GLAD

High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this project, we improved a workflow for crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95% with standard errors of less than 1%. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91% and 84% across the years, and UAs and PAs for soybean were greater than 88% and 82%, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. Overall, the median percentages of mixed maize and soybean pixel reduction across all counties were 14% and 16%, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales.