Satellite-derived bathymetry is an important method for obtaining shallow sea water depth. To address the problem that the traditional band log-ratio model exhibits significant discrepancies in bathymetric inversion accuracy in both shallow sea (12 m), where water depth is overestimated in shallow areas and underestimated in deep areas, this paper proposes a bathymetric inversion method from active–passive satellite remote sensing data based on a residual correction model. First, the proposed method uses the denoised Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL03 data set as precise depth control points to construct and implement the traditional band log-ratio model, generating initial bathymetric inversion results. Second, the initial inversion result is compared with the ICESat-2 depth control points, and the residuals between the initial water depth estimates and the true water depths in the local region are calculated. Then, based on the relationship between the spectral characteristics of remote sensing images and water depth residuals, a random forest model is constructed to fit the residual distribution across the entire study area. Finally, using the residual distribution derived from this fitting process, the initial bathymetric inversion results for the entire region are corrected, thereby improving the accuracy and precision of the water depth data. Experimental results demonstrate that the proposed method achieves a bathymetric inversion accuracy with the root mean square error better than 1.57 m and the mean absolute error better than 1.15 m for islands with varying seabed topographies, providing high-precision shallow sea bathymetric inversion results.
Dong et al. (Thu,) studied this question.