A Multi-Temporal Satellite-Derived Bathymetry Fusion Method Based on Adaptive Segmented Rank-Statistic Fusion

Satellite-derived bathymetry (SDB) provides an efficient approach for shallow-water mapping because of its wide spatial coverage and repeated observation capability. However, multi-temporal bathymetric results derived from optical imagery often exhibit substantial inconsistencies due to variations in atmospheric conditions, water optical properties, bottom reflectance, and imaging geometry. Moreover, different bathymetric intervals usually exhibit distinct uncertainty characteristics, while conventional global fusion methods generally apply a single statistical strategy to the entire depth range. To address this limitation, this study proposes an ICESat-2-constrained adaptive segment-wise rank-statistic fusion framework for multi-temporal SDB. The bathymetric range is adaptively divided into multiple depth intervals using ICESat-2 bathymetric control points, and the optimal rank-statistic fusion strategy is independently selected for each interval according to local RMSE evaluation. In this way, shallow-water outliers can be effectively suppressed, while deep-water systematic underestimation can be alleviated simultaneously. Experiments conducted in Ganquan Island, Dong Island, and Key Biscayne demonstrate that the proposed framework consistently outperforms individual single-scene results as well as conventional mean and median fusion methods. Compared with conventional mean and median fusion methods, the RMSE was reduced by up to 27.5%, while the coefficient of determination (R2) reached 0.95. Significant improvements were particularly observed in deeper bathymetric intervals and complex benthic environments. The results indicate that adaptive segmented rank-statistic fusion can effectively characterize bathymetric-dependent error variations and achieve unified optimization for shallow-water outlier suppression and deep-water bias correction.