A Bathymetry Mapping Approach Combining Log-Ratio and Semianalytical Models Using Four-Band Multispectral Imagery Without Ground Data

Four-band multispectral remote sensing imagery is widely used for a variety of purposes and has a long historical data record. However, most of the existing bathymetry inversion methods are either unable to determine the optimal solution through theoretical or semianalytical models due to the limited number of bands, or their application is limited by the difficulty in obtaining in situ data. In this article, a log-ratio model and a semianalytical model are combined to develop a new shallow water depth inversion method (L-S model) using four-band multispectral remote sensing images without the need for supporting truth data. A case study was conducted for Ganquan Island in the South China Sea, using four-band multispectral imagery from the GeoEye-1, WorldView-2 (four bands selected), Sentinel-2 (four bands selected), and Gaofen-1 satellites; a sound range of 30 m was achieved. When compared to LiDAR-measured water depth data, GeoEye-1, WorldView-2, Sentinel-2, and Gaofen-1 data have root-mean-square errors (RMSEs) of 1.33-1.97 m. In addition, compared with the results of the log-ratio model trained using 200 LiDAR-based depth readings, the L-S model results obtained for the four satellite types are similar in the RMSE. These results show that the L-S model can achieve results that are close to those of the log-ratio model without the need for external inputs. This provides a feasible new method for bathymetry inversion in areas without truth data using only four-band imagery.