Description:

Snowpack plays a significant role in the hydrologic cycle over High Mountain Asia (HMA). As a vital water resource, the distribution of snowpack volume also impacts the water availability for downstream populations. To assess the regional water balance, it is important to characterize the spatio-temporal distribution of water storage in the HMA snowpack. This HMA snow reanalysis data set contains daily estimates of posterior snow water equivalent (SWE), fractional snow covered area (fSCA), snow depth (SD), etc.

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Temporal Extent:

Start: 1999-10-01T00:00:00.000Z

End: 2017-09-30T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: 27 60 45 105

NSIDC_CPRD Short Name: HMA_SR_D Version ID: 1 Unique ID: C3272554109-NSIDC_CPRD

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Description:

This data set, part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, consists of ice velocities at 240 m resolution, generated from Landsat 4, 5, 7, and 8 optical image pairs. Velocities were derived using the autonomous Repeat Image Feature Tracking algorithm (autoRIFT) processing chain. Data are available for all land ice areas larger than 5 square km, spanning the period from 1985 to 2018 (subject to image availability and quality). Data scarcity and/or low radiometric quality are significant limiting factors for many regions during the earlier years of the data record. Annual, global coverage is nearly complete after the 2013 launch of Landsat 8.

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Temporal Extent:

Start: 1982-11-12T00:00:00.000Z

End: 2019-04-27T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: -90 -180 90 180

NSIDC_CPRD Short Name: NSIDC-0775 Version ID: 1 Unique ID: C3291016291-NSIDC_CPRD

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Description:

NASA's Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Land Cover Mapping and Estimation ([GLanCE](https://sites.bu.edu/measures/)) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of [seven continental grids](https://measures-glance.github.io/glance-grids/) that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, Oceania, and Africa are available. This dataset includes global land cover and land cover change data for the period 2001 to 2019. Africa is extended through 2021. GLanCE is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the [land cover class](https://sites.bu.edu/measures/project-overview/methods/), the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule. Known Issues * Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * All GLnanCE granules should contain 7 products except the year 2001, which contain 4, as there are no change products (chgDate, EVI2chg, prevClass) for the initial year of data. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. * High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. * The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

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Temporal Extent:

Start: 2001-07-01T00:00:00.000Z

End: 2021-07-01T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: -90 -180 90 180

LPCLOUD Short Name: GLanCE30 Version ID: 001 Unique ID: C2763264675-LPCLOUD

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Description:

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 <a href="http://nsidc.org/data/measures/gimp">Greenland Ice Mapping Project (GrIMP)</a> for related data.

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Temporal Extent:

Start: 1972-09-16T00:00:00.000Z

End: 2021-03-25T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: 60 -75 83 -14

NSIDC_CPRD Short Name: NSIDC-0642 Version ID: 2 Unique ID: C3292900075-NSIDC_CPRD

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Description:

The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). 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 GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. Known Issues * Known issues, including constraints and limitations, are provided on page 20 of the ATBD.

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Temporal Extent:

Start: 2013-01-01T00:00:00.000Z

End: 2016-12-31T23:59:59.999Z

Spatial Extent:

Minimum Bounding Rectangle: 0 -180 90 180

LPCLOUD Short Name: GFSAD30EUCEARUMECE Version ID: 001 Unique ID: C2763261648-LPCLOUD

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