APPLYING MACHINE LEARNING TO CROPLAND DATA LAYER FOR AGRO-GEOINFORMATION DISCOVERY

The Cropland Data Layer (CDL) is currently the only sub-field level high resolution crop-specific land cover data product over the entire conterminous United States (CONUS). It has been widely used in agricultural industry, business decision support, research, and education worldwide. However, CDL data has its limitations. It is an end-of-season land cover map which is not available within growing season. Moreover, CDLs in early years have many misclassified pixels (relatively low accuracy) due to cloud cover and lack of satellite images. This paper will present the studies of using machine learning technique to address these issues in CDL data. Specifically, we will present the design and implementation of a machine learning model for agro-geoinformation discovery from CDL. Several application scenarios of the proposed model, including prediction of crop cover, crop acreage estimation, in-season crop mapping, and refinement of the early-year CDL data, are demonstrated and discussed.