/search/concepts/C2089376602-NOAA_NCEI.umm_json CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea. proprietary gov.noaa.nodc:0218215_Not Applicable Circulation, temperature, and water surface elevation from Finite Volume Community Ocean Model (FVCOM) simulations of Lake Superior, Great Lakes region from 2010-01-01 to 2012-12-31 to study the 2010 coastal upwelling event (NCEI Accession 0218215) NOAA_NCEI STAC Catalog 2010-01-01 2012-12-31 -92.08, 46.44, -84.38, 48.79 https://cmr.earthdata.nasa.gov/search/concepts/C2089376983-NOAA_NCEI.umm_json "This dataset contains a three-dimensional (3-D), coupled ice-ocean Finite Volume Community Ocean Model (FVCOM) hydrodynamic simulations of circulation, temperature, and water surface elevation of Lake Superior for the years 2010-2012. The model was validated with temperature observations at National Oceanic and Atmospheric Administration (NOAA) buoys and mooring data from 2010. The upwelling event observed in satellite imagery and at a mooring station was reproduced by the model, in August 2010 along the northwestern coast. FVCOM version 3.1.6 was used for these simulations including custom modifications for wind-wave mixing (Hu and Wang, 2010) and centered-difference time integration. Ice simulations used the unstructured-grid, community ice code (UG-CICE) that was included with FVCOM version 3.1.6 (Chen et al. 2011; Gao et al. 2011). North American Regional Reanalysis (NARR) 32 km data (Mesinger et al. 2006) was used as atmospheric boundary conditions which included heat flux components (i.e., ""heating_on=T"" in the namelist). To convert the NARR forcings to the FVCOM unstructured grid, the interpolation scheme built in to FVCOM (WRF2FVCOM) was used. Details for these simulations can be found in the namelist file ""narr_0913_run.nml"" included in this data archive." proprietary gov.noaa.nodc:0220639_Not Applicable Barium isotopes collected from world-wide oceans from 1970 to 2006 and analyzed at WHOI (NCEI Accession 0220639) NOAA_NCEI STAC Catalog 1970-01-01 2006-01-01 -178.073, -76.449, 174.4, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2089377693-NOAA_NCEI.umm_json Barium isotope data from marine barites deposited throughout the world wide oceans. Samples include cold seep, hydrothermal and pelagic barites. Samples were collected from 1970 to 2006, and analyses were conducted in the NIRVANA lab at WHOI between 2016 and 2019. Data are in spreadsheet format. proprietary -gov.noaa.nodc:0221188_Not Applicable 3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188) ALL STAC Catalog 2017-09-24 2017-09-24 -88.974, 28.932, -88.965, 28.944 https://cmr.earthdata.nasa.gov/search/concepts/C2089377874-NOAA_NCEI.umm_json The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys. Data are in NetCDF. proprietary gov.noaa.nodc:0221188_Not Applicable 3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188) NOAA_NCEI STAC Catalog 2017-09-24 2017-09-24 -88.974, 28.932, -88.965, 28.944 https://cmr.earthdata.nasa.gov/search/concepts/C2089377874-NOAA_NCEI.umm_json The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys. Data are in NetCDF. proprietary +gov.noaa.nodc:0221188_Not Applicable 3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188) ALL STAC Catalog 2017-09-24 2017-09-24 -88.974, 28.932, -88.965, 28.944 https://cmr.earthdata.nasa.gov/search/concepts/C2089377874-NOAA_NCEI.umm_json The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys. Data are in NetCDF. proprietary gov.noaa.nodc:0225446_Not Applicable Assessment of coral reef benthic communities and reef fish survey data from locations in the Commonwealth of Northern Marianas Islands from 2014-10-01 to 2018-09-30 (NCEI Accession 0225446) NOAA_NCEI STAC Catalog 2014-10-01 2018-09-30 145.131154, 14.1136578, 145.8147431, 16.7162927 https://cmr.earthdata.nasa.gov/search/concepts/C2089379287-NOAA_NCEI.umm_json Overview Currently, the LTMMP has 52 long-term monitoring sites across Saipan, Tinian, and Rota that are surveyed on a rotating biennial basis. Three main habitat types are covered: Fore reef, reef flat (lagoon), and seagrass beds (lagoon). Most sites have been selected based on their association with management concerns (runoff, sewage outfalls, urban development, etc.) and/or management actions (watershed restorations efforts, marine protected areas, etc.) and include impacted sites and relatively non-impacted reference sites. In general, monitoring surveys are conducted using standard and proven ecological field survey methods. All surveys are conducted along 3-5, 50 m transect lines laid out along the depth contour (~9m depth) on the fore reef, or along consistent habitat in the lagoon (back reef and seagrass). While benthic cover analysis provides the foundation of the CNMI monitoring program, the current protocol uses several survey types per site to provide ecological depth beyond percent cover. Fore Reef Photos are taken every meter along each transect line using a 0.25m2 quadrat frame, for a total of 250 photos at each site. In the office, the computer program CPCe is used to place five random points on each photo and the biota or substrate type under each point is identified. Organisms are identified to the genus level. This analysis provides benthic percent cover and community diversity. Twelve, 3 minute, 5 m radius stationary point counts (SPC) are conducted at each site to evaluate fish assemblages. Each SPC is systematically positioned throughout the length of a site (250 m). The species and size (fork length) of all food fishes within the 5 meter radius are recorded. This provides relative diversity, abundances, species compositions, size class distribution, and biomass of the fish community. Sixteen 0.25m2 quadrats are haphazardly tossed along the length of the site and every coral colony within the quadrats is identified to the species level and measured. This method provides relative diversity, abundances, species composition, and size class of the coral community. Within these same quadrats, all algae species present are identified to the species level to provide a measure of algae community composition and species richness. Finally, non-coral macro-invertebrates including sea cucumbers, urchins, crown-of-thorns starfish, giant clams, among others, are identified and counted within 1 m of each side of the transect lines (i.e. 5, 2mx50m belt transects). This provides invertebrate abundances, species composition, and diversity. Saipan Lagoon Saipan Lagoon habitats that are monitored include Halodule uninervis beds, staghorn Acropora thickets, and mixed coral back reefs. At lagoon sites, benthic cover is quantified using a 0.25 m2 string quadrat with six intersections, placed every meter along the transect line. The biota or substrate under each intersection is recorded to the genus level, in situ. Additionally, 10, 1 m2 quads are haphazardly placed across the length of the site (250 m) and all seagrass, algae, coral, and macro-invertebrates are identified to the species level and recorded. This method captures the relative diversity, abundance, and species compositions of lagoon communities. Finally, non-coral macro-invertebrate abundances and diversity are quantified as described above for reef slope sites. proprietary gov.noaa.nodc:0225545_Not Applicable Bulk density and pore water, sediment texture and composition data from sediment cores collected aboard R/V Weatherbird II cruises WB-0812 and WB-0813 in the northern Gulf of Mexico from 2012-08-14 to 2013-08-21 (NCEI Accession 0225545) NOAA_NCEI STAC Catalog 2012-08-14 2013-08-21 -88.86673, 28.97363, -86.33833, 29.73833 https://cmr.earthdata.nasa.gov/search/concepts/C2089379450-NOAA_NCEI.umm_json This dataset contains the bulk density and pore water, sediment texture and composition data from sediment cores collected aboard R/V Weatherbird II cruises WB-0812 and WB-0813 in the northern Gulf of Mexico (nGoM) from 2012-08-14 to 2013-08-21. These data were generated for selected core sub-samples at 2mm sampling intervals for “surficial unit” and 5mm sampling resolution intervals to the base of cores. For the bulk density and pore water data, sediment cores were collected on board the R/V Weatherbird II cruise WB-0812 in the nGoM from 2012-08-14 to 2012-08-16. It reports measurement of sediment sample wet weight (g), dry weight (g) and percent pore water. Bulk density is the dry weight per sampling volume expressed as g/cm3. Whereas, sediment texture and composition data were collected aboard R/V Weatherbird II cruise WB-0813 in the nGoM from 2013-08-20 to 2013-08-21. Sediment texture values were expressed as percent gravel, sand, silt, and clay. Percent of mud can be calculated by combining percent silt and clay. Sediment composition was expressed as percent total organic matter (TOM) measured by loss on ignition (LOI), percent carbonate content measured by acid leaching, and the percent insoluble residue (IR), which was likely dominated by terrigenous clastic (land-derived) sediment sources. proprietary gov.noaa.nodc:0225979_Not Applicable Biological, chemical, physical and time series data collected from station WQBAW by University of Hawai'i at Hilo and University of Hawai'i at Mānoa and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2008-06-06 to 2016-12-06 (NCEI Accession 0225979) NOAA_NCEI STAC Catalog 2008-06-06 2016-12-06 -157.848, 21.2799, -157.848, 21.2799 https://cmr.earthdata.nasa.gov/search/concepts/C2089379551-NOAA_NCEI.umm_json NCEI Accession 0225979 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at Mānoa collected the data from their in-situ moored station named WQBAW: PacIOOS Water Quality Buoy AW (WQB-AW): Ala Wai, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at Mānoa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-AW is located at the exit of the Ala Wai Canal, near Magic Island. Continuous sampling of this outflow area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary gov.noaa.nodc:0226059_Not Applicable Biological, chemical, physical and time series data collected from station WQBKN by University of Hawai'i at Hilo and University of Hawai'i at Mānoa and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2008-08-07 to 2017-01-04 (NCEI Accession 0226059) NOAA_NCEI STAC Catalog 2008-08-07 2017-01-04 -157.865, 21.2887, -157.865, 21.2887 https://cmr.earthdata.nasa.gov/search/concepts/C2089380013-NOAA_NCEI.umm_json NCEI Accession 0226059 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at Mānoa collected the data from their in-situ moored station named WQBKN: PacIOOS Water Quality Buoy KN (WQB-KN): Kilo Nalu, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at Mānoa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-KN is located at the Kilo Nalu Nearshore Reef Observatory, near Kakaako Waterfront Park and Kewalo Basin off of Ala Moana Boulevard in Honolulu. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) NOAA_NCEI STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) ALL STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary -gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) ALL STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) NOAA_NCEI STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary +gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) ALL STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary gov.noaa.nodc:0232256_Not Applicable American Samoa Territorial Monitoring Program: Assessment of coral reef benthic and fish communities in American Samoa from 2005-03-10 to 2017-04-21 (NCEI Accession 0232256) NOAA_NCEI STAC Catalog 2005-03-10 2017-04-21 -170.563628, -14.364332, -170.812132, -14.252747 https://cmr.earthdata.nasa.gov/search/concepts/C2089380473-NOAA_NCEI.umm_json The data described here result from coral reef assessments of reef slopes (10m depth) at permanent sites around Tutuila, American Samoa as part of the ongoing American Samoa Coral Reef Monitoring Program (ASCRMP). These surveys were conducted by members of the American Samoa Coral Reef Advisory Group between 2005 and 2017. The data was collected via SCUBA surveys and reports on coral, benthic and fish composition and derived metrics (e.g., benthic cover, coral diversity, fish diversity, fish biomass). proprietary gov.noaa.nodc:0234331_Not Applicable Benthic foraminiferal assemblages, stable isotopes, and short-lived radioisotope measurements from sediment cores collected during the multiple cruises in the northwestern margin of Cuba and Gulf of Mexico from 2010-06-13 to 2017-07-19. (NCEI Accession 0234331) NOAA_NCEI STAC Catalog 2010-06-13 2017-07-19 -97.566, 18.631433, -82.339283, 29.701667 https://cmr.earthdata.nasa.gov/search/concepts/C2089380844-NOAA_NCEI.umm_json This dataset contains a compilation of seafloor surface benthic foraminifera assemblages, baseline stable carbon and oxygen isotope measurements from benthic foraminifera, and short-lived radioisotope measurements from sediment cores collected on multiple cruises and field sampling throughout the Gulf of Mexico and the northwestern margin of Cuba from 2010-06-13 to 2017-07-19. Stable isotope measurements were performed on Cibicidoides spp. The dataset includes the sediment core information such as location, date, and depth; benthic foraminiferal stable carbon and oxygen isotopes; and the total density and diversity calculations using Fisher’s Alpha and Shannon indices from the surface-most sub-sample from each core (typically 0-2 mm). For short-lived radioisotope measurements, samples were analyzed by gamma spectrometry with High-Purity Germanium (HPGe) gamma-ray detectors (Canberra Coaxial Planar configuration) for total 210Pb (46.5 keV), 214Pb (295 keV and 351 keV), and 214Bi (609 keV) activities. The mean activity of the 214Pb (295 keV), 214Pb (351 keV), and 214Bi (609 keV) was used as a proxy for 226Ra activity and therefore the supported 210Pb that is produced in situ. The reported excess 210Pb (210Pbxs) is the difference of the total 210Pb and the supported 210Pb. proprietary gov.noaa.nodc:0237816_Not Applicable Assessing cryptic reef diversity of colonizing marine invertebrates using Autonomous Reef Monitoring Structures (ARMS) deployed at coral reef sites in Kimbe Bay, Papua New Guinea from 2009-09-01 to 2012-09-12 (NCEI Accession 0237816) NOAA_NCEI STAC Catalog 2009-09-01 2012-09-12 150.126428, -5.308874, 150.131315, -5.28353 https://cmr.earthdata.nasa.gov/search/concepts/C2089381360-NOAA_NCEI.umm_json Autonomous Reef Monitoring Structures (ARMS) are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time. The data described here were collected by CREP from ARMS units moored at fixed climate survey sites located in Kimbe Bay, Papua New Guinea. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from September 2009 to September 2012, and three ARMS units were deployed by SCUBA divers at each survey site. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. Each ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms and scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 µm, and 100 µm. The 500 µm and 100 µm fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies. This dataset includes information on the species counted and identified in the 2 mm fraction. proprietary @@ -19059,8 +19127,8 @@ gov.noaa.nodc:6901098_Not Applicable Cloud amount/frequency, NITRATE and other d gov.noaa.nodc:7000052_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Prince William Sound (Gulf of Alaska) from 1986-12-15 to 1986-12-18 (NCEI Accession 7000052) NOAA_NCEI STAC Catalog 1986-12-15 1986-12-18 -150, 59, -149, 60.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089381217-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) NOAA_NCEI STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) ALL STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) ALL STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) NOAA_NCEI STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary +gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) ALL STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary gov.noaa.nodc:7001081_Not Applicable Characteristics of Sediments in the James River Estuary, Virginia, 1968 (NCEI Accession 7001081) NOAA_NCEI STAC Catalog 1966-04-01 1967-08-30 -77, 36.7, -76.15, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089382141-NOAA_NCEI.umm_json This report presents data on the physical and chemical characteristics of bottom sediments in the James River estuary, Virgina. The data were generated as part of a comprehensive study of sedimentation in which the initial objective was to broadly define the distribution of sediment properties. proprietary gov.noaa.nodc:7100000_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship DISCOVERER, JAMES COOK and other platforms from 1964-08-24 to 1971-11-17 (NCEI Accession 7100000) NOAA_NCEI STAC Catalog 1964-08-24 1971-11-17 -155.5, -66.7, 175.2, 50.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089383124-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7100048_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048) NOAA_NCEI STAC Catalog 1969-08-01 1969-08-31 -85, 7, -75, 12 https://cmr.earthdata.nasa.gov/search/concepts/C2089383261-NOAA_NCEI.umm_json Not provided proprietary @@ -19069,8 +19137,8 @@ gov.noaa.nodc:7100165_Not Applicable Chemical, physical, and other data collecte gov.noaa.nodc:7100603_Not Applicable Chemical, physical, and other data collected using bottle, BT, current meter, MBT, meteorological sensors, and secchi disk casts in the North Pacific Ocean as part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1968-01-01 to 1968-12-04 (NCEI Accession 7100603) NOAA_NCEI STAC Catalog 1968-01-01 1968-12-04 -122.9, 36.6, -121.9, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2089381029-NOAA_NCEI.umm_json Chemical, physical, and other data were collected using bottle, BT, current meter, MBT, meteorological sensors, and secchi disk casts from January 1, 1968 to December 4, 1968. Data were submitted by Stanford University; Hopkins Marine Station as part of the California Cooperative Fisheries Investigation (CALCOFI) project. Data were processed by NODC to the NODC standard F004 water physics and chemistry format. Full F004 Format descriptions are available from the NODC homepage at www.nodc.noaa.gov/. The F004 format contains data from measurements and analysis of physical and chemical characteristics of the water column. Chemical parameters that may be recorded are salinity, pH and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity and current velocity (east-west and north-south components). Cruise and station information may include environmental conditions of the study site at the time of observation. Data are very sparse prior to 1951. proprietary gov.noaa.nodc:7200096_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1968-02-23 to 1971-11-16 (NCEI Accession 7200096) NOAA_NCEI STAC Catalog 1968-02-23 1971-11-16 -86.4, 11, -61.1, 37.