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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<temporalExtentStart: string, seCorner: struct<crs: string, latitude: double, longitude: double, height: null>, cmrId: string, globalId: string, fastrp_embedding_with_labels: list<item: double>, abstract: string, daac: string, nwCorner: struct<crs: string, latitude: double, longitude: double, height: null>, temporalFrequency: string, pagerank_global: double, temporalExtentEnd: string, shortName: string, longName: string, doi: string, landingPageUrl: string, url: string>
to
{'temporalExtentStart': Value(dtype='string', id=None), 'seCorner': {'crs': Value(dtype='string', id=None), 'latitude': Value(dtype='float64', id=None), 'longitude': Value(dtype='float64', id=None), 'height': Value(dtype='null', id=None)}, 'cmrId': Value(dtype='string', id=None), 'globalId': Value(dtype='string', id=None), 'fastrp_embedding_with_labels': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'abstract': Value(dtype='string', id=None), 'daac': Value(dtype='string', id=None), 'nwCorner': {'crs': Value(dtype='string', id=None), 'latitude': Value(dtype='float64', id=None), 'longitude': Value(dtype='float64', id=None), 'height': Value(dtype='null', id=None)}, 'temporalFrequency': Value(dtype='string', id=None), 'pagerank_global': Value(dtype='float64', id=None), 'shortName': Value(dtype='string', id=None), 'landingPageUrl': Value(dtype='string', id=None), 'longName': Value(dtype='string', id=None), 'doi': Value(dtype='string', id=None), 'temporalExtentEnd': Value(dtype='string', id=None)}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2108, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<temporalExtentStart: string, seCorner: struct<crs: string, latitude: double, longitude: double, height: null>, cmrId: string, globalId: string, fastrp_embedding_with_labels: list<item: double>, abstract: string, daac: string, nwCorner: struct<crs: string, latitude: double, longitude: double, height: null>, temporalFrequency: string, pagerank_global: double, temporalExtentEnd: string, shortName: string, longName: string, doi: string, landingPageUrl: string, url: string>
to
{'temporalExtentStart': Value(dtype='string', id=None), 'seCorner': {'crs': Value(dtype='string', id=None), 'latitude': Value(dtype='float64', id=None), 'longitude': Value(dtype='float64', id=None), 'height': Value(dtype='null', id=None)}, 'cmrId': Value(dtype='string', id=None), 'globalId': Value(dtype='string', id=None), 'fastrp_embedding_with_labels': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'abstract': Value(dtype='string', id=None), 'daac': Value(dtype='string', id=None), 'nwCorner': {'crs': Value(dtype='string', id=None), 'latitude': Value(dtype='float64', id=None), 'longitude': Value(dtype='float64', id=None), 'height': Value(dtype='null', id=None)}, 'temporalFrequency': Value(dtype='string', id=None), 'pagerank_global': Value(dtype='float64', id=None), 'shortName': Value(dtype='string', id=None), 'landingPageUrl': Value(dtype='string', id=None), 'longName': Value(dtype='string', id=None), 'doi': Value(dtype='string', id=None), 'temporalExtentEnd': Value(dtype='string', id=None)}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1412, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 988, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
type
string | id
string | labels
sequence | properties
dict |
|---|---|---|---|
node | 0 | [
"Dataset"
] | {
"temporalExtentStart": "1992-01-01T00:00:00.000Z",
"seCorner": {
"crs": "wgs-84",
"latitude": -79.7,
"longitude": 180,
"height": null
},
"cmrId": "C2617226208-POCLOUD",
"globalId": "dcf602c1-0e51-55f1-97fb-dbfb8a704c0f",
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],
"abstract": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-1 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP.",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 88.2,
"longitude": -180,
"height": null
},
"temporalFrequency": "Unknown",
"pagerank_global": 0.19266644908616193,
"shortName": "ERS-1_BYU_L3_OW_SIGMA0_ENHANCED",
"landingPageUrl": "https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ers1/open/L3/byu_scp/sigma0enhanced/docs/dLongERS1.html",
"longName": "ERS-1 Gridded Level 3 Enhanced Resolution Sigma-0 from BYU",
"doi": "10.5067/ERS1B-SNEN0",
"temporalExtentEnd": "1996-05-17T23:59:59.000Z"
} |
node | 1 | [
"Dataset"
] | {
"temporalExtentStart": "1996-09-15T00:00:00.000Z",
"seCorner": {
"crs": "wgs-84",
"latitude": -89,
"longitude": 180,
