Multi-spectral remote sensing bathymetry based on the variation of substrate spectra and empirical linear relationship

Abstract

The high spatial resolution and wide applicability of multi-spectral remote sensing make it significant for estimating shallow water depth. In areas with mixed substrates, determining the contribution of substrate reflection to the sensor’s received signal can effectively enhance the precision of multi-spectral remote sensing bathymetry. Therefore, this paper proposes a Two Factors constrained Quasi-Analytical Algorithm (TF-QAA). Firstly, the algorithm considers the spectral variation of the mixed substrates. Secondly, a robust correlation between inherent optical parameters and water depth is established in this study, which is used to enforce a linear relationship constraint on the initially estimated water depth. Sentinel-2 image data was used to verify the algorithm in the Antelope Reef area of the South China Sea. The best result from images taken at different times indicates that the TF-QAA method has the average absolute error (MAE) of 0.92 m, the root mean square error (RMSE) of 1.20 m, the average relative error (MRE) of 10.6%, and the correlation coefficient (R2) of 0.97. The results suggest that the algorithm is effective for Case 1 water, providing a novel approach for extensive bathymetric mapping in shallow water areas of islands, with or without LiDAR-measured depth data.