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089383889-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7200319_Not Applicable Cloud amount/frequency, NITRATE and other data from BELLOWS from 1972-02-02 to 1972-02-10 (NCEI Accession 7200319) NOAA_NCEI STAC Catalog 1972-02-02 1972-02-10 -85.4, 27.2, -82.8, 29.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089384562-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:7200320_Not Applicable AIR PRESSURE and Other Data from UNKNOWN PLATFORMS and Other Platforms from 1955-03-01 to 1970-08-13 (NCEI Accession 7200320) ALL STAC Catalog 1955-03-01 1970-08-13 -71.9, 29.4, 8.8, 65.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089384570-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7200320_Not Applicable AIR PRESSURE and Other Data from UNKNOWN PLATFORMS and Other Platforms from 1955-03-01 to 1970-08-13 (NCEI Accession 7200320) NOAA_NCEI STAC Catalog 1955-03-01 1970-08-13 -71.9, 29.4, 8.8, 65.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089384570-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:7200320_Not Applicable AIR PRESSURE and Other Data from UNKNOWN PLATFORMS and Other Platforms from 1955-03-01 to 1970-08-13 (NCEI Accession 7200320) ALL STAC Catalog 1955-03-01 1970-08-13 -71.9, 29.4, 8.8, 65.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089384570-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7200698_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1971-12-31 to 1972-05-06 (NCEI Accession 7200698) NOAA_NCEI STAC Catalog 1971-12-31 1972-05-06 -81.3, 17, -66.5, 37.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089381211-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7201127_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1972-06-25 to 1972-06-27 (NCEI Accession 7201127) NOAA_NCEI STAC Catalog 1972-06-25 1972-06-27 -76.7, 34, -75.8, 34.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089381653-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7201380_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1971-07-19 to 1972-11-04 (NCEI Accession 7201380) NOAA_NCEI STAC Catalog 1971-07-19 1972-11-04 -80.7, 30.4, -72.7, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089382013-NOAA_NCEI.umm_json Not provided proprietary @@ -19095,8 +19163,8 @@ gov.noaa.nodc:7600769_Not Applicable Cloud amount/frequency, NITRATE and other d gov.noaa.nodc:7601177_Not Applicable Cloud amount/frequency, NITRATE and other data from MURRE II in the NE Pacific from 1975-06-20 to 1976-03-29 (NCEI Accession 7601177) NOAA_NCEI STAC Catalog 1975-06-20 1976-03-29 -135.7, 58, -134.2, 58.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089384847-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7601212_Not Applicable BENTHIC SPECIES and Other Data from KANA KEOKI From Gulf of Mexico from 1974-10-26 to 1974-12-21 (NCEI Accession 7601212) NOAA_NCEI STAC Catalog 1974-10-26 1974-12-21 -100, 17, -81, 31.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089384895-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7601237_Not Applicable Chemical and physical data from thermistor, fluorometer, and bottle casts in the Patuxent River from 1972-10-15 to 1972-10-19 (NCEI Accession 7601237) NOAA_NCEI STAC Catalog 1972-10-15 1972-10-19 -76.7, 38, -76.7, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2089384911-NOAA_NCEI.umm_json "The Patuxent River estuary was investigated over a 25-hour tidal cycle from October 17-18, 1972, during the Patuxent River Cooperative Study (conducted by the University of Maryland). These data were collected as part of a joint investigation by the University of Maryland's Center for Environmental and Estuarine Studies (Chesapeake Biological Lab) and the Institute for Fluid Dynamics and Applied Mathematics (College Park, Maryland). The resulting chemical, physical, and biological data were assembled into a format that could be utilized by investigators, collectively titled the Patuxent River Data Bank. The Patuxent River Data Bank was submitted to NODC on a 9-track, 1600 BPI tape in EBCDIC and contains headers and one data file. Heat concentration (in kilocalories/liter) and instantaneous flux magnitude (in megacalories/square meter/second) were recorded over the tidal cycle. Other data associated with this study are filed under NODC Reference #'s L01574 and L01576; all data are in the Level-A directory under L01574.001. Data associated with marine chemistry include: Dissolved organic carbon (milligrams/liter), Particulate carbon (milligrams/liter), salts (grams/liter), Dissolved oxygen (milligrams/liter), and total particulates (milligrams/liter). Instantaneous flux magnitudes for carbon were measured in grams/liter; for salts, in kilograms/liter; for oxygen, in milligrams/liter; and for total particulates, milligrams/liter. Parameters associated with primary productivity (L505) include: Nitrate +Nitrite conc., Ammonia Nitrogen conc., Total Kjeldahl Nitrogen, Organic Phosphate conc., Total Hydrolyzable Phosphate, Active Chlorophyll-a, and Total Chlorophyll. Nutrients were measured in milligrams/liter; chlorophyll concentrations were measured in micrograms/liter. Instantaneous flux magnitudes were measured in milligrams/square meter/second. Additional data collected during this investigation are filed under NODC Reference #'s L01575 and one tape of Patuxent River Estuary Hydro data ""OLD STUFF""" proprietary -gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) ALL STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) NOAA_NCEI STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary +gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) ALL STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary gov.noaa.nodc:7601642_Not Applicable Bacteria, taxonomic code, and other data collected from G.W. PIERCE in North Atlantic Ocean from sediment sampler; 1976-02-20 to 1976-03-23 (NCEI Accession 7601642) NOAA_NCEI STAC Catalog 1976-02-20 1976-03-23 -75.3, 37.1, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089384806-NOAA_NCEI.umm_json Bacteria, taxonomic code, and other data were collected using sediment sampler and other instruments in the North Atlantic Ocean from G.W. PIERCE. Data were collected from 20 February 1976 to 23 March 1976 by Virginia Institute of Marine Science in Gloucester Point with support from the Ocean Continental Shelf - Mid Atlantic (OCS-Mid Atlantic) project. proprietary gov.noaa.nodc:7601772_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship OREGON II in the NW Atlantic from 1976-02-20 to 1976-02-25 (NCEI Accession 7601772) NOAA_NCEI STAC Catalog 1976-02-20 1976-02-25 -74.4, 36.8, -72.6, 38.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089384997-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7617993_Not Applicable Cloud amount/frequency, NITRATE and other data from CAPRICORNE from 1974-07-25 to 1974-08-10 (NCEI Accession 7617993) NOAA_NCEI STAC Catalog 1974-07-25 1974-08-10 -10.3, -2.2, -3.9, 4.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385626-NOAA_NCEI.umm_json Not provided proprietary @@ -19104,8 +19172,8 @@ gov.noaa.nodc:7617994_Not Applicable Cloud amount/frequency, NITRATE and other d gov.noaa.nodc:7617995_Not Applicable Cloud amount/frequency, NITRATE and other data from A. V. HUMBOLDT from 1974-07-28 to 1974-08-17 (NCEI Accession 7617995) NOAA_NCEI STAC Catalog 1974-07-28 1974-08-17 -25, -1.5, -23.4, 1.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385645-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) ALL STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) NOAA_NCEI STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary -gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) ALL STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) NOAA_NCEI STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary +gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) ALL STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary gov.noaa.nodc:7700437_Not Applicable Cloud amount/frequency, NITRATE and other data from CHAIN from 1973-03-11 to 1973-07-06 (NCEI Accession 7700437) NOAA_NCEI STAC Catalog 1973-03-11 1973-07-06 -72.6, 26.3, -66.8, 33.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386094-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7700455_Not Applicable BENTHIC SPECIES and Other Data from GILLISS and Other Platforms from 1975-10-27 to 1976-08-27 (NCEI Accession 7700455) NOAA_NCEI STAC Catalog 1975-10-27 1976-08-27 -75.3, 37.1, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089386131-NOAA_NCEI.umm_json Data was submitted by Dr. Gerald L. Engel. This study was organized to collect data on Parasite Type and Location. Parasite (both ecto- and endo-), and site of infection were looked into. SST, wave, turbidity, gear type (trawl), species, parasite (both ecto- and endo-), and site of infection (i.e. data on parasite type and location) data were collected. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS). Special codes employed by VIMS to describe parasite types and location were included as hardcopy. The original information submitted on tape has been converted into the current NODC storage format. proprietary gov.noaa.nodc:7700456_Not Applicable BENTHIC SPECIES and Other Data from GILLISS and Other Platforms from 1976-06-14 to 1976-09-02 (NCEI Accession 7700456) NOAA_NCEI STAC Catalog 1976-06-14 1976-09-02 -75.3, 37.5, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089386139-NOAA_NCEI.umm_json "Data submitted by Dr. Gerald L. Engel. The data was collected between June 1976 and September 1976. This study was organized to collect Histopathology and Benthic data. SST, wave, turbidity, gear type (trawl v.s dredge), benthic species counts and weights were collected. These data are ""megabenthic"" species. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. The original data on tape has been converted to current NODC storage format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS)." proprietary @@ -19335,8 +19403,8 @@ gov.noaa.nodc:9300147_Not Applicable Chlorophyll-a profiles collected by various gov.noaa.nodc:9300152_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship RAINIER in the NE Pacific from 1993-03-23 to 1993-07-31 (NCEI Accession 9300152) NOAA_NCEI STAC Catalog 1993-03-23 1993-07-31 -157.3, 56.7, -133.6, 57.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387756-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180). Data was collected from NOAA Ship RAINIER. The data was collected over a period spanning from March 23, 1993 to July 31, 1993. Data was submitted in a diskette by Capt. Russell Arnold, Pacific Marine Environmental Laboratory, Seattle, WA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary gov.noaa.nodc:9300161_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Chukchi Sea and Others from 1992-07-24 to 1992-10-27 (NCEI Accession 9300161) NOAA_NCEI STAC Catalog 1992-07-24 1992-10-27 -170.4, 53.6, -149.4, 71.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089387773-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska, Chukchi Sea, and NW Pacific (limit-180). Data was collected from cruises HX 163, HX 165 and HX 167 of Ship ALPHA HELIX. The data was collected over a period spanning from July 24, 1992 to october 27, 1992. Data was submitted in one exabyte cassette by Dr. Thomas C. Royer, Institute of Marine Science, University of Alaska, Fairbanks, AK. proprietary gov.noaa.nodc:9300187_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship WHITING in the Gulf of Mexico from 1992-04-02 to 1992-07-14 (NCEI Accession 9300187) NOAA_NCEI STAC Catalog 1992-04-02 1992-07-14 -92.9, 27.4, -91.8, 27.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387862-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Mexico by SEACATs deployed in the area. Data was collected from NOAA Ship WHITING during 7 casts. The data was collected over a period spanning from April 2, 1992 to July 14, 1992. Data was submitted in one diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary -gov.noaa.nodc:9300196_Not Applicable Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196) ALL STAC Catalog 1991-06-11 1993-03-22 -88, 17, -85, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089387904-NOAA_NCEI.umm_json Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation. proprietary gov.noaa.nodc:9300196_Not Applicable Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196) NOAA_NCEI STAC Catalog 1991-06-11 1993-03-22 -88, 17, -85, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089387904-NOAA_NCEI.umm_json Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation. proprietary +gov.noaa.nodc:9300196_Not Applicable Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196) ALL STAC Catalog 1991-06-11 1993-03-22 -88, 17, -85, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089387904-NOAA_NCEI.umm_json Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation. proprietary gov.noaa.nodc:9300199_Not Applicable Benthic and tissue toxin data from stations in U.S. coastal waters from 1984-01-01 to 1989-12-31 (NCEI Accession 9300199) NOAA_NCEI STAC Catalog 1984-01-01 1989-12-31 -123, 25, -67, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089387915-NOAA_NCEI.umm_json The accession contains Benthic and Tissue toxin data from stations in U.S. coastal waters (Coastal Waters of Western U.S. and North American Coastline-North) collected under the National Status and Trends (NS&T) program from 1984-1989. NS&T program for marine environmental quality was designed to define the geographic distribution of contaminant concentrations in tissues of marine organisms and sediments, and documenting biological responses to contamination. Samples have been collected under the original Benthic Surveillance Project (sediment and tissue samples from benthic fish) since 1984. Samples have been collected under the Mussel Watch Project (sediment and bivalves) since 1986. Both programs involved collecting samples from fixed sites on both Atlantic and Pacific coasts. Sites were selected so as not to be in close proximity to a major contamination source, as the programs objective was to quantify contamination over general areas. Chemical data from sediments collected during the benthic surveillance project, 1984-1986, is contained in a single delimited ASCII file (bssed.txt). Additional contaminated sediment data from the mussel watch program, 1986-1989, is contained in a single delimited ASCII file (mwsed.txt). These data do not include tissue analysis for contaminants. Chemicals and related parameters measured in sediments include: DDT. Since 1986, NOAA'S NS&T Program has included a component called the mussel watch project that has annually collected and chemically analyzed mussels and oysters from 177 sites at coastal and estuarine sites. Tissue samples from these mollusks have been analyzed to establish temporal trends of contaminant accumulation. Contaminants analyzed during this project include: polyaromatic hydrocarbons, polychlorinated biphenyls, chlorinated pesticides (such as ddt and its metabolites), aluminum, iron, manganese, silicon, other trace elements, and lipids. Tissue contaminant data from the mussel watch project, years 1986-1989, is contained in a single wordperfect 4.2 file, mollto90.txt. a second file, tbt_90.txt, lists the sum of concentrations of tributyl tin and its breakdown products (dibutyl tin and monobutyl tin) found in bivalve tissue samples. Tributylin (tbt) was previously used as an antifouling agent in paints, but its use on vessels under 75 feet was banned in 1988. A third file, mwsiteyr.txt, lists collection sites. proprietary gov.noaa.nodc:9400001_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship WHITING in the NW Atlantic from 1993-08-29 to 1993-11-21 (NCEI Accession 9400001) NOAA_NCEI STAC Catalog 1993-08-29 1993-11-21 -71.3, 41.4, -70.3, 41.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387925-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) SEACAT data was collected in NW Atlantic (limit-40 W). Data was collected during 17 casts from NOAA Ship WHITING. The data was collected over a period spanning from August 29, 1993 to November 21, 1993. Data was submitted in a diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary gov.noaa.nodc:9400010_Not Applicable BAROMETRIC PRESSURE and Other Data from SEAWARD EXPLORER From NW Atlantic (limit-40 W) from 1993-02-06 to 1993-08-28 (NCEI Accession 9400010) NOAA_NCEI STAC Catalog 1993-02-06 1993-08-28 -75.9, 34.5, -73.7, 36.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089388069-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W) as part of Physical Oceanography Field Program offshore North Carolina supported by grant MMS #14-35-0001-30599. Data was collected from Ship SEAWARD EXPLORER cruises SE9301, SE9303, and SE9309. The data was collected over a period spanning from February 6, 1993 and August 28, 1993. Data from 146 stations containing 7,614 records was submitted on a tape by Dr. Thomas Berger, Science Applications, Inc., Raleigh NC. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. proprietary @@ -19380,8 +19448,8 @@ gov.noaa.nodc:9500149_Not Applicable ALACE subsurface drifter data in South Paci gov.noaa.nodc:9500152_Not Applicable BAROMETRIC PRESSURE and Other Data from AURORA AUSTRALIS and Other Platforms from 1991-01-06 to 1992-03-06 (NCEI Accession 9500152) NOAA_NCEI STAC Catalog 1991-01-06 1992-03-06 67.5, -69.5, 135.4, -50.