High-resolution long-term mapping of major crops using satellite data

Winter wheat, maize, rice, and sugarcane are among the most important crops for global food security and bioenergy production. However, consistent high-resolution crop distribution maps across large regions and long time periods remain limited. In this study, we developed crop-specific identification algorithms that integrate spectral and phenological characteristics derived from satellite observations. Using these methods, we generated high-resolution (≤30 m) distribution maps for winter wheat, maize, rice, and sugarcane in China from 2001 to 2024. In addition, we produced sugarcane maps for Brazil (2016–2019), global winter cereal maps (2017–2022), and rice maps across Asia (1990–2023). Validation against independent samples shows that producer’s and user’s accuracies for winter wheat, maize, and rice in China reached 89.3% and 90.6%, 76.2% and 81.6%, and 88.4% and 89.1%, respectively. The global winter cereal maps achieved producer’s and user’s accuracies of 81.1% and 87.9%, while overall accuracies for sugarcane exceeded 91% in both China and Brazil. Estimated crop planting areas exhibit strong agreement with official statistics across regions. The resulting datasets provide consistent, long-term, and high-resolution crop distribution information, offering valuable support for crop monitoring, food security assessment, and climate and land-use change studies.