An accurate 10 m annual crop map product of maize and soybean across the United States

作者：Haijun Li, Xiao‐Peng Song, Bernard Adusei, Jeffrey Pickering, Andre Lima, Andrew Poulson, Antoine Baggett, Peter Potapov, Ahmad Khan, Viviana Zalles, Andres Hernandez-Serna, Samuel M. Jantz, Amy Pickens, Carolina Ortiz-Dominguez, Xinyuan Li, Theodore Kerr, Zhen Song, Svetlana Turubanova, Eddy Bongwele, Héritier Koy Kondjo, Anna Komarova, Stephen V. Stehman, Matthew C. Hansen · 发表于：Earth system science data · 年份：2026 · DOI：10.5194/essd-18-2227-2026 · 被引用次数：2 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Soil Geostatistics and Mapping

Abstract. 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 study, we improved a workflow for crop mapping and developed an openly available, annual, 10 m spatial resolution maize and soybean map product over the Contiguous United States (CONUS) from 2019 to 2022 (available at https://glad.umd.edu/dataset/mapping-crops-10-m-resolution-united-states, last access: 26 December 2025). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10 d analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10 d ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. 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 hi...