Data

Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes due to land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush (Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover

The importance of monitoring shrublands to detect and understand changes through time is increasingly recognized as critical to management. This dataset focuses on ecological change observation over ten years of field observation at 134 plots within two sites that are located in Southwestern of Wyoming, USA from 2008-2018. At sites 1 and 3, 134 long-term field observation plots were measured annua

FGDC Metadata templates for 5 product areas during 2019 have been created to programmatically generate metadata for the products that are produced daily or weekly on the FireDanger production system. Fire Danger products are broken into 5 product areas (Fire Potential Index, Large Fire Probability, Predictive Service Area (provisional), Relative Greenness (weekly), and NDVI Greenness (weekly)). Me

The associated geotiff rasters represents the total actual evapotranspiration (ETa) from June through September for the years 2004, 2006, 2008-2010, and 2013-2016 for the entire Klamath Basin in southern Oregon. The ETa was created using Landsat imagery and the Operational Simplified Surface Energy Balance (SSEBop) model to estimate actual ET and the Python scripts to complete that process is also

Three ET datasets were generated to evaluate the potential integration of Landsat and Sentinel-2 data for improved ET mapping. The first ET dataset was generated by linear interpolation (Lint) of Landsat-based ET fraction (ETf) images of before and after the selected image dates. The second ET dataset was generated using the regular SSEBop approach using the Landsat image only (Lonly). The third E

Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the "Back-in-Time" (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically

NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the "Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for

This data release has been superseded by version 3.0, available here: https://doi.org/10.5066/P9MJVQSQ Quantifying Western U.S. shrublands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. The USGS NLCD team in collaboration with the BLM has produced the most comprehensive remote sensing-based quantification of Western U.S. shrubl

Daily SSEBop evapotranspiration at the Moderate Resolution Imaging Spectroradiometer (MODIS) scale was created for the CONUS. These data are published on the USGS earlywarning site (https://earlywarning.usgs.gov/ssebop/modis/daily). The first phase included the creation on historical actual daily ET data from 2000 to 2018. The second phase will create the ET product operationally on a daily time s

The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in U.S. Landsat Analysis Ready Data (ARD) tiles to produce Landsat Burned Area Products. The algorithm makes use of predictors derived from individual Landsat ARD tiles, lagged reference conditions, and change metrics between the tile and reference conditions. Tile-level products include pixel

The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies the spatial and temporal distribution of surface water in U.S. Landsat Analysis Ready Data (ARD) tiles to produce Landsat Dynamic Surface Water Extent (DSWE) Products. These acquisition based products are available for the conterminous U.S. (CONUS), Alaska, and Hawaii from 1982 to present. The DSWE package

The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies the spatial and temporal distribution of snow covered area in U.S. Landsat Analysis Ready Data (ARD) tiles to produce Landsat fractional Snow Covered Area (fSCA) Science Products. The fSCA packages include per-pixel percentages of snow cover (SNOW) as well as a revised cloud mask (REVCM) which flags variou