Digital sampling of remote sensing indices to optimize monitoring of US agroecosystems

Remote sensing vegetation indices such as the normalized difference vegetation index (NDVI) are widely used to monitor crop productivity and agroecosystem conditions. Yet, extracting full-field values across millions of fields from high resolution high frequency raster data remains computationally intensive at national scales, and is unrealistic for whole fields at the local level for continuous coverage of in-situ measurements. This study evaluates whether digital sampling (extracting NDVI from a limited number of stratified point locations within each field) can reliably approximate whole-field NDVI means while reducing processing demands. Using Sentinel-2 imagery from 2019 to 2021, we analyzed NDVI for ∼4.1 million crop-producing fields across the contiguous United States and compared whole-field NDVI with sampling schemes of 1 to 6 stratified points. Accuracy increased nonlinearly as points were added, with the largest improvement occurring when increasing from one to two points. Across the continental United States (CONUS), three points were generally sufficient to exceed an R 2 of 0.90, though performance varied by Long-Term Agroecosystem Research (LTAR) network production system and individual agroecosystems. Integrated LTAR regions reached high accuracy with fewer points, whereas cropland and grazingland regions required more extensive sampling. Several agroecosystems showed lower coefficients of determination between point-based and field mean NDVI, particularly during the growing and senescence seasons. Digital sampling reduced computational requirements, using far fewer Earth Engine Compute Units (EECUs) than full-field extraction. These findings demonstrate that stratified digital sampling offers an efficient, scalable approach for monitoring vegetation dynamics across U.S. agroecosystems, enabling broad-scale indicator development while reducing computational time, energy use, and analytical costs.