"height": null
},
"cmrId": "C2617226510-POCLOUD",
"globalId": "592102e7-47ed-52f0-bde4-7a9bfa18ed5b",
"fastrp_embedding_with_labels": [
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"abstract": "This NASA Scatterometer (NSCAT) satellite Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 4.45 km pixel resolution stored in SIR files; polar regions are gridded using a polar-stereographic technique. A non-enhanced version is provided at 22.25 km pixel resolution in a format known as GRD files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, polarization, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In additional to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. For more information, please visti: http://www.scp.byu.edu/docs/NSCAT_user_notes.html",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 89,
"longitude": -180,
"height": null
},
"temporalFrequency": "Unknown",
"pagerank_global": 0.19266644908616193,
"shortName": "NSCAT_BYU_L3_OW_SIGMA0_ENHANCED",
"landingPageUrl": "https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/nscat/open/L3/byu_scp/sigma0enhanced/docs/dLongNscat.html",
"longName": "NSCAT Gridded Level 3 Enhanced Resolution Sigma-0 from BYU",
"doi": "10.5067/NSBYU-SNEN0",
"temporalExtentEnd": "1997-06-29T23:59:59.999Z"
} |
node | 2 | [
"Dataset"
] | {
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"latitude": -79.7,
"longitude": 180,
"height": null
},
"cmrId": "C2617226211-POCLOUD",
"globalId": "9fa7db56-a9a2-5313-949e-4b30a3a2fbbf",
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"abstract": "This European Remote Sensing (ERS) Sigma-0 dataset is generated by the Scatterometer Climate Record Pathfinder (SCP) project at Brigham Young University (BYU) and is generated using a Scatterometer Image Reconstruction (SIR) technique developed by Dr. David Long at BYU. The dataset provides SIR processed Sigma-0 data from the ERS-2 C-band scatterometer, which is also known as the Active Microwave Instrument (AMI). AMI is a multimode radar operating at a frequency of 5.3 GHz (C-band), using vertically polarized antennas for both transmission and reception. The SIR technique results in an enhanced resolution image reconstruction and gridded on an equal-area grid (for non-polar regions) at 8.9 km pixel resolution stored in SIR files; polar regions are gridded at the same resolution using a polar-stereographic technique. A non-enhanced version is provided at 44.5 km pixel resolution in a format known as GRD (i.e., gridded) files. All files are produced in IEEE formatted binary. All data files are separated and organized by region, parameter, and sampling technique (i.e., SIR vs. GRD). The regions of China and Japan are combined into a single region. In addition to Sigma-0, various statistical parameters are provided for added guidance, including but not limited to: standard deviation, measurement counts, pixel time, Sigma-0 error, and average incidence angle. This dataset was once distributed on tape, but has been made available on FTP thanks to the BYU SCP. For more information, please visit: http://www.scp.byu.edu/docs/ERS_user_notes.html",
"daac": "NASA/JPL/PODAAC",
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"shortName": "ERS-2_BYU_L3_OW_SIGMA0_ENHANCED",
"landingPageUrl": "https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/ers2/open/L3/byu_scp/sigma0enhanced/docs/dLongERS2.html",
"longName": "ERS-2 Gridded Level 3 Enhanced Resolution Sigma-0 from BYU",
"doi": "10.5067/ERS2B-SNEN0",
"temporalExtentEnd": "2001-12-30T23:59:59.000Z"
} |
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"abstract": "The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions.",
"daac": "NASA/JPL/PODAAC",
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"longName": "GHRSST Level 2P Western Atlantic Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite (GDS version 2)",
"doi": "10.5067/GHG13-2PO02",
"temporalExtentEnd": "2018-01-08T15:29:19.000Z"
} |
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"abstract": "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.",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 79,
"longitude": 64,
"height": null
},
"temporalFrequency": "hourly",
"pagerank_global": 0.21391644908616192,
"shortName": "MTSAT2-OSPO-L2P-v1.0",
"landingPageUrl": "http://www.star.nesdis.noaa.gov/sod/mecb/goes_validation/test/top_background.php",
"longName": "GHRSST Level 2P Western Pacific Regional Skin Sea Surface Temperature from the Multifunctional Transport Satellite 2 (MTSAT-2) (GDS version 2)",
