10.3334/ORNLDAAC/1774
Thaler, E.
E.
Thaler
Larsen, I.
I.
Larsen
Yu, Q.
Q.
Yu
Remote Sensing Derived Topsoil and Agricultural Economic Losses, Midwestern USA
Soil Collection
ORNL Distributed Active Archive Center
2020
AGRICULTURE > AGRICULTURAL PLANT SCIENCE > CROP/PLANT YIELDS
LAND SURFACE > TOPOGRAPHY > TOPOGRAPHIC EFFECTS
LAND SURFACE > SOILS > SOIL EROSION
LAND SURFACE > SOILS > CARBON > SOIL ORGANIC CARBON (SOC)
HUMAN DIMENSIONS > ECONOMIC RESOURCES > AGRICULTURE PRODUCTION
AGRICULTURE > SOILS > CARBON
WORLDVIEW-3 > Computer
WORLDVIEW-2 > Computer
LANDSAT > Computer
farms
crop losses
corn
soil organic carbon
crop productivity
economic losses
ORNL DAAC
0000-00-00
0000-00-00
2003-04-01/2018-07-31
en
https://daac.ornl.gov/SOILS/guides/TopSoil_Erosion_MidWest_US.html
https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=19
Data Files
506.13901999999996 MB
GTiff
CSV
This dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss, including 4-m gridded estimates of study sites' elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil are provided. Topsoil loss at the county- and state-levels was derived from analyses of agricultural land at selected sites across the study area. From WorldView imagery, 759 fields were identified that had exposed bare soil (210 km2) and these were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field. From LiDAR-derived digital elevation models at 4-m resolution, acquired from 2003-2018, topography measures including elevation (m), curvature (m-1), and slope (deg) were extracted over the entire study area. Within each of the 28 study sites, the SOCI and topographic curvature values were extracted from co-located pixels. Topsoil loss was estimated from the relationship between subsoil exposure and topography and averaged across each site. Modeling was used to up-scale and predict topsoil and economic losses at the county and state-levels across the entire 375,000 km2 study area. The data have been used to demonstrate a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes.
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