Extracting shallow water depth from the fusion of multi-temporal ICESat-2 data and multi-spectral imageries

Abstract

With the development of satellite remote sensing and laser altimetry data, the fusion of laser altimetry data and multi-spectral images for shallow water depth inversion has become an economically convenient way. However, there are few methods that take into account the temporal dimension of data to integrate the results of water depth inversion in multiple temporal phases. In response to this issue, this article proposed a shallow water depth inversion method that integrates multi-source and multi-temporal remote sensing data. This method utilized the random forest (RF) algorithm to estimate the water depth values at different time, taking into account the contextual information of Sentinel-2 imagery and using the overall least squares as the theoretical model to fuse the multi-temporal water depth inversion results. This article took the Yongle Islands in the South China Sea as the research area and conducted the shallow water inversion experiments using ICESat-2 (Ice, Cloud and Land Elevation Satellite 2) data from 5 temporal phases and one Sentinel-2 imagery. The results show that the mean value of RMSE (root mean square error) and R2 (determination coefficient) of the proposed method using single temporal imagery was 1.53m and 0.7610, which outperformed the inversion accuracy of traditional methods that ignore image context information. The RMSE and R2 of the multi-temporal fusion model was 1.15m and 0.8622, which was 0.08m and 0.0199m higher than existing median filtering fusion method.