"doi": "10.5067/GHMT2-2PO02",
"temporalExtentEnd": "2015-12-04T11:15:00.000Z"
} |
node | 5 | [
"Dataset"
] | {
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},
"cmrId": "C2499940522-POCLOUD",
"globalId": "2af067fb-a040-5e22-9d3e-f25267c07af0",
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"abstract": "A regional Group for High Resolution Sea Surface Temperature (GHRSST) Level 3 Collated (L3C) dataset for the America Region (AMERICAS) based on retrievals from the GOES-13 Imager on board GOES-13 satellite. \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\ntime from GOES 13 in East position. GOES 13 imager level 1 data are acquired at Meteo-\nFrance/Centre de Meteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system.\nSST is retrieved from the GOES 13 infrared channels (3.9 and 10.8 micrometer) using a multispectral\nalgorithm. Due to the lack of 12 micrometer channel in the GOES 13 imager, SST retrieval is not possible\nin daytime conditions. Atmospheric profiles of water vapor and temperature from a numerical\nweather prediction model, together with a radiatiave transfer model, are used to correct the\nmultispectral algorithm for regional and seasonal biases due to changing atmospheric conditions.\nEvery 30 minutes slot is processed at full satellite resolution. The operational products are then\nproduced by remapping over a 0.05 degree regular grid (60S-60N and 135W-15W) SST fields\nobtained by aggregating 30 minute SST data available in one hour time, and the priority being\ngiven to the value the closest in time to the product nominal hour. The product format is compliant\nwith the GHRSST Data Specification (GDS) version 2.",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 60,
"longitude": -135,
"height": null
},
"temporalFrequency": "Unknown",
"pagerank_global": 0.21391644908616192,
"shortName": "GOES13-OSISAF-L3C-v1.0",
"landingPageUrl": "http://www.osi-saf.org",
"longName": "GHRSST Level 3C sub-skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES 13) Imager in East position (GDS V2) produced by OSI SAF",
"doi": "10.5067/GHG13-3CO01",
"temporalExtentEnd": "2017-12-14T15:30:01.000Z"
} |
node | 6 | [
"Dataset"
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"abstract": "A global 1 km Group for High Resolution Sea Surface Temperature (GHRSST) Level 2P dataset based on multi-channel sea surface temperature (SST) retrievals generated in real-time from the Advanced Very High Resolution Radiometer (AVHRR) on the European Meteorological Operational-A (MetOp-A)satellite (launched 19 Oct 2006). \n\nThe European Organization for the Exploitation of Meteorological Satellites (EUMETSAT),\nOcean and Sea Ice Satellite Application Facility (OSI SAF) is producing SST products in near real\ntime from Metop/AVHRR. Global AVHRR level 1b data are acquired at Meteo-France/Centre de\nMeteorologie Spatiale (CMS) through the EUMETSAT/EUMETCAST system. SST is retrieved\nfrom the AVHRR infrared channels (3.7, 10.8 and 12.0 micrometer) using a multispectral algorithm.\nAtmospheric profiles of water vapor and temperature from a numerical weather prediction model,\ntogether with a radiatiave transfer model, are used to correct the multispectral algorithm for\nregional and seasonal biases due to changing atmospheric conditions. This product is delivered at\nfull resolution in satellite projection as metagranule corresponding to 3 minutes of acquisition. The\nproduct format is compliant with the GHRSST Data Specification (GDS) version 2.",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 90,
"longitude": -180,
"height": null
},
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"pagerank_global": 0.21391644908616192,
"shortName": "AVHRR_SST_METOP_A-OSISAF-L2P-v1.0",
"landingPageUrl": "http://www.osi-saf.org",
"longName": "GHRSST Level 2P sub-skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on Metop satellites (currently Metop-A) (GDS V2) produced by OSI SAF",
"doi": "10.5067/GHAMA-2PO02",
"temporalExtentEnd": "2016-11-23T11:52:04.000Z"
} |
node | 7 | [
"Dataset"
