Search

It is essential to study how to obtain precise bathymetric and mapping in inland lakes only from satellite data. In this study, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon point cloud was first denoised by combining the manual interaction (MI) and density-based spatial clustering of applications with noise (DBSCAN) algorithm in QiXiang Co (QXC) and Caiduochaka (CK) on the Tibetan Plateau (TP). Second, we proposed to fit the complete profile depth of the along-track profile and the constructed free profile by using a mathematical function. Finally, the random forest (RF) and linear regression (LR) models were used to obtain bathymetric maps, and bathymetric performance and the benefits of free profile for improving accuracy were verified with the measured depth. The results indicated that the combination of the MI and DBSCAN algorithm could obtain signal photons, and the mean absolute error (MAE) between the fitted free profile water depth and the measured profile water depth was less than 0.90 m. Compared with the original tracks (gt1l, gt2l, and gt3l) in QXC (0–28 m), the MAE of the RF and LR models decreased by 0.35 m and 0.46 m for the validation dataset after adding the free profile. Similarly, the MAE of the RF and LR models decreased by 0.11–0.33 m and 0.13–0.15 m in CK, respectively. This study suggested that this method could be used to obtain the bathymetric map and improve accuracy by fitting profile depth based on ICESat-2 and Sentinel-2 data in shallow water.