A Spatial-Temporal Attention-Based U-Net for Crop Mapping from Time-Series Sentinel-2 Imagery: A Case in Sanjiang Plain

Food security is a growing global concern, and accurate crop mapping in major grain-producing regions like China’s Sanjiang Plain—which contributes approximately 7% of national grain output—is essential for agricultural resource management. However, crop classification in this area is hindered by frequent cloud cover, complex phenological rhythms, and spatial heterogeneity. To address these challenges, this study proposes Spatial-Temporal Attention U-Net (STA-UNet), a crop classification model based on time-series Sentinel-2 imagery, incorporating four key modules: Convolutional Block Attention for enhanced sensitivity to parcel boundaries, Temporal Attention Encoder for adaptive capture of temporal dependencies under cloud interference, Dynamic Upsampling for improved boundary recovery of small parcels, and Adaptive Feature Fusion for bridging semantic gaps between heterogeneous features. Extensive experiments on rice, maize, and soybean classification demonstrate that STA-UNet achieves an overall accuracy of 93.61% and an F1-score of 0.925, outperforming state-of-the-art methods. In spatial generalization tests, STA-UNet maintains overall accuracy above 85.02% in the left-subregion transfer setting and achieves the best three-year average OA of 81.34% in the rice-dominated right-subregion stress test, while temporal generalization tests confirm limited inter-annual performance degradation. These results indicate that STA-UNet provides a robust and effective framework for crop mapping in cloud-prone, phenologically complex agricultural regions.