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"abstract": "The Saildrone Arctic 2019 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2019 Saildrone Arctic campaign featured six Saildrone USVs (jointly funded by NOAA and NASA) deployed during a 150-day cruise in the Bering and Chukchi Seas, spanning from 14 May 2019 to 11 October 2019. The primary mission objective for 2019 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, which could lead to significant improvements in modeling of diurnal warming and understanding of the marginal ice zones. Additionally, these new data will provide additional Arctic SST observations to benefit SST algorithm development and validation, and for studies of air- sea-ice interactions. Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2019_Arctic_Cruise_Report.pdf <p>\r\nDuring the Arctic campaign, NASA-funded Saildrones SD-1036 and SD-1037 undertook transects in the Chukchi Sea, approaching the sea ice edge to measure air-sea heat and momentum fluxes in the ocean near sea ice and to validate satellite sea-surface temperature measurements in the Arctic. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.<p>\r\nThe Saildrone Arctic 2019 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies. \r\n",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 75.5,
"longitude": -168.7,
"height": null
},
"temporalFrequency": "Unknown",
"pagerank_global": 0.19266644908616193,
"shortName": "SAILDRONE_ARCTIC",
"landingPageUrl": "http://podaac.jpl.nasa.gov/saildrone",
"longName": "Saildrone Arctic field campaign surface and ADCP measurements for NOPP-MISST project",
"doi": "10.5067/SDRON-NOPP0",
"temporalExtentEnd": "2019-10-11T18:30:01.000Z"
} |
node | 8 | [
"Dataset"
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"height": null
},
"cmrId": "C2491772162-POCLOUD",
"globalId": "4a26c4bc-222a-5c74-b989-9cf4edb6b4d6",
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"doi": "10.5067/SDRON-ATOM0",
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} |
node | 9 | [
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"abstract": "Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. \r\nThe saildrone Baja campaign was a 60-day cruise from San Francisco Bay, down along the US/Mexico coast to Guadalupe Island and back again over the period 11 April 2018 to 11 June 2018. Repeat surveys were taken around NDBC moored buoys, and during the final week of the cruise a targeted front was sampled. Scientific objectives included studies of upwelling and frontal region dynamics, air-sea interactions, and diurnal warming effects, while its validation objectives included establishing the utility of data from the saildrone platform for assessment of satellite data accuracy and model assimilation. During the Baja campaign, the single deployed saildrone was equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface.\r\nThis Saildrone Baja dataset is comprised of one data file with the saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) for the entire cruise at 1 minute temporal resolution. A second file contains the ADCP current vector data that is depth-resolved to 100m at 2m intervals and binned temporally at 5 minute resolution. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.",
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"doi": "10.5067/SDRON-SURF0",
"temporalExtentEnd": "2018-06-11T20:17:26.000Z"
} |
node | 10 | [
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"abstract": "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature (SST) analysis produced daily on an operational basis at the Canadian Meteorological Center. This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.",
"daac": "NASA/JPL/PODAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 90,
"longitude": -180,
"height": null
},
"temporalFrequency": "daily",
"pagerank_global": 0.2776664490861619,
"shortName": "CMC0.2deg-CMC-L4-GLOB-v2.0",
"landingPageUrl": null,
"longName": "GHRSST Level 4 CMC0.2deg Global Foundation Sea Surface Temperature Analysis (GDS version 2)",
"doi": "10.5067/GHCMC-4FM02",
"temporalExtentEnd": "2017-03-18T00:00:00.000Z"
} |
node | 11 | [
"Dataset"
] | {
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"longitude": 180,
"height": null
},
"cmrId": "C2540275683-LPCLOUD",
"globalId": "ac139cfa-2b7a-5e1d-a995-1d2beefa5449",
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"abstract": "The MCD43D62 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. \r\n\r\nMCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon.\r\n\r\nMCD43D62 is the NBAR for MODIS band 1. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n",
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"doi": "10.5067/MODIS/MCD43D62.061",
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} |
node | 12 | [
