Mapping Complex Cropping Patterns in China (2018–2021) at 10 m Resolution: A Data-Driven Framework based on Multi-Product Integration and Google Satellite Embedding

Abstract. Mapping complex cropping patterns and temporal dynamics is of great significance for addressing the challenges faced by agricultural systems. However, in China, annual nationwide maps depicting multiple crops and rotation sequences are still lacking. In this study, we developed a data-driven crop mapping framework by integrating multiple existing crop products with the Google Satellite Embeddings derived from the AlphaEarth foundation model, and produced 10-meter resolution mapping of complex cropping patterns across China from 2018 to 2021. Firstly, we integrated multiple publicly available crop mapping products within a harmonized framework that applies a unified cropland extent and cropping intensity, providing a systematic assessment of their consistency at pixel level. Consistency analysis results classify the study area into areas with consistency and areas with confusion, the latter serving as the mapping focus. Then, by combining harmonized crop data layers with random forest classifiers trained on foundation-derived embedding features, our framework effectively enhanced the spatial coherence and temporal stability, achieving an overall accuracy of 92.60 % and an F1 score of 0.7584. Compared with ADM-2 statistics, the mapped cropping areas achieved high consistency (R² = 0.849, RMSE = 4.61, MAE = 2.07). The resulting datasets provide an integrated depiction of China’s complex cropping systems, enabling consistent interannual assessments of changes in crop types, cropping sequences, and spatial patterns at 10 m resolution, thereby establishing a robust foundation for refined agricultural management and policy decisions, while supporting climate-smart and sustainable land-use planning.