• Zhuoting Wu1,
  • Prasad S. Thenkabail2,
  • James P. Verdin3
  • 1Western Geographic Science Center, US Geological Survey, Merriam-Powell Center for Environmental Research, Northern Arizona University, Flagstaff, AZ 86001
  • 2Western Geographic Science Center, US Geological Survey, Flagstaff, AZ 86001
  • 3USGS EROS Data Center, Sioux Falls, SD 57198


Increasing pressure to feed the growing population with scarce water resources requires accurate and routine cropland mapping. This paper develops and implements a rule-based automated cropland classification algorithm (ACCA) using multi-sensor remote sensing data. Pixel-by-pixel accuracy assessments showed that ACCA produced an overall accuracy of ⩾96 percent (Khat = 0.8) when tested using independent data layers. Furthermore, ACCA-generated county cropland areas showed high agreement (R-square values ⩾0.94) when compared with three independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data for the 58 counties in California. Our results demonstrate the ability of ACCA to generate cropland extent and areas over space and time, in an automated fashion with high degree of accuracies year after year, greatly contributing to food and water security analysis and decision making.

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Preview of Automated Cropland Classification Algorithm (ACCA) for California Using Multi-sensor Remote Sensing


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