A scalable satellite-based crop yield mapper

New advances in satellite data acquisition and processing offer promise for monitoring agricultural lands globally. Using these data to estimate crop yields for individual fields would benefit both crop management and scientific research, especially for areas where reliable ground-based estimates are not currently made. Here we introduce a generalized approach for mapping crop yields with satellite data and test its predictions for yields across more than 17,000 maize fields and 11,000 soybean fields spanning multiple states and years in the Midwestern United States. The method, termed SCYM (a scalable satellite-based crop yield mapper), uses crop model simulations to train statistical models for different combinations of possible image acquisition dates, and these are then applied to Landsat and gridded weather data within the Google Earth Engine platform, where the Landsat is composited to find the “best” dates of observations on a pixel-by-pixel basis. SCYM estimates successfully captured a significant fraction of maize yield variation in all state-years, with a range of 14–58% and an average of 35% for this particular study region and crop. Similar results were observed for soybean, with an average of 32% of yield variation captured. The multi-year yield estimates were also used to examine the temporal persistence of yield advantages for the top yielding fields in different counties, which is one measure of how important factors such as farmer skill are in explaining yield gaps. The strength of the SCYM approach lies in its ability to leverage physiological knowledge embedded in crop models to interpret satellite observations in a scalable way, as it can be readily applied to new crops, regions, and types and timing of remote sensing observations without the need for ground calibration.