데이터셋 상세
미국
Landsat-based Illustrative implementation of Satellite Psychrometry for ET Mapping
Remote sensing-based evapotranspiration (ET) can be derived using various methods, from soil moisture accounting to vegetation-index based approaches to simple and complex surface energy balance techniques. Due to the complexity of fully representing and parameterizing ET sub-processes, different models tend to diverge in their estimations. However, most models appear to provide reasonable estimations that can meet user requirements for seasonal water use estimation and drought monitoring. One such model is the Operational Simplified Surface Energy Balance (SSEBop). This study presents a formulation of the SSEBop model using the psychrometric principle for vapor pressure/relative humidity measurements where the “dry-bulb” and “wet-bulb” equivalent readings can be obtained from satellite-based land surface temperature estimates.
데이터 정보
연관 데이터
Annual SSEBop ET rasters at Landsat scale from 2010-2019 for the CONUS
공공데이터포털
CONUS-wide actual ET (ETa) from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model (version 4) in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010-2019).
Annual SSEBop ET rasters at Landsat scale from 2010-2019 for the CONUS
공공데이터포털
CONUS-wide actual ET (ETa) from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model (version 4) in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010-2019).
Landsat and Sentinel-2 satellite data fusion-derived evapotranspiration maps of Palo Verde Irrigation District, California, USA
공공데이터포털
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 ET dataset was generated from the proposed Landsat-Sentinel data fusion (L-S) approach by applying ETf images from Landsat and Sentinel. The scripts (two) used to generate these three ET datasets are included – one script for processing SSEBop model to generate ET maps from Lonly and another script for generating ET maps from Lint and L-S approach.
Satellite-based Water Use Dynamics Using Historical Landsat Data (1984-2014) in the Southwestern United States
공공데이터포털
Historical (1984-2014) Landsat-based ET maps were generated for Palo Verde Irrigation District (PVID) and eight other sub-basins in parts of Middle and Lower Central Valley, California. A total of 3,396 Landsat images were processed using the Operational Simplified Surface Energy balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study areas over the 31 years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively strong association between SSEBop ET and validation datasets. Historical trend analysis of seven agro-hydrologic variables were done using the Seasonal Mann-Kendall test.
Satellite-based Water Use Dynamics Using Historical Landsat Data (1984-2014) in the Southwestern United States
공공데이터포털
Historical (1984-2014) Landsat-based ET maps were generated for Palo Verde Irrigation District (PVID) and eight other sub-basins in parts of Middle and Lower Central Valley, California. A total of 3,396 Landsat images were processed using the Operational Simplified Surface Energy balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study areas over the 31 years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively strong association between SSEBop ET and validation datasets. Historical trend analysis of seven agro-hydrologic variables were done using the Seasonal Mann-Kendall test.
Operational Global Actual Evapotranspiration using the SSEBop model
공공데이터포털
The spreadsheet consists of all the data and statistics used in the publication 'Operational Global Actual Evapotranspiration: Development, Evaluation and Dissemination'.
Operational Global Actual Evapotranspiration using the SSEBop model
공공데이터포털
The spreadsheet consists of all the data and statistics used in the publication 'Operational Global Actual Evapotranspiration: Development, Evaluation and Dissemination'.
Actual Evapotranspiration at Landsat scale at CONUS scale for 2010-2019
공공데이터포털
The spreadsheet includes a tab for each figure and table in the publication titled "Mapping Actual Evapotranspiration using Landsat for the Conterminous United States: Google Earth Engine Implementation and Assessment of the SSEBop Model" by Senay et al. 2021. Each tab includes the graphic and the data used to create it.
Actual Evapotranspiration at Landsat scale at CONUS scale for 2010-2019
공공데이터포털
The spreadsheet includes a tab for each figure and table in the publication titled "Mapping Actual Evapotranspiration using Landsat for the Conterminous United States: Google Earth Engine Implementation and Assessment of the SSEBop Model" by Senay et al. 2021. Each tab includes the graphic and the data used to create it.
Daily SSEBop Evapotranspiration Data from 2000 to Present
공공데이터포털
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 – 2018. The second phase will create the ET product operationally on a daily time scale. The corresponding data files for each day, geotiff and meta data file, are compressed as a zip file for download. The values for the daily ET data are scaled by a factor of 1000. NoData pixels are indicated as NoData(9999). Daily ETa data are produced at 1 km resolution and are available at: https://earlywarning.usgs.gov/ssebop/modis/daily
목록