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Full catalog/TopSoil_Erosion_MidWest_US_1774
TopSoil_Erosion_MidWest_US_1774·v1·dataset

Topsoil loss and farm losses in the US Midwest

Remote Sensing Derived Topsoil and Agricultural Economic Losses, Midwestern USA
land NASA ORNL_CLOUD Level 3 multiple
In plain English

What it measures. Estimates of how much topsoil has been lost across the US Corn Belt and the resulting economic losses to farmers from reduced crop productivity, plus supporting maps of terrain, slope, and soil exposure.

How it's made. Derived by combining high-resolution WorldView satellite imagery (to spot bare soil) with LiDAR-based terrain models from Landsat-era data, then scaling field findings up to county and state levels.

How & where you'd use it. Helps quantify the cost of soil erosion to agriculture and informs farming and land-management decisions in the Midwest.

What's measured

AGRICULTURE › AGRICULTURAL PLANT SCIENCE › CROP/PLANT YIELDSLAND SURFACE › SOILS › CARBON › SOIL ORGANIC CARBON (SOC)LAND SURFACE › SOILS › SOIL EROSIONHUMAN DIMENSIONS › ECONOMIC RESOURCES › AGRICULTURE PRODUCTIONLAND SURFACE › TOPOGRAPHY › TOPOGRAPHIC EFFECTSAGRICULTURE › SOILS › CARBON

Coverage & cadence

  • Time span2003-04-01 → 2018-07-31
  • Measured byLANDSAT (Computer) · WORLDVIEW-2 (Computer) · WORLDVIEW-3 (Computer)
  • Processing levelLevel 3
  • Spatial extent-97.3245, 40.0397, -86.986, 46.0461
  • Formatsmultiple
  • StatusCOMPLETE

What you can do with it

  • Track deforestation, fire scars and land-cover change
  • Monitor crop and vegetation health (NDVI/EVI)
  • Map how built-up vs. green an area is over time
Official description

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 are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil. 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 were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field within the sites. Topography measures, including elevation (m), curvature (m-1), and slope (deg), were extracted over the entire study area from LiDAR-derived digital elevation models at a 4 m resolution acquired from 2003-2018. 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.The relationship between topsoil loss and topographic curvature 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.

Get the data

topsoil_erosion_midwest_us_1774_access.py
import earthaccess
earthaccess.login(strategy="netrc")          # free Earthdata Login

results = earthaccess.search_data(
    short_name="TopSoil_Erosion_MidWest_US_1774",
    version="1",
    bounding_box=(-122.5, 37.2, -121.8, 37.9),  # your area (W,S,E,N)
    temporal=("2024-01-01", "2024-12-31"),       # your dates
)
files = earthaccess.open(results)   # stream straight from ORNL_CLOUD
Browsing CMR needs no login. Downloading or streaming bytes needs a free Earthdata Login + the earthaccess package.