Reflectance-based Model for Soybean Mapping in United States at Common Land Unit Scale with Landsat 8

Abstract

The objective of this study is to validate the feasibility of a reflectance-based model for soybean crop area classification in advance of the county scale statistics from the United States Department of Agriculture (USDA). This classification method is named Reflectance-based North American Model (RNAM). It operates through the analysis of the main physically driven characteristics of farm fields and their specific radiometric profile obtained from Operational Land Imager (OLI) onboard Landsat 8. The state area of Illinois/US was selected because it is the largest soybean producer and accounted for nearly 35 percent of the total soybeans production in US. Farm fields within a set of 32 counties were analyzed for six crop years between 2013 to 2018. Results obtained from RNAM were compared to official estimates of USDA at county level. Coefficients R2 ranged between 0.92 and 0.96, indicating good agreement of the estimates. Results from RNAM were also validated with the geospatial reference map Cropland Data Layer (CDL) of soybeans from USDA. The overall map accuracy found was 93.86% with Kappa Index of Agreement of 0.795. Thus, RNAM was considered able to provide timely thematic soybean maps, in late September, in advance of the county scale statistics from USDA.

Keywords

• small farms
• agriculture
• cropland data layer
• planting date
• rnam