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089386699-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected from Ship AURORA AUSTRALIS. The data was collected over a period spanning from January 6, 1991 and March 6, 1992. Data from 343 casts containing 185,102 records was submitted via File Transfer Protocol by Ms. Edwina Tanner, Antarctic Cooperative Research Centre, University of Tasmania, Australia. proprietary gov.noaa.nodc:9500160_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Chukchi Sea from 1995-08-24 to 1995-09-01 (NCEI Accession 9500160) NOAA_NCEI STAC Catalog 1995-08-24 1995-09-01 163.988167, 66.665667, -168.998, 71.312667 https://cmr.earthdata.nasa.gov/search/concepts/C2089386823-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected from 73 stations in Chukchi Sea and East Siberian Sea area. The station numbers are 1-6, 8-30, 32-74, 76. Data was collected from Ship ALPHA HELIX cruise HX189. The data was collected BY Dr. J. Grebmeier of the University of Tennessee over a period spanning from August 24, 1995 to September 1, 1995. This project was funded by Office of Naval Research under grant no: NAVY N00014-94-1-1042Grebmeier. Data in NODC file format F022 was submitted by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks. proprietary gov.noaa.nodc:9600001_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Chukchi Sea from 1995-09-10 to 1995-10-08 (NCEI Accession 9600001) NOAA_NCEI STAC Catalog 1995-09-10 1995-10-08 160, 52, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2089386837-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Chukchi Sea as part of Office of Naval Research project. Data was collected from Ship ALPHA HELIX cruise HX-190. The data was collected over a period spanning from September 11, 1995 to October 8, 1995. Data was collected from 209 CTD stations by Institute of Marine Science, University of Alaska, Fairbanks, AK and was submitted by Dr Thomas Weingartner via File transfer Protocol in F022 file format of NODC. proprietary -gov.noaa.nodc:9600025_Not Applicable AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025) NOAA_NCEI STAC Catalog 1992-11-09 1993-02-24 158, -2, 158, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2089386973-NOAA_NCEI.umm_json The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data. proprietary gov.noaa.nodc:9600025_Not Applicable AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025) ALL STAC Catalog 1992-11-09 1993-02-24 158, -2, 158, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2089386973-NOAA_NCEI.umm_json The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data. proprietary +gov.noaa.nodc:9600025_Not Applicable AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025) NOAA_NCEI STAC Catalog 1992-11-09 1993-02-24 158, -2, 158, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2089386973-NOAA_NCEI.umm_json The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data. proprietary gov.noaa.nodc:9600039_Not Applicable Bacterial production, primary production, phytoplankton, zooplankton, biological analysis of fish, and massive fish length data from the EVRIKA and other platforms in the Antarctic from 23 February 1980 to 09 December 1988 (NCEI Accession 9600039) NOAA_NCEI STAC Catalog 1980-02-23 1988-12-09 -62.76, -63.98, -31.83, -50 https://cmr.earthdata.nasa.gov/search/concepts/C2089387013-NOAA_NCEI.umm_json Bacterial production, primary production, phytoplankton, zooplankton, biological analysis of fish, and massive fish length data were collected from the EVRIKA and other platforms in the Antarctic. Data were collected by the Atlantic Research Institute of Fishing Economy and Ocean from 23 February 1980 to 09 December 1988. proprietary gov.noaa.nodc:9600065_Not Applicable BAROMETRIC PRESSURE and Other Data from THOMAS G. THOMPSON and Other Platforms From TOGA Area - Pacific (30 N to 30 S) from 1992-10-13 to 1992-12-13 (NCEI Accession 9600065) NOAA_NCEI STAC Catalog 1992-10-13 1992-12-13 -149.389635, -17.193678, -134.31313, 12.067383 https://cmr.earthdata.nasa.gov/search/concepts/C2089387122-NOAA_NCEI.umm_json The data in this accession was collected as part of Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) in TOGA Area - Pacific (30 N to 30 S) using Ship THOMAS G. THOMPSON. CTD Data were collected by University of Washington, Seattle, WA between October 13, 1992 and December 13, 1992. Five Files of CTD data were submitted by Dr. Wilford Gardner. Good documentation accompanies this data. proprietary gov.noaa.nodc:9600140_Not Applicable BAROMETRIC PRESSURE and Other Data from NOAA Ship ALBATROSS IV and Other Platforms From NW Atlantic (limit-40 W) from 1995-02-11 to 1995-07-20 (NCEI Accession 9600140) NOAA_NCEI STAC Catalog 1995-02-11 1995-07-20 -69.237, 40.413, -65.647, 42.335 https://cmr.earthdata.nasa.gov/search/concepts/C2089387550-NOAA_NCEI.umm_json Hydrochemical, hydrophysical, and other data were collected from the ENDEAVOR and NOAA Ship ALBATROSS IV from February 11, 1995 to July 20, 1995. Data were submitted by Dr. David Mountain from the US DOC; NOAA; NATIONAL MARINE FISHERIES SERVICE - WOODS HOLE. These data were collected using meteorological sensors, secchi disks, transmissometers, bottle casts, and CTD casts in the Northwest Atlantic Ocean. proprietary @@ -19420,14 +19488,14 @@ gov.noaa.nodc:9800199_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data gov.noaa.nodc:9900010_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From Arabian Sea from 1995-03-18 to 1997-08-13 (NCEI Accession 9900010) NOAA_NCEI STAC Catalog 1995-03-18 1997-08-13 56.5, 10, 68.8, 24.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387251-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900014_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From Arabian Sea from 1995-01-09 to 1995-09-12 (NCEI Accession 9900014) NOAA_NCEI STAC Catalog 1995-01-09 1995-09-12 57.3, 10, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387273-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900015_Not Applicable CARBON DIOXIDE - PARTIAL PRESSURE (pCO2) - SEA and Other Data from NOAA Ship DISCOVERER and Other Platforms from 1987-05-19 to 1994-01-07 (NCEI Accession 9900015) NOAA_NCEI STAC Catalog 1987-05-19 1994-01-07 -179.9, -70.3, 179.9, 54.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089387289-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:9900022_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022) NOAA_NCEI STAC Catalog 1998-08-01 1998-12-31 -124.1, 44.6, -124, 44.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089387361-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900022_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022) ALL STAC Catalog 1998-08-01 1998-12-31 -124.1, 44.6, -124, 44.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089387361-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:9900022_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022) NOAA_NCEI STAC Catalog 1998-08-01 1998-12-31 -124.1, 44.6, -124, 44.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089387361-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900054_Not Applicable Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054) NOAA_NCEI STAC Catalog 1992-01-02 1992-12-31 -170.8, -14.4, -170.6, -14.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387610-NOAA_NCEI.umm_json Data from a 1992 survey of the American Samoa coral reef ecosystem was received from Dr. Barry Smith of the University of Guam. The digital files replace a paper report submitted to NODC in Fall 1998. This study was part of the American Samoa Coastal Resources Inventory (ASCRI), partly funded by Sea Grant. His component of the study focuses on a systematic inventory of conspicuous marine macro-invertebrates observations. proprietary gov.noaa.nodc:9900054_Not Applicable Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054) ALL STAC Catalog 1992-01-02 1992-12-31 -170.8, -14.4, -170.6, -14.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387610-NOAA_NCEI.umm_json Data from a 1992 survey of the American Samoa coral reef ecosystem was received from Dr. Barry Smith of the University of Guam. The digital files replace a paper report submitted to NODC in Fall 1998. This study was part of the American Samoa Coastal Resources Inventory (ASCRI), partly funded by Sea Grant. His component of the study focuses on a systematic inventory of conspicuous marine macro-invertebrates observations. proprietary gov.noaa.nodc:9900094_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094) NOAA_NCEI STAC Catalog 1999-01-01 1999-04-29 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387865-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900094_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094) ALL STAC Catalog 1999-01-01 1999-04-29 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387865-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) ALL STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) NOAA_NCEI STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) ALL STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900158_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from OCEANUS and Other Platforms from 1993-03-12 to 1993-03-23 (NCEI Accession 9900158) NOAA_NCEI STAC Catalog 1993-03-12 1993-03-23 -67.2, 31.7, -64.1, 36.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089388472-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) NOAA_NCEI STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) ALL STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary @@ -19978,8 +20046,8 @@ jornada_albedo_667_1 PROVE Surface albedo of Jornada Experimental Range, New Mex jornada_canopy_brf_668_1 PROVE Vegetation Reflectance of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-23 1997-05-28 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804797176-ORNL_CLOUD.umm_json Directional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997. proprietary jornada_landcover_lai_665_1 PROVE Land Cover and Leaf Area of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-13 1997-05-31 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804794793-ORNL_CLOUD.umm_json Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the PROVE exercise on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover. proprietary jornada_mquals_666_1 PROVE MQUALS Reflectance at Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-23 1997-05-25 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804795305-ORNL_CLOUD.umm_json This study utilized low flying, aircraft-based radiometers for optical characterization of top-of-the-canopy reflectance at Jornada Experimental Range in New Mexico during the Prototype Validation Experiment (PROVE) in May 1997. The objective was to examine the usefulness of low-flying aircraft for Moderate Resolution Imaging Spectroradiometer (MODIS) validation of land products. proprietary -joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers SCIOPS STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers ALL STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary +joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers SCIOPS STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary kakqimpacts_1 KAKQ NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -82.1814, 32.8531, -71.8333, 41.115 https://cmr.earthdata.nasa.gov/search/concepts/C1995580744-GHRC_DAAC.umm_json The KAKQ NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary kalahari_aot_h2o_vapor_719_1 SAFARI 2000 AOT and Column Water Vapor, Kalahari Transect, Wet Season 2000 ORNL_CLOUD STAC Catalog 2000-03-03 2000-03-18 21.72, -24.17, 25.5, -18.65 https://cmr.earthdata.nasa.gov/search/concepts/C2788397022-ORNL_CLOUD.umm_json The data presented here include the aerosol optical thickness (AOT) and column water vapor measurements taken at sites along the Kalahari Transect using a Microtops sunphotometer. Data were collected every 30 minutes at 4 sites that were visited during the SAFARI 2000 Kalahari Wet Season Campaign between March 3, 2000, and March 18, 2000. AOT values are provided at 340-, 440-, 675-, 870-, and 936-nm wavelengths. An estimate of the Angstrom Coefficient is also provided to allow the estimation of AOT at other wavelengths. The purpose of this data collection was primarily for documentation of the conditions at each site and to aid in the correction of remote sensing data, for validation of Earth Observation System (EOS) products such as MODIS and MISR aerosol products, and for modeling of canopy productivity. proprietary kalahari_co2_heat_flux_765_1 SAFARI 2000 Kalahari Transect CO2, Water Vapor, and Heat Flux, Wet Season 2000 ORNL_CLOUD STAC Catalog 2000-03-01 2000-03-19 21.71, -24.16, 23.59, -15.44 https://cmr.earthdata.nasa.gov/search/concepts/C2789074715-ORNL_CLOUD.umm_json Short-term measurements of carbon dioxide, water, and energy fluxes were collected at four locations along a mean annual precipitation gradient in southern Africa during the SAFARI 2000 wet (growing) season campaign of 2000. The purpose of this research was to determine how observed vegetation-atmosphere exchange properties are functionally related to long-term climatic conditions. proprietary @@ -20069,13 +20137,13 @@ larsemann_hills_dem_1 Digital Elevation Model of Larsemann Hills, Antarctica AU_ larsemann_sat_1 Larsemann Hills Satellite Image Map 1:25000 AU_AADC STAC Catalog 1990-08-01 1990-08-31 75.971, -69.489, 76.411, -69.324 https://cmr.earthdata.nasa.gov/search/concepts/C1214313531-AU_AADC.umm_json Satellite image map of Larsemann Hills, Princess Elizabeth Land, Antarctica. This map (edition 2) was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990. The map is at a scale of 1:25000, and was produced from a multispectral SPOT 1 - HRV 2 scene (WRS K278 J495), acquired 19 February 1988. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary larsemann_visible_disturbance_1 Annotated maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica AU_AADC STAC Catalog 1990-01-01 2000-05-09 76.07, -69.47, 76.42, -69.37 https://cmr.earthdata.nasa.gov/search/concepts/C1214313590-AU_AADC.umm_json Annotated large format maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica. The compilation was done by Ewan McIvor of the Australian Antarctic Division and based on discussions with scientists Jim Burgess and Andy Spate. Included are locations and notes relating to: 1 walking and vehicular routes; 2 helicopter landing sites; 3 a tide gauge; 4 a fuel line; 5 a grave site; 6 a long term micro erosion monitoring site established in 1990 by Burgess and Spate; 7 two ice caves; and 8 a pliocene deposit. proprietary larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0 Larval food composition of four wild bee species in five European cities ENVIDAT STAC Catalog 2021-01-01 2021-01-01 0.2197266, 46.890732, 28.3886719, 59.0864909 https://cmr.earthdata.nasa.gov/search/concepts/C2789815269-ENVIDAT.umm_json Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition. proprietary -latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ALL STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." proprietary latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." proprietary +latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ALL STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." proprietary law_dome_1977_1 Law Dome Field Logs And Strain Grid Results, 1977 AU_AADC STAC Catalog 1977-03-16 1977-12-14 110, -70, 114, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311164-AU_AADC.umm_json In 1977 several traverses were carried out over the Law Dome area, primarily to drill new ice cores on the dome. The 1974 drill site (near Cape Folger) was redrilled to add instrumentation for inclination, while additional holes at BHQ (418m) and the dome summit (475m, 2x 30m) were also drilled. In addition to the drilling work, two strain grids were laid out on the ice surface, and the grid laid out in 1974 was remeasured. Notes on the traverse and drilling (but few results) are contained in this record, along with the results of the strain grid surveys. Records for this work have been archived at the Australian Antarctic Division. Logbook(s): Glaciology Log of 1977 Field Work proprietary law_dome_700yr_ion_chem_2 700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica AU_AADC STAC Catalog 1988-01-01 2000-03-06 112.8, -66.76, 112.86, -66.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main. This work was completed as part of ASAC project 757 (ASAC_757). Species which have been the subject of publication and could be made available after consultation: Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full) NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help. An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012. proprietary law_dome_700yr_ion_chem_2 700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica ALL STAC Catalog 1988-01-01 2000-03-06 112.8, -66.76, 112.86, -66.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main. This work was completed as part of ASAC project 757 (ASAC_757). Species which have been the subject of publication and could be made available after consultation: Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full) NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help. An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012. proprietary -law_dome_700yr_na_1 700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome ALL STAC Catalog 1301-01-01 1995-12-31 112.806946, -66.76972, 112.806946, -66.76972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below. proprietary law_dome_700yr_na_1 700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome AU_AADC STAC Catalog 1301-01-01 1995-12-31 112.806946, -66.76972, 112.806946, -66.76972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below. proprietary +law_dome_700yr_na_1 700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome ALL STAC Catalog 1301-01-01 1995-12-31 112.806946, -66.76972, 112.806946, -66.76972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below. proprietary law_dome_gravity_1964_1968_1 Gravity Measurements on Law Dome, 1964-1968 AU_AADC STAC Catalog 1964-01-01 1968-12-31 110, -68, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311151-AU_AADC.umm_json A compilation of gravity measurements taken on Law Dome from 1964-1968. The hard copy of this document has been archived in the Australian Antarctic Division Records Store. proprietary law_dome_gravity_1971_1 Gravity Observations on Law Dome, 1971-1972 AU_AADC STAC Catalog 1971-01-01 1972-12-31 110, -68, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311152-AU_AADC.umm_json Log of gravity observations made on Law Dome in 1971 and 1972. The hard copy of this document has been archived in the Australian Antarctic Division Records Store. proprietary law_dome_gravity_1981_1 Gravity Measurements on Law Dome, Spring Traverse 1981 AU_AADC STAC Catalog 1981-09-26 1981-12-30 110, -69, 120, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1292615128-AU_AADC.umm_json Gravity measurements taken on Law Dome and Wilkes Land during the spring traverse in 1981. Many readings are taken at the same location at two different times (trip out, and trip back). Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary @@ -20210,8 +20278,8 @@ macquarie_aws_1 Automatic Weather Station Data from Macquarie Island AU_AADC STA macquarie_heli_zone_1 Macquarie Island Helicopter Exclusion Zone AU_AADC STAC Catalog 2005-01-01 2005-01-24 158.75, -54.8, 158.97, -54.46 https://cmr.earthdata.nasa.gov/search/concepts/C1214313628-AU_AADC.umm_json The Macquarie Island Helicopter Exclusion Zone was created in January 2005 in consultation with Peter Cusick, Parks and Wildlife Service, Tasmania. The zone was created by buffering the coastline by 1 km on the seaward side of the island, generally following the escarpment on the interior of the island and buffering the refuges by 200 m to create an approximately 400 m wide corridor to the refuges. Access corridors were also created at the station. The Australian Antarctic Data Centre's topographic data representing coastline, escarpment and refuges was used. In March 2007 the zone was modifed in consultation with Terry Reid, Parks and Wildlife Service, Tasmania. The corridors to the refuges were extended through to the escarpment. The Helicopter Exclusion Zone is shown in a map of the island (see link below). proprietary macquarie_quickbird_mapping_1 Macquarie Island mapping from Quickbird satellite imagery. AU_AADC STAC Catalog 2003-02-25 2003-06-20 158.85, -54.56, 158.94, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214313631-AU_AADC.umm_json Features of a northwest part of Macquarie Island mapped from mosaiced pan sharpened Quickbird satellite imagery derived from Quickbird satellite imagery captured on 25 February 2003. The mapped features are coastline, walking tracks and the edge of vegetation. proprietary macquarie_sma_gis_1 Macquarie Island Special Management Areas AU_AADC STAC Catalog 2003-11-01 2003-11-30 158.77, -54.78, 158.95, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214313610-AU_AADC.umm_json Macquarie Island Nature Reserve Special Management Areas were originally defined for 2003/04 and have since been updated. Special Management areas are declared from year to year to protect vulnerable species, vegetation communities or sites extremely vulnerable to human disturbance. Related URLs provide: 1 the download of a shapefile with the boundaries of the Special Management Areas; and 2 a link to the website of Parks and Wildlife Service, Tasmania with information about the Special Management Areas. proprietary -macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 AU_AADC STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 ALL STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary +macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 AU_AADC STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary macquarie_tracks_1 Macquarie Island walking tracks AU_AADC STAC Catalog 1997-09-01 2012-06-30 158.77, -54.78, 158.95, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214311191-AU_AADC.umm_json This GIS dataset represents walking tracks on Macquarie Island and was compiled by the Australian Antarctic Data Centre from surveys and other sources. This data is displayed in a pair of A3 1:50000 maps of Macquarie Island (see a Related URL). proprietary madagascar_diatoms MADAGASCAR National Oceanographic Data Centre - Diatoms CEOS_EXTRA STAC Catalog 2003-10-01 2004-10-31 43.61, -23.38, 43.68, -23.35 https://cmr.earthdata.nasa.gov/search/concepts/C2232477687-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset of diatoms has been collected at three stations in Toliara Bay, and it currently consists of 2754 records of 19 families. proprietary madagascar_dinoflagelles MADAGASCAR National Oceanographic Data Centre - Dinoflagellates CEOS_EXTRA STAC Catalog 2002-12-01 2003-12-31 43.61, -23.38, 43.68, -23.35 https://cmr.earthdata.nasa.gov/search/concepts/C2232477667-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset of dinoflagellates has been collected at three stations in Toliara Bay, and it currently consists of 1297 records of 15 families. proprietary @@ -20252,8 +20320,8 @@ measurement-data-sets-the-measured-potential-of-alpine-photovoltaics_1.0 Monitor medical_bibliography_1 A bibliography of polar medicine related articles AU_AADC STAC Catalog 1947-01-01 2007-06-06 60, -90, 160, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311212-AU_AADC.umm_json This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007. The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981. Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually. The publications are divided into the following areas: Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters The fields in this dataset are: Author Title Journal Year proprietary medical_bibliography_1 A bibliography of polar medicine related articles ALL STAC Catalog 1947-01-01 2007-06-06 60, -90, 160, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311212-AU_AADC.umm_json This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007. The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981. Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually. The publications are divided into the following areas: Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters The fields in this dataset are: Author Title Journal Year proprietary mega-plots_1.0 Towards comparable species richness estimates across plot-based inventories - data ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -14.0625, 33.1375512, 42.1875, 72.1818036 https://cmr.earthdata.nasa.gov/search/concepts/C2789816317-ENVIDAT.umm_json "The data file refers to the data used in Portier et al. ""Plot size matters: towards comparable species richness estimates across plot-based inventories"" (2022) *Ecology and Evolution*. This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/)." proprietary -mendocino_mathison_peak_nff_sr Airborne laser swath mapping (ALSM) data of the San Andreas fault SCIOPS STAC Catalog 2003-02-05 2003-02-11 -123.81387, 39.31092, -123.720085, 39.333496 https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json "This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory. Spatial Data Organization Information - Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1 Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222" proprietary mendocino_mathison_peak_nff_sr Airborne laser swath mapping (ALSM) data of the San Andreas fault ALL STAC Catalog 2003-02-05 2003-02-11 -123.81387, 39.31092, -123.720085, 39.333496 https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json "This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory. Spatial Data Organization Information - Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1 Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222" proprietary +mendocino_mathison_peak_nff_sr Airborne laser swath mapping (ALSM) data of the San Andreas fault SCIOPS STAC Catalog 2003-02-05 2003-02-11 -123.81387, 39.31092, -123.720085, 39.333496 https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json "This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory. Spatial Data Organization Information - Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1 Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222" proprietary met-obs-jmr-stations-1976_1 Meteorological Observations Made At JMR Stations 1976-1977 AU_AADC STAC Catalog 1976-01-01 1977-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313660-AU_AADC.umm_json During the Mirny-Dome C traverse in 1976/77, time was spent at a number of cane sites taking JMR measurements, to determine the precise location. During this time, basic meteorological observations of air temperature and pressure were made and recorded. These documents have been archived in the records store at the Australian Antarctic Division. proprietary met_profile_SA_729_1 SAFARI 2000 Upper Air Meteorological Profiles, South Africa, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-01 2000-09-30 -10, -41, 31, -24 https://cmr.earthdata.nasa.gov/search/concepts/C2789021046-ORNL_CLOUD.umm_json The University of Wyoming has a series of balloonborne radiosonde measurements from all around the world, from the surface to 30 km. This data set contains upper air meteorological profiles from 594 radiosonde launches deployed from sites in South Africa. These sonde launches were made to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaign of 2000.Vaisala RS80 sondes were launched from nine sites in South Africa between August 1, 2000 and September 30, 2000. The launch sites were Pietersburg (changed to Polokwane after 2000), Pretoria (Irene), Bethlehem, Springbok, De Aar, Durban, Cape Town, Port Elizabeth, and Gough Island. The parameters measured by the radiosonde instruments include: pressure, air temperature, relative humidity, wind speed, and wind direction. proprietary met_profile_skukuza_728_1 SAFARI 2000 Upper Air Meteorological Profiles, Skukuza, Dry Seasons 1999-2000 ORNL_CLOUD STAC Catalog 1999-08-14 2000-09-23 31.59, -24.97, 31.59, -24.97 https://cmr.earthdata.nasa.gov/search/concepts/C2789020292-ORNL_CLOUD.umm_json Vaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000. The radiosondes were launched from Skukuza Airport between August 14-September 3, 1999, and between August 24-September 23, 2000. The radiosonde instrument package RS80 measured the following meteorological parameters: pressure in hecto-Pascals (P), ambient temperature in degrees Celsius (T), and relative humidity in percentage (RH). A hydrostatic equation was applied to the recorded data, after error-checking, to calculate the output parameters: height above sea level in meters, dew point temperature in degrees Celsius, and q (g/kg) which is specific humidity in grams per kilogram. proprietary @@ -20388,8 +20456,8 @@ npolimpacts_1 NASA S-Band Dual Polarimetric Doppler Radar (NPOL) IMPACTS V1 GHRC ns0012bq_482_1 BOREAS NS001 TMS Level-2 Images: Reflectance and Temperature in BSQ Format ORNL_CLOUD STAC Catalog 1994-04-19 1994-09-16 -106.32, 53.42, -97.23, 56.25 https://cmr.earthdata.nasa.gov/search/concepts/C2929136513-ORNL_CLOUD.umm_json This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. Collection of the NS001 images occurred over the study areas during the 1994 field campaigns. The Level-2 NS001 data are atmospherically corrected versions of some of the best original NS001 imagery and cover the dates of 19-Apr-1994, 07-Jun-1994, 21-Jul-1994, 08-Aug-1994, and 16-Sep-1994. proprietary ns001bil_440_1 BOREAS NS001 TMS Level-0 Images in BIL Format ORNL_CLOUD STAC Catalog 1994-05-24 1994-09-19 -106.32, 53.42, -97.23, 56.25 https://cmr.earthdata.nasa.gov/search/concepts/C2929070415-ORNL_CLOUD.umm_json The NS001 TMS imagery, along with the other remotely sensed images, was collected in order to provide spatially extensive information over the primary study areas. This information includes detailed land cover and biophysical parameter maps such as fPAR and LAI. Data collections occurred over the study areas during the 1994 field campaigns. proprietary nsafcovr_252_1 BOREAS Forest Cover Layers of the NSA in Raster Format ORNL_CLOUD STAC Catalog 1988-01-01 1992-12-31 -98.82, 55.72, -97.83, 56.07 https://cmr.earthdata.nasa.gov/search/concepts/C2807622831-ORNL_CLOUD.umm_json Processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. proprietary -nsf0232042 Aeromagnetic and gravity data of the Central Transantarctic Mountains ALL STAC Catalog 2003-12-27 2004-01-25 139.27539, -83.95234, 170.21844, -82.35733 https://cmr.earthdata.nasa.gov/search/concepts/C2231555173-CEOS_EXTRA.umm_json Near complete coverage of the East Antarctic shield by ice hampers geological study of crustal architecture important for understanding global tectonic and climate history. Limited exposures in the central Transantarctic Mountains (CTAM), however, show that Archean and Proterozoic rocks of the shield as well as Neoproterozoic-lower Paleozoic sedimentary successions were involved in oblique convergence associated with Gondwana amalgamation. Subsequently, the area was overprinted by Jurassic magmatism and Cenozoic uplift. To extend the known geology of the region to ice-covered areas, we conducted an aeromagnetic survey flown in draped mode by helicopters over the Transantarctic Mountains and by fixed-wing aircraft over the adjacent polar plateau. We flew >32,000 line km covering an area of nearly 60,000 km2 at an average altitude of 600 m, with average line spacing 2.5 km over most areas and 1.25 km over basement rocks exposed in the Miller and Geologists ranges. Additional lines flown to true north, south and west extended preliminary coverage and tied with existing surveys. Gravity data was collected on the ground along a central transect of the helicopter survey area. From December 2003 to January 2004, the CTAM group flew a helicopter and twin-otter aeromagnetic survey and collected gravity station data on the ground in profile form. These data will be integrated with other geologic and geophysical data in order to extend the known geology of the region to ice-covered areas. proprietary nsf0232042 Aeromagnetic and gravity data of the Central Transantarctic Mountains CEOS_EXTRA STAC Catalog 2003-12-27 2004-01-25 139.27539, -83.95234, 170.21844, -82.35733 https://cmr.earthdata.nasa.gov/search/concepts/C2231555173-CEOS_EXTRA.umm_json Near complete coverage of the East Antarctic shield by ice hampers geological study of crustal architecture important for understanding global tectonic and climate history. Limited exposures in the central Transantarctic Mountains (CTAM), however, show that Archean and Proterozoic rocks of the shield as well as Neoproterozoic-lower Paleozoic sedimentary successions were involved in oblique convergence associated with Gondwana amalgamation. Subsequently, the area was overprinted by Jurassic magmatism and Cenozoic uplift. To extend the known geology of the region to ice-covered areas, we conducted an aeromagnetic survey flown in draped mode by helicopters over the Transantarctic Mountains and by fixed-wing aircraft over the adjacent polar plateau. We flew >32,000 line km covering an area of nearly 60,000 km2 at an average altitude of 600 m, with average line spacing 2.5 km over most areas and 1.25 km over basement rocks exposed in the Miller and Geologists ranges. Additional lines flown to true north, south and west extended preliminary coverage and tied with existing surveys. Gravity data was collected on the ground along a central transect of the helicopter survey area. From December 2003 to January 2004, the CTAM group flew a helicopter and twin-otter aeromagnetic survey and collected gravity station data on the ground in profile form. These data will be integrated with other geologic and geophysical data in order to extend the known geology of the region to ice-covered areas. proprietary +nsf0232042 Aeromagnetic and gravity data of the Central Transantarctic Mountains ALL STAC Catalog 2003-12-27 2004-01-25 139.27539, -83.95234, 170.21844, -82.35733 https://cmr.earthdata.nasa.gov/search/concepts/C2231555173-CEOS_EXTRA.umm_json Near complete coverage of the East Antarctic shield by ice hampers geological study of crustal architecture important for understanding global tectonic and climate history. Limited exposures in the central Transantarctic Mountains (CTAM), however, show that Archean and Proterozoic rocks of the shield as well as Neoproterozoic-lower Paleozoic sedimentary successions were involved in oblique convergence associated with Gondwana amalgamation. Subsequently, the area was overprinted by Jurassic magmatism and Cenozoic uplift. To extend the known geology of the region to ice-covered areas, we conducted an aeromagnetic survey flown in draped mode by helicopters over the Transantarctic Mountains and by fixed-wing aircraft over the adjacent polar plateau. We flew >32,000 line km covering an area of nearly 60,000 km2 at an average altitude of 600 m, with average line spacing 2.5 km over most areas and 1.25 km over basement rocks exposed in the Miller and Geologists ranges. Additional lines flown to true north, south and west extended preliminary coverage and tied with existing surveys. Gravity data was collected on the ground along a central transect of the helicopter survey area. From December 2003 to January 2004, the CTAM group flew a helicopter and twin-otter aeromagnetic survey and collected gravity station data on the ground in profile form. These data will be integrated with other geologic and geophysical data in order to extend the known geology of the region to ice-covered areas. proprietary nuclear-microsatellite-genotypes-of-pinus-cembra-from-fribourg-and-the-alps_1.0 Nuclear microsatellite genotypes of Pinus cembra ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.229492, 43.691708, 15.88623, 48.019324 https://cmr.earthdata.nasa.gov/search/concepts/C3383777020-ENVIDAT.umm_json The dataset contains individual genotypes at 11 nuclear microsatellite markers of samples of Swiss stone pine (*Pinus cembra*). Samples were collected in 12 natural and presumably planted stands in the canton of Fribourg (Switzerland). The dataset is complemented by individual genotypes (same markers) from samples of a subset of 40 populations across the European Alps (taken from Gugerli et al., Journal of Biogeography 2023; https://doi.org/10.1111/jbi.14586; dataset archived at Dryad, https://doi.org/10.5061/dryad.866t1g1v6). proprietary nuclear-microsatellite-genotypes-of-the-butterfly-melanargia-galathea_1.0 Nuclear microsatellite genotypes of the butterfly Melanargia galathea ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383775617-ENVIDAT.umm_json Individual genotypes assessed at six nuclear microsatellite loci (two alleles per locus) for individuals of the butterfly Melanargia galathea (Marbled white) that were collected throughout Switzerland, along the regular grid of the Biodiversity Monitoring (BDM) of Switzerland. For each individual, the sampling site (number) and the genotype are given. Note that due to privacy restrictions, the original geographic coordinates remain disclosed. For original coordinates, data holders should be contacted (Swiss Federal Office for the Environment; Biodiversity Monitoring Switzerland). The data refer to the following publication: Terzer et al., Distinct spatial patterns of genetic structure and diversity in the butterfly Marbled White (Melanargia galathea) inhabiting fragmented grasslands. Conservation Genetics, https://doi.org/0.1007/s10592-023-01593-4. proprietary number-of-natural-hazard-fatalities-per-year-in-switzerland-since-1946_1.0 Number of natural hazard fatalities per year in Switzerland since 1946 ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.6469727, 45.767523, 10.579834, 47.864774 https://cmr.earthdata.nasa.gov/search/concepts/C2789816460-ENVIDAT.umm_json This dataset contains the number of fatalities due to flood, debris flow, landslide, rockfall, windstorm, lightning, ice avalanche, earthquake and other processes like roof avalanche or lacustrine tsunami for each year since 1946. The following information is contained (by column and column title): * year * total number of hazard fatalities * number of fatalities by flood (German: Hochwasser, Überschwemmung). Flood includes people drowned in flooded or inundated areas or carried away in streams under high-water conditions. * number of fatalities by debris flow (German: Murgang). * number of fatalities by landslide (German: Erdrutsch). Landslide includes people killed by landslides and hillslope debris flows (German: Hangmure). * number of fatalities by rockfall (German: Steinschlag, Fels- und Bergsturz). * number of fatalities by windstorm (German: Sturm). Windstorm includes people killed by falling objects or trees during very strong wind conditions and people who drowned in lakes because their boat capsized during such conditions. * number of fatalities by lightning (German: Blitz). * number of fatalities by ice avalanche (German: Eislawine). * number of fatalities by earthquake (German: Erdbeben). * number of fatalities by other processes like roof avalanche, lacustrine tsunami (German: andere Prozesse wie Dachlawine, Tsunami im See). The data was collected based on newspaper research. For more information please refer to _Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747-2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016._ The data collection is financed by the FOEN (with exception of the collection of the avalanche fatalities). The data contains the official statistics of the FOEN on fatalities due to flood, debris flow, landslide, rock fall and avalanche. __Restrictions: The data set is not complete.