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Semantic segmentation of cropland is essential for extracting crop distribution from satellite remote sensing (RS) images. However, the dynamic temporal patterns caused by crop rotations and the heterogeneous spatial characteristics of cropland hinder high-precision segmentation, leading to boundary ambiguity, intra-class variability across crop growth stages and occlusion from clouds or shadows. To address these challenges, we propose STFE, a spatiotemporal feature-enhanced network for cropland segmentation in remote sensing time-series images. STFE integrates spatial and temporal features through three key designs. First, an edge-guided spatial attention (EGSA) module enhances boundary representation, improving delineation of irregular parcels. Second, a progressive feature enhancement (PFE) strategy progressively fuses multi-scale features, strengthening spatial representation. Third, for temporal feature extraction, we incorporate a differential awareness attention (DAA) module, built on ConvLSTM, dynamically aggregates temporal information, enabling robust modeling of crop rotations and seasonal variations. Experimental on three benchmark datasets – PASTIS, ZueriCrop, and DNETHOR – confirm the effectiveness of STFE, achieving a mean IoU gain of 3.2% over the best baseline. By effectively leveraging spatiotemporal cues, STFE provides a reliable and scalable solution for monitoring cropland dynamics, supporting sustainable agriculture and informed decision-making.
Chang et al. (Mon,) studied this question.