A Multi-Modal Data Fusion Framework for Mapping the 1990–2020 Evolution of China's Major Cash Cropping Systems at 0.1° Resolution

Accurate mapping of spatio-temporal dynamics of various cash crops is crucial for understanding their socioeconomic and ecological implications. Here, we develop an innovative gridded cropping system model that fuses multi-modal data to investigate the evolving patterns of six major cash crops (cotton, groundnuts, rapeseed, sugar beet, sugarcane and vegetables) in China during 1990-2020 at a 0.1°×0.1° resolution. This high-resolution dataset provides a foundation for assessing the impacts of cash crop cultivation on soil, water, and greenhouse gas emissions, informing crop management and agricultural policy decisions. Moreover, our model is transferable to other regions and the globe, allowing fully leveraging global multi-modal environmental data to evaluate broader sustainability implications.