__ Only fatalities in or around settlements and on open transportation routes are included. More precisely, fatalities were not collected, when persons exposed themselves to a great danger on purpose. Or fatalities during leisure activities which are connected to a higher risk were not included (this includes e.g. canoeing or river surfing during flood, canyoning, mountaineering, climbing, walking or driving on a closed road). Fatalities by avalanches are collected at the WSL Institute for Snow and Avalanche Research SLF. You can download the avalanche fatalities per hydrological year [here](https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936) and per calendar year [here](https://www.envidat.ch/dataset/avalanche-fatalities-per-calendar-year-since-1936). For a direct comparison with the fatalities presented here, please download the data set with the calendar years and do not consider fatalities in the backcountry (tour) or in terrain close to ski areas (offpiste). proprietary @@ -20400,11 +20468,11 @@ number_of_woody_species_gt_12_cm_dbh-41_1.0 Number of woody species (>= 12 cm DB number_of_young_forest_plants_by_damage-209_1.0 Number of young forest plants by damage ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789816738-ENVIDAT.umm_json Number of regeneration trees starting at 10 cm height up to 11.9 cm dbh with a particular type of damage or with no damage. The attribute is recorded by targeting the next regeneration tree in the centre of the subplot during NFI’s regeneration survey. A regeneration tree may have more than one type of damage, which means it may contribute to the total number of regeneration trees for several different types of damage. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary nutrient-addition-stillberg_1.0 Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.867544, 46.7716544, 9.867544, 46.7716544 https://cmr.earthdata.nasa.gov/search/concepts/C3226082769-ENVIDAT.umm_json # Background information The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short‐term fertilisation trials persisted in a longer‐term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation. Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha−1 a−1 was added annually to plots containing 30‐year‐old *Larix decidua* or 32‐year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed. # Experimental design Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha−1 a−1). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* Möhl et al (2019). # Data description The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here: proprietary nutrient-sustainability-in-beech-forests_1.0 Nutrient sustainability in beech forests ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383776137-ENVIDAT.umm_json With this study, our aim was to estimate the nutrient fluxes relevant for assessing nutrient sustainability as accurately as possible and to calculate nutrient balances for alternative forest management scenarios. Furthermore, we tested whether mapping units from existing geologic maps can serve as a basis for forest practitioners to estimate nutrient sustainability or whether more detailed data is needed. Positive fluxes include deposition and weathering, while negative fluxes include losses due to leaching and nutrient removal through timber harvesting in the balance. Weathering and leachate losses were modeled with a geochemical model. The SwissStandSim model was used to simulate the biomass growth under different harvesting and silvicultural strategies, allowing sustainability to be assessed for each nutrient at a given intensity of use. This assessment was made per rotation period of 100 years based on two criteria: i) nutrient supply; and ii) total stocking volume. proprietary -nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley CEOS_EXTRA STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley ALL STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary +nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley CEOS_EXTRA STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary nymesoimpacts_1 New York State Mesonet IMPACTS GHRC_DAAC STAC Catalog 2020-01-03 2023-03-02 -79.6375, 40.594, -72.1909, 44.9057 https://cmr.earthdata.nasa.gov/search/concepts/C1995873777-GHRC_DAAC.umm_json The New York State Mesonet IMPACTS dataset is browse-only. It consists of temperature, wind, wind direction, mean sea level pressure, precipitation, and snow depth measurements, as well as profiler Doppler LiDAR and Microwave Radiometer (MWR) measurements from the New York State Mesonet network during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The Mesonet network consists of ground weather stations, LiDAR profilers, and microwave radiometer (MWR) profilers. These browse files are available from January 3, 2020, through March 2, 2023, in PNG format. proprietary -obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands ALL STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands AU_AADC STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary +obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands ALL STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary observational-data-switzerland-2016-2021_1.0 Observational data: avalanche observations, danger signs and stability test results, Switzerland (2016/2017 to 2020/2021 ) ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815389-ENVIDAT.umm_json This is the freely available part of the data used in the publication by Techel et al. (2022): _On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger_ - danger signs - human triggered avalanches - rutschblock test results (still to be added) - extended column test results (still to be added) proprietary observed-and-simulated-snow-profile-data-from-switzerland_1.0 Observed and simulated snow profile data ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082908-ENVIDAT.umm_json This data set includes information on all observed and simulated snow profiles that were used to train and validate the random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified. The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model. proprietary observer-driven-pseudoturnover-in-vegetation-surveys_1.0 Observer-driven pseudoturnover in vegetation surveys ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815537-ENVIDAT.umm_json "This dataset was used to analyze the inter-observer error (i.e. pseudoturnover) in vegetation surveys for the publication Boch S, Küchler H, Küchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science. In the framework of the project ""Monitoring the effectiveness of habitat conservation in Switzerland"", we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover. The excel file contains two sheets: 1) Raw species lists of the 224 plots conducted by two different observers. Woody species are distinguished in three layers: H (herb layer; woody species <0.5 m in height), S (shrub layer; woody species 0.5–3 m in height) and T (tree layer; woody species >3 m in height). ""cf."" indicates uncertain identification, ""aggr."" indicates that the plant was identified only to the aggregate level. Cover was estimated for each species using a modified Braun-Blanquet scale (r ≙ <0.1%, + ≙ 0.1% to <1%, 1 ≙ 1% to <5%, 2 ≙ 5% to <25%, 3 ≙ 25% to <50%, 4 ≙ 50% to <75%, 5 ≙ 75% to <100%). 2) File used for the linear mixed effects model." proprietary @@ -20421,8 +20489,8 @@ orbview_3 Orbview-3 USGS_LTA STAC Catalog 2003-01-01 2007-12-31 -180, -90, 180, oriental-beech-spectral-and-trait-data_1.0 Oriental and European beech spectral, traits and genetics data ENVIDAT STAC Catalog 2023-01-01 2023-01-01 7.35, 48.65, 7.35, 48.65 https://cmr.earthdata.nasa.gov/search/concepts/C3226082588-ENVIDAT.umm_json The dataset includes leaf spectroscopy, leaf traits and genetic data for oriental and european beech trees at two mature forest sites (Allenwiller in France and Wäldi in Switzerland) sampled in summer 2021 and 2022 for top and bottom of canopy leaves. proprietary ornl_lai_point_971_1 ISLSCP II Leaf Area Index (LAI) from Field Measurements, 1932-2000 ORNL_CLOUD STAC Catalog 1932-01-01 2000-12-31 -156.67, -54.5, 172.75, 71.3 https://cmr.earthdata.nasa.gov/search/concepts/C2784892799-ORNL_CLOUD.umm_json Leaf Area Index (LAI) data from the scientific literature, covering the period from 1932-2000, have been compiled at the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) to support model development and validation for products from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument. There is one data file which consists of a spreadsheet table, together with a bibliography of more than 300 original-source references. Although the majority of measurements are from natural or semi-natural ecosystems, some LAI values have been included from crops (limited to a sub-set representing different crops at different stages of development under a range of treatments). Like Net Primary Productivity (NPP), Leaf Area Index (LAI) is a key parameter for global and regional models of biosphere/atmosphere exchange. Modeling and validation of coarse scale satellite measurements both require field measurements to constrain LAI values for different biomes (typical minimum, maximum values, phenology, etc.). Maximum values for point measurements are unlikely to be approached or exceeded by area-weighted LAI, which is what satellites and true spatial models are estimating. proprietary otdlip_1 OPTICAL TRANSIENT DETECTOR (OTD) LIGHTNING V1 GHRC_DAAC STAC Catalog 1995-04-13 2000-03-23 -180, -70, 180, 70 https://cmr.earthdata.nasa.gov/search/concepts/C1979889849-GHRC_DAAC.umm_json The Optical Transient Detector (OTD) records optical measurements of global lightning events in the daytime and nighttime. The data includes individual point (lightning) data, satellite metadata, and several derived products. The OTD was launched on 3 April 1995 aboard the Microlab-1 satellite into a near polar orbit with an inclination of 70 degrees with respect to the equator, at an altitude of 740 km. proprietary -oxygen-isotopes-plateau-1984_1 7 year oxygen isotope results from samples taken on Antarctic Plateau traverse, 1984 AU_AADC STAC Catalog 1978-01-01 1984-12-31 100, -75, 130, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313700-AU_AADC.umm_json A total of nine stations were sampled for oxygen isotopes during the 1984 spring traverse to the Antarctic Plateau. The aim of this program was to take a number of samples from a core or a pit, at stations of known accumulation over a particular period, to see how far inland the annual cycles could accurately be traced. The samples were not taken at ice movement stations, but at canes each 2km along the line, to avoid sampling the accumulation, and thus isotope disturbance resulting from parking the vans beside the IMS poles in 1978 and 1979. The accumulation for the cane at each sampled station was calculated for the six years since 1978, and the total multiplied by 7/6 to give the sampling depth required to cover 7 years. Seventy samples were taken at each station, i.e. approximately 10 per year. At most stations a PICO drill was used to obtain a core, and the samples cut with a stainless steel knife on the stainless sink in the living van. At the southern end of the line where the accumulation is much lower, the samples were taken from the wall of a pit, as the small length of core for each sample did not provide enough melt. The snow was sampled in the pits by sliding a flat sheet of galvanized iron into the snow at each interval starting at the top, and scraping the snow above this into a melt jar. Isotopic contamination of samples from both these methods should be negligible. All samples were melted in plastic jars, and then transferred into 5Oml plastic bottles. A total of 630 samples from 9 stations were returned to Australia for oxygen isotope analysis, carried out in Melbourne by Ted Vishart, Dick Marriot, and Gao Xiangqun. The station/cane labels for the sample sites were: A028 V140/4 (near GC30) V230/4 (near GC37) V270/1 (near GC38) V300/1 (near GC39) V350/1 (near GC40) V400/1 (near GC41) V450/1 (near GC42) V630/1 (near GC47) The columns in the spreadsheet are: Sequence Number Core depth (metres) Oxygen isotope value (the number is a ratio of O18 per ml of O16, expressed as a percentage (but as parts per 1000 instead of 100)) proprietary oxygen-isotopes-plateau-1984_1 7 year oxygen isotope results from samples taken on Antarctic Plateau traverse, 1984 ALL STAC Catalog 1978-01-01 1984-12-31 100, -75, 130, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313700-AU_AADC.umm_json A total of nine stations were sampled for oxygen isotopes during the 1984 spring traverse to the Antarctic Plateau. The aim of this program was to take a number of samples from a core or a pit, at stations of known accumulation over a particular period, to see how far inland the annual cycles could accurately be traced. The samples were not taken at ice movement stations, but at canes each 2km along the line, to avoid sampling the accumulation, and thus isotope disturbance resulting from parking the vans beside the IMS poles in 1978 and 1979. The accumulation for the cane at each sampled station was calculated for the six years since 1978, and the total multiplied by 7/6 to give the sampling depth required to cover 7 years. Seventy samples were taken at each station, i.e. approximately 10 per year. At most stations a PICO drill was used to obtain a core, and the samples cut with a stainless steel knife on the stainless sink in the living van. At the southern end of the line where the accumulation is much lower, the samples were taken from the wall of a pit, as the small length of core for each sample did not provide enough melt. The snow was sampled in the pits by sliding a flat sheet of galvanized iron into the snow at each interval starting at the top, and scraping the snow above this into a melt jar. Isotopic contamination of samples from both these methods should be negligible. All samples were melted in plastic jars, and then transferred into 5Oml plastic bottles. A total of 630 samples from 9 stations were returned to Australia for oxygen isotope analysis, carried out in Melbourne by Ted Vishart, Dick Marriot, and Gao Xiangqun. The station/cane labels for the sample sites were: A028 V140/4 (near GC30) V230/4 (near GC37) V270/1 (near GC38) V300/1 (near GC39) V350/1 (near GC40) V400/1 (near GC41) V450/1 (near GC42) V630/1 (near GC47) The columns in the spreadsheet are: Sequence Number Core depth (metres) Oxygen isotope value (the number is a ratio of O18 per ml of O16, expressed as a percentage (but as parts per 1000 instead of 100)) proprietary +oxygen-isotopes-plateau-1984_1 7 year oxygen isotope results from samples taken on Antarctic Plateau traverse, 1984 AU_AADC STAC Catalog 1978-01-01 1984-12-31 100, -75, 130, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313700-AU_AADC.umm_json A total of nine stations were sampled for oxygen isotopes during the 1984 spring traverse to the Antarctic Plateau. The aim of this program was to take a number of samples from a core or a pit, at stations of known accumulation over a particular period, to see how far inland the annual cycles could accurately be traced. The samples were not taken at ice movement stations, but at canes each 2km along the line, to avoid sampling the accumulation, and thus isotope disturbance resulting from parking the vans beside the IMS poles in 1978 and 1979. The accumulation for the cane at each sampled station was calculated for the six years since 1978, and the total multiplied by 7/6 to give the sampling depth required to cover 7 years. Seventy samples were taken at each station, i.e. approximately 10 per year. At most stations a PICO drill was used to obtain a core, and the samples cut with a stainless steel knife on the stainless sink in the living van. At the southern end of the line where the accumulation is much lower, the samples were taken from the wall of a pit, as the small length of core for each sample did not provide enough melt. The snow was sampled in the pits by sliding a flat sheet of galvanized iron into the snow at each interval starting at the top, and scraping the snow above this into a melt jar. Isotopic contamination of samples from both these methods should be negligible. All samples were melted in plastic jars, and then transferred into 5Oml plastic bottles. A total of 630 samples from 9 stations were returned to Australia for oxygen isotope analysis, carried out in Melbourne by Ted Vishart, Dick Marriot, and Gao Xiangqun. The station/cane labels for the sample sites were: A028 V140/4 (near GC30) V230/4 (near GC37) V270/1 (near GC38) V300/1 (near GC39) V350/1 (near GC40) V400/1 (near GC41) V450/1 (near GC42) V630/1 (near GC47) The columns in the spreadsheet are: Sequence Number Core depth (metres) Oxygen isotope value (the number is a ratio of O18 per ml of O16, expressed as a percentage (but as parts per 1000 instead of 100)) proprietary ozone-measurement-and-analysis-in-the-intercantonal-forest-observation-progra_1.0 Ozone measurement and analysis in the Intercantonal Forest Observation Program ENVIDAT STAC Catalog 2023-01-01 2023-01-01 7.5, 47.53, 7.5, 47.53 https://cmr.earthdata.nasa.gov/search/concepts/C3383777000-ENVIDAT.umm_json In order to quantify effects of ozone on Swiss forest ozone flux calculations are needed. Currently 5 stations are operated to measure the ozone load on forests. These data form the basis for ozone flux calculations. The ozone flux calculation is carried out with the DO3SE model (Büker et al. 2012). This can then be used for epidemiological evaluations for the Swiss forest (Thomas et al. 2005, Braun et al. 2014, Braun et al. 2017, Braun et al. 2020, Braun et al. 2021, Braun et al. 2022). proprietary p3metnavimpacts_1 P-3 Meteorological and Navigation Data IMPACTS GHRC_DAAC STAC Catalog 2020-01-12 2023-02-28 -95.243, 33.261, -64.987, 48.237 https://cmr.earthdata.nasa.gov/search/concepts/C1995868137-GHRC_DAAC.umm_json The P-3 Meteorological and Navigation Data IMPACTS dataset is a subset of airborne measurements that include GPS positioning and trajectory