CAFF-Net for coupled eco-health prediction of urban green space and water body with multi-source remote sensing

Aiming at three bottlenecks of multi-source remote sensing eco-health inversion: cross-modal feature redundancy, decoupled single-task modeling, and detail distortion of fragmented urban land covers, this paper proposes CAFF-Net for coupled eco-health prediction of urban green spaces and water bodies using Sentinel-1 SAR and Sentinel-2 optical data. A dual-branch Swin-Transformer separately extracts heterogeneous features, and serial CBAM realizes adaptive feature filtering to suppress redundant information. An improved multi-task UNet synchronously predicts green space, water body and coupled ecological health indices with joint loss to model aquatic-terrestrial ecological linkages. Experiments show CAFF-Net achieves an 𝑅 2 of 0.928, 3.3% higher than SOTA, with strong robustness under heavy cloud interference, supporting sponge city and watershed governance.