Estimating Grazing Land Acres Across the Contiguous United States Using Machine Learning Methods

Quantifying the extent of rangeland and pastureland (collectively termed grazing lands herein) in the US is a critical first step in many grazing lands assessments. This research presents a model-assisted framework to estimate grazing land acreage within arbitrary geographic boundaries by integrating high quality survey data with satellite-based raster geospatial data. Leveraging the image photo interpretation data from the USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) survey as a reference dataset, we use machine learning to fuse NRI point level data with auxiliary data from the satellite-based Cropland Data Layer (CDL) to enhance the precision of acreage estimates of grazing lands. The methodology includes three steps: (1) modeling the relationship between NRI rangeland and pastureland indicators and CDL variables; (2) generating a high-resolution rangeland and pastureland probabilities map across the contiguous US; and (3) summarizing these probabilities to calculate the acreage of rangeland and pastureland for specific areas of interest. This approach provides researchers and land managers with a scalable tool to define grazing land extents within a self-selected study area, ensuring that subsequent resource characteristics or condition assessments are representative and spatially accurate.