Machine Learning Calibration of Sentinel-3 Significant Wave Height Using Level-2 Features Only: A Two-Stage Approach Validated at 83 NDBC Buoys

Satellite radar altimetry provides the only global, all-weather record of significant wave height (SWH), but Sentinel-3 Level-2 retrievals retain systematic, context-dependent errors near coasts and at low sea states. We present a two-stage calibration that uses only Level-2 variables: a buoy-weighted isotonic regression removes the scale-dependent bias, and a LightGBM model predicts the residual from waveform-quality and along-track descriptors, with quantile models supplying a per-sample uncertainty. The method was evaluated on 10,234 Sentinel-3A/B overpasses collocated with 83 National Data Buoy Center (NDBC) buoys (2019–2024) under leave-one-buoy-out validation. The root-mean-square error with respect to the buoys fell from 0.266 m to 0.211 m (−21%; station-bootstrap 95% confidence interval of the reduction 0.038–0.073 m) and the bias from −0.113 m to +0.006 m. The gain was unchanged without buoy-dependent descriptors and persisted with nested hyper-parameter selection and in held-out years (2023–2024) but was uneven across regions. It was largest within 20 km of the coast and below 1.5 m. High sea states defined by the buoy value showed no RMSE reduction, a pattern consistent with regression dilution and buoy noise. After conformal rescaling, the 80% and 90% intervals reached nominal coverage on average at stations not used for fitting.