From Bands to Depth: Understanding Bathymetry Decisions on Sentinel-2

Deploying Sentinel-2 satellite-derived bathymetry (SDB)robustly across sites remains challenging. We analyze aSwin-Transformer U-Net (Swin-BathyUNet) to understandhow it infers depth and when its predictions are trustwor-thy. A leave-one-band-out study ranks spectral importanceto the different bands consistent with shallow-water op-tics. We adapt ablation-based CAM to regression (A-CAM-R) and validate faithfulness via a performance–retentiontest: keeping only the top-p% salient pixels while neutral-izing the rest causes large, monotonic RMSE increases,indicating explanations localize causal evidence. Atten-tion ablations show decoder-conditioned cross-attention onskips is the most cost-effective upgrade, improving robust-ness to glint/foam. Cross-region inference (train one site,test another) reveals depth-dependent degradation: MAErises nearly linearly with depth, and bimodal depth dis-tributions exacerbate mid/deep errors. Practical guidancefollows: maintain wide receptive fields, preserve radiomet-ric fidelity in green/blue channels, pre-filter bright high-variance near shore, and pair light target-site fine-tuningwith depth-aware calibration to transfer across regions.