SAF-CropNet: Spatially adaptive fusion of SAR and optical imagery for semantic segmentation in operational cropland mapping across regions

Accurate cropland mapping from remote sensing imagery is essential for agricultural monitoring, food security assessment, and land resource management. Although optical and synthetic aperture radar (SAR) observations provide complementary information for cropland extraction, their local contributions vary across imaging conditions, land-cover backgrounds, parcel morphologies, and regional agricultural systems. This variability limits direct concatenation and static fusion, as modality-specific errors may propagate into segmentation outputs in the absence of spatially adaptive weighting. This study proposes SAF-CropNet, a spatially adaptive SAR–optical fusion framework for binary cropland semantic segmentation. Specifically, SAF-CropNet first extracts modality-specific SAR structural features and optical spectral-textural features through separate encoder branches. The Paired Reciprocal Inter-modal Selective Modulator enhances complementary SAR–optical interactions, while the Contextual State-space Modeling module captures long-range parcel organization. A spatially adaptive fusion head then estimates per-pixel contribution weights for SAR, optical, and contextual features before decoding. Evaluation was conducted on a seven-area SAR–optical benchmark spanning China, Germany, and France. The benchmark integrates GF1/GF3 and Sentinel-1/2 imagery with reference labels derived from field surveys and RapidCrops products and covers fragmented smallholder systems, water-rich and peri-urban mosaics, and large mechanized cropland. Within-region five-fold validation shows that SAF-CropNet achieves an average F1 score of 0.8550 and an mIoU of 0.8068, outperforming the evaluated segmentation baselines while remaining lightweight, with 3.38 M parameters and 6.21 GFLOPs for 256 × 256 SAR–optical inputs. In leave-one-area-out transfer experiments, stratified few-shot adaptation increased the average F1 score from 0.4986 under zero-shot transfer to 0.7698. Grouped cross-continental and cross-sensor experiments further showed that direct transfer remains constrained under compound shifts in sensor characteristics, spatial resolution, landscape structure, and reference-label conventions. These findings indicate that SAF-CropNet combines strong within-region segmentation accuracy and computational efficiency with substantial gains from limited target-domain adaptation, while direct zero-shot generalization under severe compound domain shifts remains limited.