Cross-City Evaluation of Multi-Sensor SAR–Optical Fusion Strategies for Agricultural Land Cover Classification Using Deep Learning

Accurate and transferable land cover mapping from satellite imagery is a prerequisite for national-scale agrienvironmental monitoring and climate change impact assessment. The joint use of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery offers complementary structural and spectral information, yet systematic evaluation of fusion strategies and their geographic transferability remains limited. We trained and tested five U-Net fusion architectures—S1-only, S2-only, early (input-level), feature-level (middle), and decision-level (late)—alongside a SegFormer-b2 transformer baseline over two German cities (Munich and Berlin) using the Multi-Sensor Land Cover Classification (MSLCC) dataset (single-date 2017 Sentinel-1B/Sentinel-2A acquisitions) at 10 m resolution. Three cross-city transfer protocols (Munich → Berlin, Berlin → Munich, and combined training) quantify model transferability across contrasting urban–rural gradients. Early fusion achieved the highest in-city macro-averaged F1 score among U-Net variants (0.8278), a small but statistically significant improvement over the optical-only baseline (0.8236; patch-level paired bootstrap, p=0.006); feature-level (middle, 0.8164) and decision-level (late, 0.8196) fusion were, by contrast, significantly worse than the optical-only baseline (p<0.001 and p=0.030, respectively), and the SAR-only model (0.6864) trailed substantially. The built-up class was the primary beneficiary of SAR inclusion under early fusion. SegFormer-b2 (0.8214) was numerically close to, but statistically significantly below, the best convolutional configuration (p=0.005), and exhibited strong cross-city transfer (0.8632–0.8608 macro-F1), consistent with the geographic invariance conferred by its ImageNet-pretrained encoder. Combined training across both cities improved over the single-direction transfer average by 0.012 macro-F1 points for U-Net and 0.006 points for SegFormer, offering a practical route to national-scale deployment without requiring explicit domain adaptation. Spectral index augmentation (NDVI, NDWI, ExG) and SE channel attention did not significantly improve over plain early fusion when derived from percentile-normalized inputs, with the best variant statistically indistinguishable from the baseline at macro-F1 = 0.8268 (p=0.365); the result is attributable to a specific preprocessing dependency: NDVI, NDWI, and ExG are only physically meaningful when computed from calibrated reflectance, whereas here they were derived after scene-level 2nd–98th percentile stretching, which strips the absolute radiometric referencing the indices rely on; practitioners combining spectral indices with percentile-normalized (rather than physically calibrated, e.g., Level-2A surface-reflectance) inputs should expect a similar null result.