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"abstract": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. \r\n\r\nThe MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic (ISO), volumetric (VOL), geometric (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data.\r\n\r\nUsers are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications (https://www.umb.edu/spectralmass/terra_aqua_modis/v006).\r\n\r\nThe MCD43 product is not recommended for solar zenith angles beyond 70 degrees.\r\n\r\nThe MODIS BRDF/Albedo products have achieved stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\nObservations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period.\r\n*\tMCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period.\r\n*\tBetter quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day.\r\n*\tThe MCD43 products use L2G-lite surface reflectance as input.\r\n*\tIn cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel.\r\n*\tCMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo.\r\n\r\nImportant Quality Information\r\nThe incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted down stream products particularly over arid bright surfaces (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/displayCase.cgi?esdt=MOD09&caseNum=PM_MOD09_20010&caseLocation=cases_data&type=C6). This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products.\r\n\r\n\r\n\r\n",
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"shortName": "MCD43GF",
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"longName": "MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V006",
"doi": "10.5067/MODIS/MCD43GF.006",
"temporalExtentEnd": "2017-12-31T23:59:59.999Z"
} |
node | 13 | [
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"abstract": "The MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products (MCD19A1CMGL (https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and MCD19A1CMGO (https://doi.org/10.5067/MODIS/MCD19A1CMGO.061)). \r\n\r\nThe MCD19A2CMG AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, column water vapor for Terra, column water vapor for Aqua, average cloud fraction, available AOD, satellite overpass times, line and sample number, offset, and number of AOD records. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. ",
"daac": "LP DAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 90,
"longitude": -180,
"height": null
},
"temporalFrequency": "daily",
"pagerank_global": 0.15088217237078042,
"shortName": "MCD19A2CMG",
"landingPageUrl": "https://doi.org/10.5067/MODIS/MCD19A2CMG.061",
"longName": "MODIS/Terra+Aqua AOD and Water Vapor from MAIAC, Daily L3 Global 0.05Deg CMG V061",
"doi": "10.5067/MODIS/MCD19A2CMG.061",
"temporalExtentEnd": null
} |
node | 14 | [
"Dataset"
] | {
"temporalExtentStart": "1984-03-12T00:00:00.000Z",
"seCorner": {
"crs": "wgs-84",
"latitude": -90,
"longitude": 180,
"height": null
},
"cmrId": "C2763261610-LPCLOUD",
"globalId": "bf3e0ad8-3e3d-526e-8480-98e3d92ac4be",
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"abstract": "The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program. The GFCC Surface Reflectance Estimates Multi-Year Global dataset is derived from the enhanced Global Land Survey (GLS) datasets for epochs centered on the years 1990, 2000, 2005, and 2010. The GLS datasets are composed of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images at 30 meter resolution. Data available for this product represent the best available “leaf-on” date during the peak growing season. The original GLS datasets were enhanced with supplemental Landsat images when data were incomplete for the epoch or inadequate for analysis due to acquisition during “leaf-off” seasons. The enhanced GLS data were acquired June 1984 through August 2011. Atmospheric corrections were applied to seven visible bands to estimate surface reflectance by compensating for the scattering and absorption of radiance by atmospheric conditions. GFCC30SR is a multi-file data product. The surface reflectance data products are used as source data for other datasets in the GFCC collection.\r\n\r\nFor each available date, data files are delivered in a zip folder that consists of six surface reflectance bands, a Top of Atmosphere temperature band, an Atmospheric Opacity layer, and the Landsat Surface Reflectance Quality layer. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/146/GFCC_ATBD.pdf). \r\n",
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"doi": "10.5067/MEASURES/GFCC/GFCC30SR.001",
"temporalExtentEnd": "2011-12-25T23:59:59.999Z"
} |
node | 15 | [
