CropSTS: A Remote Sensing Foundation Model for Cropland Classification with Decoupled Spatiotemporal Attention

Recent progress in geospatial foundation models (GFMs) has demonstrated strong generalization capabilities for remote sensing downstream tasks. However, existing GFMs still struggle with fine-grained cropland classification due to ambiguous field boundaries, insufficient and low-efficient temporal modeling, and limited cross-regional adaptability. In this paper, we propose CropSTS, a remote sensing foundation model designed with a decoupled temporal–spatial attention architecture, specifically tailored for the temporal dynamics of cropland remote sensing data. To efficiently pre-train the model under limited labeled data, we employ a hybrid framework combining joint-embedding predictive architecture with knowledge distillation from web-scale foundation models. Despite being trained on a small dataset and using a compact model, CropSTS achieves state-of-the-art performance on the PASTIS-R benchmark in terms of mIoU and F1-score. Our results validate that structural optimization for temporal encoding and cross-modal knowledge transfer constitute effective strategies for advancing GFM design in agricultural remote sensing.