data, aircraft orientation, and atmospheric state measurements of temperature, pressure, water vapor, and horizontal winds. These measurements were taken from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data are available in ASCII-ict format from January 12, 2020, through February 28, 2023. proprietary p_pet_500m_1.0 Average precipitation and PET over Switzerland at 500m resolution ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815390-ENVIDAT.umm_json "Long-term (1980-2011) average annual precipitation (pcp_ch_longterm_yr_avg.tif) and potential evapotranspiration (pet_ch_longterm_yr_avg.tif) at 500m resolution. Units are mm per year. Files are GeoTIFF rasters, and can be read in R using the command raster(""pcp_ch_longterm_yr_avg.tif), after installing packages ""raster"" and ""rgdal""." proprietary @@ -20434,8 +20502,8 @@ pedestrian_royal_1 Effects of human activity on Royal penguins on Macquarie Isla pfynwald-2019-dendrochronological-and-tree-ring-isotope-dataset_1.0 Pfynwald 2019 - Dendrochronological and tree-ring isotope dataset ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3383777025-ENVIDAT.umm_json Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). This dataset includes measure tree-ring width and tree-ring isotope chronologies (stable isotope ratios of non-exchangeable hydrogen δ2H, oxygen δ18O, and carbon δ13C), for the Pfynwald experiment, including control trees, irrigation and irrigation-stop treatments until the year 2019. This dataset contains all data on which the following publication below is based. Please cite this paper together with the citation for the datafile. proprietary pfynwald-geoelectric-experiment-2022_1.0 Pfynwald Geoelectric Experiment 2022 ENVIDAT STAC Catalog 2024-01-01 2024-01-01 7.612088, 46.303066, 7.612088, 46.303066 https://cmr.earthdata.nasa.gov/search/concepts/C3383775675-ENVIDAT.umm_json "This collection of datasets consists of various measurements taken during the year 2022 in Pfynwald. It combines 2 electrical resistivity transects which were monitored in May and July, before and after the irrigation season. The transects transverse all treatments (irrigation, control, irrigation stop). Each transect was repeated twice during the day for a period of 3 days to a week. In addition there are raw and post-processed drone images (and resulting PRI maps), which were used to compare the below ground responses (from resistivity) to the above ground (crown) stress. Here, the raw data is stored and a link to a git project is provided where python code is stored to reproduce all the results the published manuscript: ""Does optimality partitioning theory fail for belowground traits? Insights from geophysical imaging of a drought-release experiment in a dry Scots Pine forest"", New Phytologist, Shakas et al., 2024. The in-depth explanations from each processing step are found in the code (git project)." proprietary pfynwald_2016 Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland ENVIDAT STAC Catalog 2016-01-01 2016-01-01 7.61192, 46.30284, 7.61192, 46.30284 https://cmr.earthdata.nasa.gov/search/concepts/C2789816328-ENVIDAT.umm_json To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years. proprietary -pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ALL STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary +pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ALL STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary phipsimpacts_1 Particle Habit Imaging and Polar Scattering Probe (PHIPS) IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -95.243, 33.261, -64.987, 48.237 https://cmr.earthdata.nasa.gov/search/concepts/C1995874351-GHRC_DAAC.umm_json The Particle Habit Imaging and Polar Scattering (PHIPS) Probes IMPACTS dataset consists of cloud particle imagery collected by the Particle Habit Imaging and Polar Scattering (PHIPS) probe onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. PHIPS allows for the measurement of particle shape, size, and habit. The browse files in this dataset contain the post-processed particle-by-particle stereo images (2 images from different angles) collected by PHIPS during the campaign. The files are available from January 18, 2020, through February 28, 2023, in PNG format. proprietary phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0 Phosphorus and nitrogen leaching from beech forest soils ENVIDAT STAC Catalog 2021-01-01 2021-01-01 9.927478, 50.3518, 10.26725, 52.838967 https://cmr.earthdata.nasa.gov/search/concepts/C2789816374-ENVIDAT.umm_json Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites. Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019 with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019. For more details please refer to the publication. proprietary photo_mosaic_laurens_or_1 Heard Island, Laurens Peninsula, Coastal Orthophoto Mosaic derived from Non-Metric Photography AU_AADC STAC Catalog 1980-01-01 2000-12-31 73.23, -53.05, 73.41, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214311224-AU_AADC.umm_json The orthophoto mosaic is a rectified georeferenced image of the Heard Island, Laurens Peninsula Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric cameras (focal length unknown). proprietary @@ -20665,26 +20733,26 @@ sbuparsimpacts_1 SBU Parsivel IMPACTS GHRC_DAAC STAC Catalog 2020-01-01 2023-03- sbuplimpacts_1 SBU Pluvio Precipitation Gauge IMPACTS GHRC_DAAC STAC Catalog 2020-01-07 2023-03-02 -73.138, 40.8556, -72.8714, 40.90712 https://cmr.earthdata.nasa.gov/search/concepts/C1995869760-GHRC_DAAC.umm_json The SBU Pluvio Precipitation Gauge IMPACTS dataset consists of precipitation intensity and precipitation accumulation collected using the OTT Pluvio2 weighing rain gauge during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. NASA’s Earth Venture program funded IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data files in this dataset are available in ASCII-CSV format from January 7, 2020, through March 2, 2023. proprietary sbuskylerimpacts_1 SBU X-band Phased Array Radar (SKYLER) IMPACTS GHRC_DAAC STAC Catalog 2022-01-17 2023-02-28 -77.4867, 40.1501, -71.266, 43.695 https://cmr.earthdata.nasa.gov/search/concepts/C2704110186-GHRC_DAAC.umm_json The SBU X-band Phased Array Radar (SKYLER) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) X-band Phased Array Radar (SKYLER) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. SKYLER provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 17, 2022, through February 28, 2023, in netCDF-4 format. proprietary sbusndimpacts_1 SBU Mobile Soundings IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -76.980629, 40.4841385, -70.8692093, 43.7849808 https://cmr.earthdata.nasa.gov/search/concepts/C1995869776-GHRC_DAAC.umm_json The SBU Mobile Sounding IMPACTS dataset consists of mobile sounding profiles collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Mobile-sounding profiles were obtained about every three hours during snow events by Stony Brook University (SBU). The sounding measures temperature, humidity, height, and horizontal wind direction and speed in the atmosphere. Atmospheric pressure is calculated from GPS height. Data files are available from January 18, 2020, through February 28, 2023 in netCDF-3 format. proprietary -scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary +scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary -scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 ALL STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 SCIOPS STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary +scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 ALL STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1716 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716 ALL STAC Catalog 1979-12-27 1980-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420764-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1716 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716 SCIOPS STAC Catalog 1979-12-27 1980-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420764-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1772 Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155493-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1772 Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155493-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary -scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary +scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary -scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary +scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] SCIOPS STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an “Antarctic Specially Managed Area” (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] ALL STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an “Antarctic Specially Managed Area” (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary schweizerisches-landesforstinventar-2009-2017_1.0 Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817193-ENVIDAT.umm_json Swiss National Forest Inventory. Results of the fourth survey 2009–2017. The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings. Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017. In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt, im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate über den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erläutert. Der Bericht ist thematisch strukturiert nach den europäischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalität, Holzproduktion, biologische Vielfalt, Schutzwald und Sozioökonomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab. Keywords: Waldfläche, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversität, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz Content license: All rights reserved. Copyright © 2020 by WSL, Birmensdorf. proprietary @@ -20724,8 +20792,8 @@ sentinel-3-olci-l1-bundle-1_NA Sentinel-3/OLCI - Level-1B Full Resolution INPE S sequential-wind-doppler-lidar-wind-profile-measurements-on-the-gotthard-pass-in-_1.0 Sequential Wind-Doppler LiDAR wind profile measurements on the Gotthard pass in Switzerland - Summer 2023 ENVIDAT STAC Catalog 2024-01-01 2024-01-01 8.551163, 46.548983, 8.574859, 46.566216 https://cmr.earthdata.nasa.gov/search/concepts/C3383777394-ENVIDAT.umm_json "This data include wind speeds, wind directions, turbulence, temperature, pressure and humidity data of 10 LiDAR locations along a transect in the Gotthard wind park taken in during the summer of 2023. The data is quality controlled and annotated according to CF-1.8 data principles. The methods behind this data collection are explained in an article under review called ""Resolving three-dimensional wind velocity fields with sequential wind-Doppler LiDAR for wind energy in the complex terrain - Gotthard Pass, Switzerland""." proprietary shadoz_ozonesonde_726_1 SAFARI 2000 SHADOZ Ozonesonde Data, Zambia and Regional Sites, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-01 2000-11-30 55.48, -7.98, 55.48, -7.98 https://cmr.earthdata.nasa.gov/search/concepts/C2789016629-ORNL_CLOUD.umm_json Ozonesonde launches were made by the Southern Hemisphere ADditional OZonesondes (SHADOZ) group as part of the SAFARI 2000 Dry Season Campaign in September 2000 (Thompson et al., 2002). Ozonesondes are balloon-borne instruments measuring profile ozone, as well as temperature and pressure from an attached radiosonde, up to 35 km in height (around 5 hPa in pressure coordinates) capturing the troposphere and lower stratospheric portion of the atmosphere. During the campaign, ozonesondes were launched daily during the height of the burning season and in a region of active biomass burning activity. proprietary shape_still_matters_1.0 Shape still matters - experimental quantification of deadwood effects on rockfall dynamics ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.705375, 46.972932, 9.709125, 46.975172 https://cmr.earthdata.nasa.gov/search/concepts/C3383777473-ENVIDAT.umm_json We conducted an experimental rockfall campaign on a forested slope with deadwood and on with the windthrown area cleared in Schiers (CH). Two different rock categories were used: natural rocks with masses between 30 and 85 kg and artificial EOTA-concrete rocks with rock masses ranging from 45 kg up to 3200 kg. In contrast to the intended research article publication, also all information about the experimental runs of the smaller rock masses are available. Besides the geometries of those rocks, the data set contains three LiDAR point clouds, orthophotos, the release point (and release line used in RAMMS), the retrieved single tree positions with DBH, and energy absorption threshold, video footage, and all reconstructed trajectories. proprietary -shirley_dem_1 A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica ALL STAC Catalog 2005-01-01 2007-05-01 110.473, -66.287, 110.509, -66.277 https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and (iv) an orthophoto created using the DEM. The data are stored in the UTM zone 49 map projection. The horizontal datum is WGS84. The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins. See a related URL below for a map showing Shirley island. proprietary shirley_dem_1 A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica AU_AADC STAC Catalog 2005-01-01 2007-05-01 110.473, -66.287, 110.509, -66.277 https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and (iv) an orthophoto created using the DEM. The data are stored in the UTM zone 49 map projection. The horizontal datum is WGS84. The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins. See a related URL below for a map showing Shirley island. proprietary +shirley_dem_1 A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica ALL STAC Catalog 2005-01-01 2007-05-01 110.473, -66.287, 110.509, -66.277 https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and (iv) an orthophoto created using the DEM. The data are stored in the UTM zone 49 map projection. The horizontal datum is WGS84. The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins. See a related URL below for a map showing Shirley island. proprietary short-term-drainage-density-dynamics-dataset-for-the-haute-mentue-catchment_1.0 Short-term Drainage Density Dynamics Dataset for the Haute-Mentue Catchment ENVIDAT STAC Catalog 2024-01-01 2024-01-01 6.306152, 46.422713, 7.163086, 46.785016 https://cmr.earthdata.nasa.gov/search/concepts/C3383777548-ENVIDAT.umm_json The dataset contains time series of water levels, precipitation measured in the two sub-catchments of the Haute-Mentue catchment and its vicinity during summer and autumn 2022, as well as flowing drainage network lengths calculated for these areas using the CEASE method developed by the authors. Detailed description of the dataset is provided in the documentation. proprietary simrad_SO Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC. SCIOPS STAC Catalog 2002-08-03 2002-09-15 -75.5, -68.75, -69.5, -65.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214155475-SCIOPS.umm_json Using the hull mounted Simrad EK500 Scientific Sounder System, acoustic returns from 38, 120, and 200 kHz transducers were recorded continuously along ship's track from Aug 3 - Sept 15, 2002. Of interest, was the acoustic returns from zooplankton patches and density structures, and the signel correlations with known plankton tows and CTD casts. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. These data have been reduced to daily files and are supported by software for manipulative purposes. Ship name/cruise ID/dates of cruise RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002 proprietary simrad_SO Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC. ALL STAC Catalog 2002-08-03 2002-09-15 -75.5, -68.75, -69.5, -65.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214155475-SCIOPS.umm_json Using the hull mounted Simrad EK500 Scientific Sounder System, acoustic returns from 38, 120, and 200 kHz transducers were recorded continuously along ship's track from Aug 3 - Sept 15, 2002. Of interest, was the acoustic returns from zooplankton patches and density structures, and the signel correlations with known plankton tows and CTD casts. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. These data have been reduced to daily files and are supported by software for manipulative purposes. Ship name/cruise ID/dates of cruise RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002 proprietary @@ -20738,8 +20806,8 @@ sir_c Spaceborne Imaging Radar C-band (SIR-C) USGS_LTA STAC Catalog 1994-04-09 1 site-description-davos-seehornwald-ch-dav_1.0 Site description Davos Seehornwald CH-DAV ENVIDAT STAC Catalog 2024-01-01 2024-01-01 9.855552, 46.815921, 9.855552, 46.815921 https://cmr.earthdata.nasa.gov/search/concepts/C3383777014-ENVIDAT.umm_json Site description of the Seehornwald Davos (CH-DAV) research site. The material provided offers information on the biomass and growth of trees in CH-DAV, including supporting materials such as maps, geolocations and analyses. CH-DAV is part of networks such as ICOS, LWF, TreeNet, ICPForests and is dedicated to forest monitoring and research. proprietary slgeo_1 SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) LANDSAT GEOTIFF V1 GHRC_DAAC STAC Catalog 2000-09-11 2008-09-08 -91.7794, 27.8502, -82.6518, 31.417 https://cmr.earthdata.nasa.gov/search/concepts/C1979944011-GHRC_DAAC.umm_json The Sediment Analysis Network for Decision Support (SANDS) Landsat Geotiff dataset includes images for sediment redistribution after a hurricane on the coast of the Gulf of Mexico and then creates a product based on the analysis from September 11, 2000 to September 8, 2008. This dataset consists of the set of daytime GeoTiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis. Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of Mexico coastline in Alabama and portions of Florida). These are seasonal data for storms. proprietary slgsa_1 SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) LANDSAT GEOLOGICAL SURVEY OF AL (GSA) ANALYSIS V1 GHRC_DAAC STAC Catalog 2000-09-11 2008-09-08 -90, 27, -84.25, 31 https://cmr.earthdata.nasa.gov/search/concepts/C1979944726-GHRC_DAAC.umm_json The Sediment Analysis Network for Decision Support (SANDS) Landsat Geological Survey of AL (GSA) Analysis dataset analyzed changes in the coastal shoreline and sedimentation using Landsat GeoTiff images as part of the Sediment Analysis Network for Decision Support (SANDS) project. The daytime GeoTiffs images from Landsat 5 and Landsat 7 were analyzed for sediment re-distribution after a hurricane over the Gulf of Mexico coastline in Alabama and part of the Florida area (coordinates 31 to 27 North latitude and 90 to 84.25 West longitude). These are seasonal data for storms from 2001-2008. In addition to the analyzed files, the data files include the ESRI files for zipped bands and grids, metadata, and storm temporal information for the sediment analysis images. proprietary -slow-snow-compression_1.0 A grain-size driven transition in the deformation mechanism in slow snow compression ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.8417222, 46.8095077, 9.8417222, 46.8095077 https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.umm_json We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory. Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep. The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans. proprietary slow-snow-compression_1.0 A grain-size driven transition in the deformation mechanism in slow snow compression ALL STAC Catalog 2023-01-01 2023-01-01 9.8417222, 46.8095077, 9.8417222, 46.8095077 https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.umm_json We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory. Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep. The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans. proprietary +slow-snow-compression_1.0 A grain-size driven transition in the deformation mechanism in slow snow compression ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.8417222, 46.8095077, 9.8417222, 46.8095077 https://cmr.earthdata.nasa.gov/search/concepts/C3226083057-ENVIDAT.umm_json We conducted consecutive loading-relaxation experiments at low strain rates to study the viscoplastic behavior of the intact ice matrix in snow. The experiments were conducted using a micro-compression stage within the X-ray tomography scanner in the SLF cold laboratory. Next, to evaluate the experiments, a novel, implicit solution of a transient scalar model was developed to estimate the stress exponent and time scales in the effective creep relation (Glen's law). The result reveals that, for the first time, a transition in the exponent in Glen's law depends on geometrical grain size. A cross-over of stress exponent $n=1.9$ for fine grains to $n=4.4$ for coarse grains is interpreted as a transition from grain boundary sliding to dislocation creep. The dataset includes compression force data from 11 experiments and corresponding 3D image data from tomography scans. proprietary smapcpex_1 Soil Moisture Active Passive (SMAP) CPEX GHRC_DAAC STAC Catalog 2017-05-24 2017-07-16 -180, 3.866, 180, 41.524 https://cmr.earthdata.nasa.gov/search/concepts/C3385014628-GHRC_DAAC.umm_json The SMAP CPEX dataset consists of data collected from the Soil Moisture Active Passive (SMAP) satellite that carries two instruments, a radar (active) and a radiometer (passive), that together will make global measurements of land surface soil moisture and freeze/thaw state. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017, through July 16, 201,7 in netCDF-3 format. proprietary smart_radiometers_727_1 SAFARI 2000 Surface Atmospheric Radiative Transfer (SMART), Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-15 2000-09-17 31.59, -24.97, 31.59, -24.97 https://cmr.earthdata.nasa.gov/search/concepts/C2789018469-ORNL_CLOUD.umm_json Surface-sensing Measurements for Radiative Transfer (SMART) and Chemical, Optical, and Microphysical Measurements of In-situ Troposphere (COMMIT) consist of a suite of instruments that measure (both in-situ and by remote sensing) parameters that help to characterize, as completely as possible, constituents of the atmosphere at a given location. SMART and COMMIT are mobile systems that can be deployed to locations that exhibit interesting atmospheric phenomena. This allows investigators to participate in coordinated measurement campaigns, such as SAFARI 2000.The SMART instruments were deployed to the Skukuza Airport from August 15 to September 17, 2000 to take part in the SAFARI 2000 Dry Season Aircraft Campaign. The SMART-COMMIT mission is designed to pursue the following goals: Earth Observing System (EOS) validation; innovative investigations; and long-term atmospheric monitoring. The results reported in this data set are for the following instruments deployed and measurements recorded at the Skukuza Airport site within the Kruger National Park: several broadband radiometers, for global, diffuse, direct downward solar irradiance and global infrared downward irradiance; meteorological sensors, for surface air temperature, pressure, relative humidity, and wind; and a Solar Spectral Flux Radiometer (NASA Ames) for spectral solar downward irradiance. proprietary smgeo_1 SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) MODIS GEOTIFF V1 GHRC_DAAC STAC Catalog 2000-09-11 2008-09-09 -90.0021, 27, -84.25, 31.0125 https://cmr.earthdata.nasa.gov/search/concepts/C1979944933-GHRC_DAAC.umm_json The Sediment Analysis Network for Decision Support (SANDS) MODIS GeoTIFF dataset consists of the set of GeoTIFF images provided to the Geological Survey of Alabama for their analysis. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis. proprietary @@ -20791,8 +20859,8 @@ sonobuoy_whale_SO Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, sonobuoy_whale_SO Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC SCIOPS STAC Catalog 2001-03-21 2001-08-28 -77.2, -70.3, -61.5, -59 https://cmr.earthdata.nasa.gov/search/concepts/C1214155588-SCIOPS.umm_json Mysticete whale calls were monitored/recorded via deployment of directional sonobuoys during March-August 2001. This monitoring technique is used to study whale distribution, behavior and aid in estimating populations. Deployments were either random or when whales were observed. The observed calls are identified by species. Ancillary calls by seals are reported but not identified by species. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. Ship names/cruise ID/cruise dates R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001 Access to the original acoustic recordings should be directed to the Investigator identified in this description. proprietary source-code-climate-change-scenarios-at-hourly-resolution_1.0 Source code for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach ENVIDAT STAC Catalog 2021-01-01 2021-01-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2789816944-ENVIDAT.umm_json This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. proprietary sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0 Sources and turnover of soil organic matter in Pfynwald irrigation experiment ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083043-ENVIDAT.umm_json This dataset contains all data on which the following publication below is based. Paper Citation: Guidi, C., Lehmann, M.M., Meusburger, K., Saurer, M., Vitali, V., Peter, M., Brunner, I., Hagedorn, F. (accepted). Tracing sources and turnover of soil organic matter in a long-term irrigated dry forest using a novel hydrogen isotope approach. Soil Biology and Biochemistry. Please cite this paper together with the citation for the datafile. Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). Data include: (1) Isotopic composition (stable isotope ratios of non-exchangeable hydrogen δ2Hn, carbon δ13C, and nitrogen δ15N) and Hn, C and N concentrations in SOM sources (fresh Pinus sylvestris needles, litter layer, fine roots), bulk SOM (organic layer, 0-2 cm, 2-5 cm, 60-80 cm), particle-size fractions (depths: 0-2 cm, 2-5 cm; cPOM: coarse POM; fPOM: fine POM; MOM: mineral-associated organic matter); (2) Mass loss, δ2Hn values and Hn concentrations of Pinus sylvestris fine roots and needle litter (litter decomposition experiments from Herzog et al. 2019, ISME journal, and Guidi et al. 2022, Global Change Biology); (3) Relative source contribution (foliar litter, fine roots, and mycelia) to bulk SOM and fractions estimated using Bayesian mixing models (R package MixSIAR, version 3.1.12) with irrigation and depth as fixed factors. The models were informed with δ13C, δ15N and δ2Hn values and C, N, and Hn concentrations of foliar litter, roots, and mycelia as input sources. Given the kinetic isotope fractionation occurring during microbial SOM decomposition, the mixing models were informed with isotope fractionation factors, representing the isotope enrichment from sources to soils; (4) Fraction of new organic Hn (Fnew) over the irrigation period, calculated using a simple end-member mixing model according to Balesdent et al. (1987) and mean residence time estimated as MRT = - t / ln (1 - Fnew), with t time in years since irrigation started and assuming single-pool model with first-order kinetics. proprietary -sowers_0739491 2008 South Pole Firn Air Methane Isotopes ALL STAC Catalog 2008-12-01 2009-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport. proprietary sowers_0739491 2008 South Pole Firn Air Methane Isotopes SCIOPS STAC Catalog 2008-12-01 2009-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport. proprietary +sowers_0739491 2008 South Pole Firn Air Methane Isotopes ALL STAC Catalog 2008-12-01 2009-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport. proprietary spatial-modelling-of-ecological-indicator-values_1.0 Spatial modelling of ecological indicator values ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817163-ENVIDAT.umm_json "Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland. This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection (""+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs""). The excel file (xlsx) provides a short description of the raster layers. Paper Citation: Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)" proprietary spatial-planning-brazil_1.0 Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -46.1425781, -24.005155, -44.4836426, -23.1908626 https://cmr.earthdata.nasa.gov/search/concepts/C2789817270-ENVIDAT.umm_json "The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication)." proprietary species-distribution-maps-gdplants_1.0 Species distribution maps of Fagales and Pinales (GDPlants) ENVIDAT STAC Catalog 2022-01-01 2022-01-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2789817446-ENVIDAT.umm_json This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022). To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details). proprietary @@ -21204,8 +21272,8 @@ usgs_npwrc_acutetoxicity_Version 06JUL2000 Acute Toxicity of Fire-Control Chemic usgs_npwrc_alpha_Version 16MAY2000 Alpha Status, Dominance, and Division of Labor in Wolf Packs. CEOS_EXTRA STAC Catalog 1986-01-01 1998-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231552683-CEOS_EXTRA.umm_json "The prevailing view of a wolf (Canis lupus) pack is that of a group of individuals ever vying for dominance but held in check by the ""alpha"" pair, the alpha male and the alpha female. Most research on the social dynamics of wolf packs, however, has been conducted on non-natural assortments of captive wolves. Here I describe the wolf-pack social order as it occurs in nature, discuss the alpha concept and social dominance and submission, and present data on the precise relationships among members in free-living packs based on a literature review and 13 summers of observations of wolves on Ellesmere Island, Northwest Territories, Canada. I conclude that the typical wolf pack is a family, with the adult parents guiding the activities of the group in a division-of-labor system in which the female predominates primarily in such activities as pup care and defense and the male primarily during foraging and food-provisioning and the travels associated with them." proprietary usgs_npwrc_canvasbacks_Version 13NOV2001 Influence of Age and Selected Environmental Factors on Reproductive Performance of Canvasbacks CEOS_EXTRA STAC Catalog 1974-01-01 1980-01-01 -102.5, 48, -95, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231549601-CEOS_EXTRA.umm_json Age, productivity, and other factors affecting breeding performance of canvasbacks (Aythya valisineria) are poorly understood. Consequently, we tested whether reproductive performance of female canvasbacks varied with age and selected environmental factors in southwestern Manitoba from 1974 to 1980. Neither clutch size, nest parasitism, nest success, nor the number of ducklings/brood varied with age. Return rates, nest initiation dates, renesting, and hen success were age-related. Return rates averaged 21% for second-year (SY) and 69% for after-second-year (ASY) females (58% for third-year and 79% for after-third-year females). Additionally, water conditions and spring temperatures influenced chronology of arrival, timing of nesting, and reproductive success. Nest initiation by birds of all ages was affected by minimum April temperatures. Clutch size was higher in nests initiated earlier. Interspecific nest parasitism did not affect clutch size, nest success, hen success, or hatching success. Nest success was lower in dry years (17%) than in moderately wet (54%) or wet (60%) years. Nests per female were highest during wet years. No nests of SY females were found in dry years. In years of moderate to good wetland conditions, females of all ages nested. Predation was the primary factor influencing nest success. Hen success averaged 58% over all years. The number of ducklings surviving 20 days averaged 4.7/brood. Because SY females have lower return rates and hen success than ASY females, especially during drier years, management to increase canvasback populations might best be directed to increasing first year recruitment (no. of females returning to breed) and to increasing overall breeding success by reducing predation and enhancing local habitat conditions during nesting. proprietary usgs_npwrc_ducks_Version 07JAN98 Assessing Breeding Populations of Ducks by Ground Counts. CEOS_EXTRA STAC Catalog 1952-01-01 1959-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231554819-CEOS_EXTRA.umm_json Waterfowl inventories taken during the breeding season are recognized as a basic technique in assessing the number of ducks per unit area. That waterfowl censusing is still an inexact technology leading to divergent interpretations of results is also recognized. The inexactness stems from a wide spectrum of factors that include weather, breeding phenology, asynchronous nesting periods, vegetative growth, species present and their daily activity, previous field experience of personnel, plus others (Stewart et al., 1958; Diem and Lu, 1960; Crissey, 1963a). In spite of the possible errors, accurate estimates are necessary to our understanding of production rates of all North American breeding waterfowl. Statistically adequate censuses of breeding pairs and accurate predictions of young produced per pair still remain as two of the primary statistics in determining yearly recruitment rate of species breeding in particular units of pond habitats. Without precise breeding pair and production data, the problems involved in describing the reproductive potential of any species and its environmental or density-dependent limiting factors cannot be adequately resolved. The purposes of this paper are to (1) describe methods used to estimate yearly breeding pair abundance on two study areas, one in Manitoba and the other in Saskatchewan; (2) assess the relative consistency, precision, and accuracy of pair counts as related to the breeding biology of duck species; and (3) recommend census methods that can more closely approximate absolute populations breeding in parkland and grassland habitats. proprietary -usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear ALL STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear CEOS_EXTRA STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary +usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear ALL STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary usgs_npwrc_incidentalmarinecatc_Version 11APR2001 Incidental Catch of Marine Birds in the North Pacific High Seas Driftnet Fisheries in 1990. CEOS_EXTRA STAC Catalog 1990-01-01 1990-01-01 -140, 20, 140, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553439-CEOS_EXTRA.umm_json "The incidental take of marine birds was estimated for the following North Pacific driftnet fisheries in 1990: Japanese squid, Japanese large-mesh, Korean squid, and Taiwanese squid and large-mesh combined. The take was estimated by assuming that the data represented a random sample from an unstratified population of all driftnet fisheries in the North Pacific. Estimates for 13 species or species groups are presented, along with some discussion of inadequacies of the design. About 416,000 marine birds were estimated to be taken incidentally during the 1990 season; 80 % of these were in the Japanese squid fishery. Sooty Shearwaters, Short-tailed Shearwaters, and Laysan Albatrosses were the most common species in the bycatch. Regression models were also developed to explore the relations between bycatch rate of three groups Black-footed Albatross, Laysan Albatross, and ""dark"" shearwatersand various explanatory variables, such as latitude, longitude, month, vessel, sea surface temperature, and net soak time (length of time nets were in the water). This was done for only the Japanese squid fishery, for which the most complete information was available. For modeling purposes, fishing operations for each vessel were grouped into 5-degree blocks of latitude and longitude. Results of model building indicated that vessel had a significant influence on bycatch rates of all three groups. This finding emphasizes the importance of the sample of vessels being representative of the entire fleet. In addition, bycatch rates of all three groups varied spatially and temporally. Bycatch rates for Laysan Albatrosses tended to decline during the fishing season, whereas those for Black-footed Albatrosses and dark shearwaters tended to increase as the season progressed. Bycatch rates were positively related to net soak time for Laysan Albatrosses and dark shearwaters. Bycatch rates of dark shearwaters were lower for higher sea surface temperatures." proprietary usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships ALL STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary @@ -21228,8 +21296,8 @@ usgsbrdnpwrcd0000001_Version 15DEC98 An Assessment of Exotic Plant Species of Ro usgsbrdnpwrcd0000003_Version 16JUL97 Human Disturbances of Waterfowl: An Annotated Bibliography. CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551896-CEOS_EXTRA.umm_json The expansion of outdoor recreational activities has increased greatly the interaction between the public and waterfowl and waterfowl habitat. The effects of these interactions on waterfowl habitats are more visible and obvious, whereas the effects of interactions which disrupt the normal behavior of waterfowl are more subtle and often overlooked, but perhaps no less of a problem than destruction of habitat. This bibliography contains excerpts or annotations from 211 articles that contained information about effects of human disturbances on waterfowl. Indices are provided for subject/keywords, geographic locations, species of