Eleven Continuous Years of Annual National Cropland Data Layer - Land Cover Products

Authors: Rick Mueller*, USDA/National Agricultural Statistics Service, Patrick Willis, usda/nass
Topics: Agricultural Geography, Land Use and Land Cover Change, Remote Sensing
Keywords: land cover, agriculture, remote sensing, Cropland Data Layer, CropScape
Session Type: Paper
Day: 4/7/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded

The Cropland Data Layer Program within the United States Department of Agriculture’s National Agricultural Statistics Service is a 30-meter agricultural-specific national land cover product. The 2018 Cropland Data Layer product was released in February of 2019. The Cropland Data Layer product has been produced annually since 2008, over the conterminous United States. Upon completion of the growing season, the Cropland Data Layer product was released into the public domain via the CropScape https://nassgeodata.gmu.edu/CropScape portal. CropScape allows users to explore, visualize, and query the geospatial raster data products. Exploring the Cropland Data Layer in CropScape provides users the opportunity to study cultivation practices, crop intensification and rotations, and the changing trends and localities in production agriculture. The Cropland Data Layer is a supervised land-cover classification utilizing a decision tree machine learning approach using optical satellites while leveraging ground reference data collected from the Farm Service Agency, other government agencies, such as the United States Geological Survey, and cooperative industry partnerships. Medium resolution satellites such as Landsat 8, Disaster Monitoring Constellation Deimos-1 and UK2, Resourcesat-2 LISS-III, and Sentinel-2 were used to collect imagery throughout the North American growing season. This paper focuses on the continuous Cropland Data Layer product improvement process, including leveraging cooperative industry partnerships, obtaining improved remote sensing data, improving the methods used in classification, and leveraging historical data to improve identification of specialty and locally grown crops.

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