CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support

By Weiguo Han, Zhengwei Yang, Liping Di, Richard Mueller. 2012

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Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis (2018, methodology): “For TA3, we made use of CropScape – Cropland Data Layer (CDL) for 2016, provided by the United States Department of Agriculture, National Agricultural Statistics Service (Boryan et al., 2011; Han et al., 2012), in order to classify randomly generated samples.”
Multigenerational migration of fall armyworm, a pest insect (2019, methodology): “We downloaded corn planting distribution for 2011–2014 from the U.S. Department of Agriculture—National Agricultural Statistics Service CropScape—Cropland Data Layer (CDL; http:// nassgeodata.gmu.edu/CropScape/; Han et al. 2012, 2014).”
A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (2018, neutral): “…there is no unique cropland layer available for Australia and China like United States Department of Agriculture (USDA) National Agricultural Statistical Service (NASS) 30 m Cropland Data Layer (CDL) derived from Landsat for USA (Johnson and Mueller, 2010; Boryan et al., 2011; Han et al., 2012).”