waterfowl, and authors used in this bibliography. proprietary usgsbrdnpwrcs0000004_Version 12MAY03 Collecting and Analyzing Data from Duck Nesting Studies CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231554360-CEOS_EXTRA.umm_json Northern Prairie has a long history of studying nest success of upland nesting ducks. Over the years, we have developed standardized procedures for collecting and analyzing these types of data. Data forms and instruction manuals developed by the Center are used widely by biologists throughout the northern Great Plains and elsewhere. Extensive use of standardized procedures led to a cooperative effort among Federal, State, Private, and other Non-Government Organizations that has allowed us to compile the Nest File, a data base of more than 75,000 duck nests spanning 30+ years in the northern Great Plains. proprietary validation-of-the-critical-crack-length-in-snowpack_1.0 Validating and improving the critical crack length in SNOWPACK ENVIDAT STAC Catalog 2019-01-01 2019-01-01 9.78797, 46.80757, 9.809407, 46.8292944 https://cmr.earthdata.nasa.gov/search/concepts/C2789817607-ENVIDAT.umm_json To validate the critical crack length as implemented in the snow cover model SNOWPACK, PST experiments were conducted for three winter seasons (2015-2017) at two field site above Davos, Switzerland. This dataset contains manually observed snow profiles and stability tests. Furthermore, corresponding SNOWPACK simulations are included. These data were analyzed and results were published in Richter et al. (2019). Please refer to the Readme file for further details on the data. These data are the basis of the following publication: Richter, B., Schweizer, J., Rotach, M. W., and van Herwijnen, A.: Validating modeled critical crack length for crack propagation in the snow cover model SNOWPACK, The Cryosphere, 13, 3353–3366, https://doi.org/10.5194/tc-13-3353-2019, 2019. proprietary -vanderford_data_1983_85_1 Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85 AU_AADC STAC Catalog 1983-01-01 1985-12-31 108, -67.5, 113, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311394-AU_AADC.umm_json A report outlining the work done on the Vanderford (and Adams) glaciers in 1983/84 and 1984/85, detailing the methods they used for determining ice thickness and velocity. Includes a copy of the program used to process the raw data, gravity observations, and velocity results. These documents have been archived at the Australian Antarctic Division. proprietary vanderford_data_1983_85_1 Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85 ALL STAC Catalog 1983-01-01 1985-12-31 108, -67.5, 113, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311394-AU_AADC.umm_json A report outlining the work done on the Vanderford (and Adams) glaciers in 1983/84 and 1984/85, detailing the methods they used for determining ice thickness and velocity. Includes a copy of the program used to process the raw data, gravity observations, and velocity results. These documents have been archived at the Australian Antarctic Division. proprietary +vanderford_data_1983_85_1 Airborne Topographic and Ice Thickness Survey of the Vanderford Glacier, 1983-85 AU_AADC STAC Catalog 1983-01-01 1985-12-31 108, -67.5, 113, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311394-AU_AADC.umm_json A report outlining the work done on the Vanderford (and Adams) glaciers in 1983/84 and 1984/85, detailing the methods they used for determining ice thickness and velocity. Includes a copy of the program used to process the raw data, gravity observations, and velocity results. These documents have been archived at the Australian Antarctic Division. proprietary vanderford_gravity_1980_1 Gravity Readings, Vanderford Glacier 1980 AU_AADC STAC Catalog 1980-02-11 1980-02-15 110, -67.5, 112, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311395-AU_AADC.umm_json A collection of gravity readings, taken on the Vanderford Glacier in February 1980. Also includes barometric pressure readings, taken at the same time, for determining the height of the location where the reading was taken. Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary vapour-isotopic-composition-along-air-parcel-trajectories-in-antarctic_1.0 Modeled Isotopic Composition of Water Vapour Along Air Parcel Trajectories in the Antarctic ENVIDAT STAC Catalog 2023-01-01 2023-01-01 174.375, -84.9479651, -179.546875, -42.7168763 https://cmr.earthdata.nasa.gov/search/concepts/C3226083103-ENVIDAT.umm_json # Summary This data set contains Python programming code and modeled data discussed in a related research article. We developed a simple isotope model to study the drivers of the particularly depleted vapour isotopic composition measured on the ship of the Antarctic Circumnavigation Expedition close to the outlet of the Mertz glacier, East Antarctica, in the 6-day period from 27 January 2017 to 1 February 2017. The model considers the stable water isotopologues H2(16O), H2(18O), and HD(16O). It uses data from the ERA5 reanalysis product with a spatial resolution of 0.25° x 0.25° (Hersbach et al., 2018) and 10-day backward trajectories for the location of the ship, published by Thurnherr et al. (2020a). Our data set includes the model code, Python scripts for visualizing the results, and data produced by the model including the results shown in the figures of the related research article. Here, we summarize the most important model characteristics while further details can be found in the readme.txt file and the related research article including its supporting information. # Main model characteristics The modeling approach consists of two steps called *Model Sublimation* and *Model Air Parcel*. The former estimates the isotopic compositions of the snow and sublimation flux across the Antarctic Ice Sheet using an Eulerian frame of reference while the latter models the vapour isotopic composition and specific humidity along air parcel trajectories using a Lagrangian frame of reference. The isotope effects of most phase changes are represented by equilibrium fractionation. Only for ocean evaporation, kinetic fractionation is additionally taken into account (original Craig-Gordon formula). For snow sublimation, two assumptions are tested: *Run E* assumes that sublimation is associated with equilibrium fractionation while *Run N* assumes that sublimation occurs without isotopic fractionation. ### Model Sublimation Model Sublimation uses a simple one-dimensional mass-balance approach in each grid cell, considering snow accumulation due to snowfall and vapour deposition and snow ablation due to sublimation. The snowpack is represented by 100 layers of equal thickness (e.g., 1 cm) and density (350 kg m-3). The isotopic composition of snowfall is parameterized by generalizing a site-specific, empirical relationship between the daily mean air temperature and snowfall isotopic composition. In the case of vapour deposition, Model Sublimation assumes equilibrium fractionation and estimates the isotopic composition of the atmospheric vapour as the average value for two idealized situations: (i) locally sourced vapour which has the same isotopic composition as the sublimation flux; (ii) non-locally sourced vapour in isotopic equilibrium with snowfall. Model Sublimation is run with a time step of 1 h, independently of Model Air Parcel. ### Model Air Parcel Every hour, an ensemble of trajectories arrives at different heights in the ABL above the ship. For each of these trajectories, we consider an air parcel with a constant volume of 1 x 1 x 1 m3. The air parcels are initialized at the first suitable time when the trajectories are located in the ABL, either over the ice-free ocean in conditions of evaporation or over snow (Antarctic Ice Sheet or sea ice). Subsequently, the masses of the water isotopologues in the air parcels are simulated with a time step of 3 h, considering vapour uptake or removal due to the moisture flux at the snow or liquid ocean surface (only if the parcel is in the ABL) and cloud/precipitation formation (if the saturation specific humidity is reached). Sea ice is taken into account in a very simplified way. We represent the sea ice by grid cells with a sea-ice cover of more than 90% and assume the isotopic composition of the sublimation flux to be identical to that in the nearest grid cell of the Antarctic Ice Sheet. The isotopic composition of the sublimation flux is taken from Model Sublimation whereas the isotopic composition of the vapour deposition flux (over snow) and condensation flux (over ice-free ocean) is simulated assuming an isotopic equilibrium with the air parcel. Isotope effects of cloud/precipitation formation are represented using the classic Rayleigh distillation model with equilibrium fractionation, where the cloud water is assumed to precipitate immediately after formation. # References Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J.,... others (2018). *ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. doi: 10.24381/cds.bd0915c6 Thurnherr, I., Wernli, H., & Aemisegger, F. (2020a). *10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017*. Zenodo. doi: 10.5281/zenodo.4031705 proprietary veg_continuous_fields_xdeg_931_1 ISLSCP II Continuous Fields of Vegetation Cover, 1992-1993 ORNL_CLOUD STAC Catalog 1992-04-01 1993-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784863182-ORNL_CLOUD.umm_json The objective of this study was to derive continuous fields of vegetation cover from multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data using all available bands and derived Normalized Difference Vegetation Index (NDVI). The continuous fields describe sub-pixel proportions of cover for tree, herbaceous, bare ground and water cover types. For tree cover, additional fields describing leaf longevity (evergreen and deciduous) and leaf morphology (broadleaf and needleleaf) were also generated. The modeling of carbon dynamics and climate require knowing tree characteristics such as these. These products were resampled and aggregated to 0.25, 0.5 and 1.0 degree grids for the International Satellite Land Surface Climatology Project (ISLSCP) data initiative II. The data set describes the geographic distributions of three fundamental vegetation characteristics: tree, herbaceous and bare ground cover, plus a water layer. For tree cover, leaf longevity and morphology layers were produced.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets. proprietary @@ -21290,8 +21358,8 @@ willmott_673_1 LBA Regional Climate Data, 0.5-Degree Grid, 1960-1990 (Willmott a wind-topo_model_0.1.0 Wind-Topo_model ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817956-ENVIDAT.umm_json "Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain. It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input. This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM. Accompanies the publication ""Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning"" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265 Please cite this publication if you use Wind-Topo or derive new models from it. The code can be used under the GNU Affero General Public License (AGPL)." proprietary wind_dem_1 Digital Elevation Model of the Windmill Islands AU_AADC STAC Catalog 1999-07-11 1999-08-23 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311463-AU_AADC.umm_json This DEM includes all the inshore and offshore islands, all the peninsulas and the lower slopes of the icecap leading up to Law Dome. The DEM has a cell size of 10 m. proprietary windmill_bathy_surveys_1 Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands AU_AADC STAC Catalog 1997-02-01 1997-03-31 110.515, -66.297, 110.565, -66.258 https://cmr.earthdata.nasa.gov/search/concepts/C1214311438-AU_AADC.umm_json Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands. This dataset resulted from bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands, carried out in February and March 1997 as part of ASAC Project 2201. The surveys were carried out by Jonny Stark and Tim Ryan in the workboat the 'Southern Comfort'. proprietary -winston_bathy_1 A bathymetric survey of Winston Lagoon ALL STAC Catalog 1987-01-09 1987-01-14 73.23557, -53.20274, 73.83911, -52.95006 https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted. Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m. proprietary winston_bathy_1 A bathymetric survey of Winston Lagoon AU_AADC STAC Catalog 1987-01-09 1987-01-14 73.23557, -53.20274, 73.83911, -52.95006 https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted. Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m. proprietary +winston_bathy_1 A bathymetric survey of Winston Lagoon ALL STAC Catalog 1987-01-09 1987-01-14 73.23557, -53.20274, 73.83911, -52.95006 https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted. Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m. proprietary wisperimpacts_1 Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -95.2426928, 33.2614038, -67.8781539, 48.2369386 https://cmr.earthdata.nasa.gov/search/concepts/C2175816611-GHRC_DAAC.umm_json The Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS dataset consists of condensed water contents, water vapor measurements, and isotope ratios in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in ASCII format from January 18, 2020, through February 28, 2023. proprietary wml_bilderstudie_1.0 Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789818010-ENVIDAT.umm_json "This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the ""WaMos meets LFI"" (WML) project." proprietary wmlganzeschweiz_1.0 WaMos meets LFI, ganze Schweiz ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789818071-ENVIDAT.umm_json The data consists of a forest visitor survey conducted at 50 plots in the whole of Switzerland, once during the winter- and once during the summer season. Physical forest characteristics according to the Swiss National Forest Inventory NFI were collected from the same plots in winter and summer. Visibility was measured using terrestrial laser scanning. At some plots, sound measurements were also conducted. proprietary @@ -21301,8 +21369,8 @@ wrfimpacts_1 Weather Research and Forecasting (WRF) Model IMPACTS GHRC_DAAC STAC wsl-drought-initiative-2018_1.0 Litterfall and pollen data of three LWF beech plots ENVIDAT STAC Catalog 2019-01-01 2019-01-01 6.65804, 46.58377, 9.06707, 47.22516 https://cmr.earthdata.nasa.gov/search/concepts/C2789818298-ENVIDAT.umm_json This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018). Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen). __Datafile:__ _LWF_beech_plots_litterfall_pollen.xlsx_ 1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion. 2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models. proprietary wslintern-article-envidat-supports-open-science_1.0 EnviDat Supports Open Science ENVIDAT STAC Catalog 2020-01-01 2020-01-01 8.4546488, 47.3605728, 8.4546488, 47.3605728 https://cmr.earthdata.nasa.gov/search/concepts/C2789818383-ENVIDAT.umm_json "The article ""EnviDat Supports Open Science"" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support. License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 ""No Rights Reserved"" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions." proprietary wwllnmth_1 World Wide Lightning Location Network (WWLLN) Monthly Thunder Hour Data GHRC_DAAC STAC Catalog 2013-01-01 2023-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3301410475-GHRC_DAAC.umm_json The World Wide Lightning Location Network (WWLLN) has monitored global lightning since late 2004. Since 2013, the number of global WWLLN sensors has remained largely consistent. This WWLLN Monthly Thunder Hour dataset is calculated from lightning detections from 1 January 2013 onward and is an ongoing dataset. A thunder hour is an hour during which thunder can be heard at a given location. Thunder hours represent a historical measure of lightning occurrence and a metric of thunderstorm frequency that is comparatively less sensitive to geographic variations in the detection capabilities of a lightning location system. Thunder hours are the number of hours in a given month during which at least two WWLLN strokes were observed within 15 km of each grid point. Each file includes the monthly accumulated thunder hours for one year. The data are provided at 0.05° latitude and longitude resolution. proprietary -wygisc_wolphoyo Aerial Photos for Crazy Woman and Clear Creek Watersheds ALL STAC Catalog 1970-01-01 -107, 44, -106.36, 44.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json The purpose of this data was to provide a base layer of aerial photos at the watershed scale for two areas used as part of a the Wyoming Open Land pilot area. Digital and registered aerial photos of Crazy Woman and Clear Creek Watersheds, Wyoming. Each photo represents approximatley one-quarter of a U.S.G.S. Topographic map (north-east, north-west, south-each and south-west quarters). TIFF image format. proprietary wygisc_wolphoyo Aerial Photos for Crazy Woman and Clear Creek Watersheds SCIOPS STAC Catalog 1970-01-01 -107, 44, -106.36, 44.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json The purpose of this data was to provide a base layer of aerial photos at the watershed scale for two areas used as part of a the Wyoming Open Land pilot area. Digital and registered aerial photos of Crazy Woman and Clear Creek Watersheds, Wyoming. Each photo represents approximatley one-quarter of a U.S.G.S. Topographic map (north-east, north-west, south-each and south-west quarters). TIFF image format. proprietary +wygisc_wolphoyo Aerial Photos for Crazy Woman and Clear Creek Watersheds ALL STAC Catalog 1970-01-01 -107, 44, -106.36, 44.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json The purpose of this data was to provide a base layer of aerial photos at the watershed scale for two areas used as part of a the Wyoming Open Land pilot area. Digital and registered aerial photos of Crazy Woman and Clear Creek Watersheds, Wyoming. Each photo represents approximatley one-quarter of a U.S.G.S. Topographic map (north-east, north-west, south-each and south-west quarters). TIFF image format. proprietary yield-15_1.0 Yield ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817175-ENVIDAT.umm_json Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary yield_and_mortality-13_1.0 Yield and mortality ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817288-ENVIDAT.umm_json Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled, died or disappeared between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary yield_and_mortality_star-163_1.0 Yield and mortality* ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817402-ENVIDAT.umm_json Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were used, died or disappeared between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary