Adaptive depth piecewise Algorithm Based on sediment classification for shallow water bathymetry by ICESat-2 and Sentinel-2

Abstract:
Satellite derived bathymetry as a supplementary means of sonar measurement, plays an important role in the disputed islands and reefs. With the permeability of 532nm laser, ICESat-2 can provide a certain number of water depth control points for passive optical remote sensing satellite images, which can fill in the gap of shallow water depth data in disputed areas. However, due to the hardware conditions of ICESat-2, the major limitation for bathymetry is that ICESat-2 height profiles are inhomogeneously distributed in horizontal and vertical directions. Considering the influence of different sediments and depth ranges on the empirical model, an adaptive depth piecewise Algorithm Based on sediment classification (APBSC) is developed to estimate shallow water bathymetry. In this paper, APBSC and Log_ratio model are used to fit the spectral data with ICESat-2 height profiles. After water depth mapping using the trained model, independent ICESat- 2 point clouds from Quanfu Island and sonar measured depth from Ganquan Island are used to validate the Sentinel -2 derived bathymetry. The results show that the overall root means square errors (RMSE) for APBSC are 1.34m for Ganquan Island and 0.56m for Quanfu Island respectively. Compared with Log_ratio model, RMSE in Ganquan Island and Quanfu Island decreased by 0.08m and 0.1m respectively.