Data partnership synergy: The Cropland Data Layer

Abstract

The US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has generated the Cropland Data Layer (CDL) product for more than twelve years, providing annual geospatial updates of the agricultural landscape across the US Heartland. The CDL program delivers acreage estimates based on regression modeling for decision support and provides a crop-specific geospatial dataset for the public domain. This model produces acreage estimates for statisticians and key decision makers in NASS Field Offices and the Agricultural Statistics Board, the official statistical reporting unit of USDA. The CDL program has grown incrementally as collaborative partnerships and technological efficiencies have increased via reengineering both the classification and estimation process. The CDL is now operational in 19 states for 2008 covering the major corn, soybeans, cotton, and wheat areas. Additionally, the CDL is generated multiple times during the growing season. This allows the program to take advantage of updated satellite imagery and updated farmer reported ground data, in consideration for the crop reports that NASS releases in June, August, September, and October. Satellite data have been used successfully for years by the CDL program to accurately identify crop types and produce acreage estimates at the state, district, and county levels. Continued expansion of the CDL program would be impossible without leveraging both satellite and ground truth data partnerships. The USDA/Foreign Agricultural Service/Satellite Image Archive (SIA) provides year round coverage of all major growing areas, while the USDA/Farm Services Agency (FSA) provides farmer reported agricultural specific ground truth. These data sharing partnerships are synergized by the CDL to provide a crop specific land cover classification utilizing regression tree software derived from two major inputs; 1) 56 meter multispectral imagery from Resourcesat-1 AWiFS and 2) ground truth training data from the FSA, Common Land Unit Program. Additionally, 3) ancillary datasets are incorporated into the classification method to improve non-agricultural land cover, including; The National Elevation Dataset; the 2001 National Land Cover Dataset (NLCD), the NLCD Imperviousness and Forest Canopy products. The current CDL product is a comprehensive land cover inventory produced operationally in-season annually.