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"abstract": "The MCD43D25 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D25 is the BRDF isotropic parameter for the MODIS NIR broadband. The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for the MODIS NIR broadband. \r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
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"doi": "10.5067/MODIS/MCD43D25.061",
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} |
node | 16 | [
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"abstract": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Black-Sky Albedo for ShortWave (VNP43D65) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. \r\n\r\nVNP43D54 through VNP43D79 are the albedo products of the VNP43D BRDF/Albedo product suite. Black-sky albedo (BSA) and white-sky albedo (WSA) values are provided for the nine VIIRS moderate resolution bands (M1 through M5, M7, M8, M10, and M11) along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA3 (https://doi.org/10.5067/VIIRS/VNP43MA3.001) product. In addition to the bands included in VNP43MA3, this product suite includes albedo values for the VIIRS Day/Night Band (DNB). The black-sky albedo (directional hemispherical reflectance) is defined as albedo in the absence of a diffuse component and is a function of solar zenith angle. White-sky albedo (bihemispherical reflectance) is defined as albedo in the absence of a direct component when the diffuse component is isotropic. Details regarding methodology are available on the VNP43MA3 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D65 is the BSA for the VIIRS shortwave broadband (1.61 μm).",
"daac": "LP DAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 90,
"longitude": -180,
"height": null
},
"temporalFrequency": "daily",
"pagerank_global": 0.1515369979714807,
"shortName": "VNP43D65",
"landingPageUrl": "https://doi.org/10.5067/VIIRS/VNP43D65.001",
"longName": "VIIRS/NPP BRDF/Albedo BSA at Solar Noon ShortWave Daily L3 Global 30ArcSec CMG V001",
"doi": "10.5067/VIIRS/VNP43D65.001",
"temporalExtentEnd": null
} |
node | 17 | [
"Dataset"
] | {
"temporalExtentStart": "2002-07-04T00:00:00.000Z",
"seCorner": {
"crs": "wgs-84",
"latitude": -90,
"longitude": 180,
"height": null
},
"cmrId": "C2343109950-LPCLOUD",
"globalId": "8ef7367e-8223-569b-b9c0-703f0591bd98",
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"abstract": "The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). \r\n\r\nValidation at stage 3 (https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Surface Reflectance products. Further details regarding MODIS land product validation for the MYD09 data product is available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09).\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
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"landingPageUrl": "https://doi.org/10.5067/MODIS/MYD09GQ.061",
"longName": "MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061",
"doi": "10.5067/MODIS/MYD09GQ.061",
"temporalExtentEnd": null
} |
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"abstract": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature and Emissivity (LST&E) Day Version 1 product (VNP21A1D) is compiled daily from daytime Level 2 Gridded (L2G) intermediate products. \r\n\r\nThe L2G process maps the daily (VNP21) (https://doi.org/10.5067/VIIRS/VNP21.001) swath granules onto a sinusoidal MODIS grid and stores all observations overlapping a gridded cell for a given day. The VNP21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all cloud-free observations that have good LST accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The 1 kilometer dataset is derived through resampling the native 750 meter VIIRS resolution in the input product.\r\n\r\nThe VNP21A1D product is developed synergistically with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST&E Version 6 product (MOD21A1D) (https://doi.org/10.5067/MODIS/MOD21A1D.006)) using the same input atmospheric products and algorithmic approach. The overall objective for NASA VIIRS products is to ensure the algorithms and products are compatible with the MODIS Terra and Aqua algorithms to promote the continuity of the Earth Observation System (EOS) mission. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/1332/VNP21_ATBD_V1.pdf). VIIRS LST&E products are available 2 months after acquisition due to latency of data inputs.\r\n\r\nThe VNP21A1D product contains seven Science Datasets (SDS): LST, quality control, emissivity for bands M14, M15, and M16, view zenith angle, and time of observation. A low-resolution browse image for LST is also available for each VNP21A1D granule.\r\n",
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"longName": "VIIRS/NPP Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid Day V001",
"doi": "10.5067/VIIRS/VNP21A1D.001",
"temporalExtentEnd": "2024-05-01T23:59:59.999Z"
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"abstract": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameter 1 Band M10 product (VNP43D22) is produced daily using 16 days of data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day, which is reflected in the file name. The VNP43D product suite is provided in a Climate Modeling Grid (CMG), which covers the entire globe for use in climate simulation models. Due to the large file size, each VNP43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the nine VIIRS moderate resolution bands along with the visible, near-infrared (NIR), and shortwave bands included in the VNP43MA1 (https://doi.org/10.5067/VIIRS/VNP43MA1.001) product is stored in a separate file as VNP43D01 through VNP43D36. In addition to the bands included in VNP43MA1, this product suite includes model parameters for the VIIRS Day/Night Band (DNB) as VNP43D37 through VNP43D39. Details regarding methodology are available on the VNP43MA1 product page and in the Algorithm Theoretical Basis Document (ATBD).\r\n\r\nVNP43D22 is the BRDF isotropic parameter for VIIRS band M10 (1.61 μm). The isotropic parameter, in conjunction with the volumetric and geometric parameters, is used to derive the BRDF/Albedo values for VIIRS band M10.",
"daac": "LP DAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 90,
"longitude": -180,
"height": null
},
"temporalFrequency": "daily",
"pagerank_global": 0.1515369979714807,
"shortName": "VNP43D22",
"landingPageUrl": "https://doi.org/10.5067/VIIRS/VNP43D22.001",
"longName": "VIIRS/NPP BRDF/Albedo Parameter 1 Band M10 Daily L3 Global 30 ArcSec CMG V001",
"doi": "10.5067/VIIRS/VNP43D22.001",
"temporalExtentEnd": null
} |
node | 20 | [
"Dataset"
] | {
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"latitude": -90,
"longitude": 180,
"height": null
},
"cmrId": "C2540268595-LPCLOUD",
"globalId": "5274396c-c028-581a-a873-dabb6808074a",
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"abstract": "The MCD43D30 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameter dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Sprectroradiometer (MODIS) data at 30 arc second (1,000 meter) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. Each of the three model parameters (isotropic, volumetric, and geometric) for each of the MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands included in MCD43C1 (https://doi.org/10.5067/MODIS/MCD43C1.061) are stored in a separate file as MCD43D01 through MCD43D30. \r\n\r\nMCD43D30 is the BRDF geometric parameterfor the MODIS shortwave broadband. The geometric parameter, in conjunction with the isotropic and volumetric parameters, is used to derive the BRDF/Albedo values for the MODIS shortwave broadband.\r\n\r\nImprovements/Changes from Previous Versions\r\n\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).",
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"abstract": "The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.",
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"abstract": "The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products (MYD17A2H)(https://doi.org/10.5067/MODIS/MYD17A2H.061) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR).\r\n\r\nThe MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H (https://doi.org/10.5067/modis/myd15a2h.061) is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year.\r\n\r\nStage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation has been achieved for MYD17 products.\r\n\r\nImprovements/Changes from Previous Versions\r\n* The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\r\n* A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\r\n* The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\r\n\r\n\r\n\r\n",
"daac": "LP DAAC",
"nwCorner": {
"crs": "wgs-84",
"latitude": 90,
"longitude": -180,
"height": null
},
"temporalFrequency": "Unknown",
"pagerank_global": 0.15088217237078042,
"shortName": "MYD17A3HGF",
"landingPageUrl": "https://doi.org/10.5067/MODIS/MYD17A3HGF.061",
"longName": "MODIS/Aqua Net Primary Production Gap-Filled Yearly L4 Global 500m SIN Grid V061",
"doi": "10.5067/MODIS/MYD17A3HGF.061",
"temporalExtentEnd": null
} |
End of preview.