Monitoring lake water levels is important to fully understand the characteristics and mechanism of lake dynamic change, the impact of climate change and human activities on lakes, etc. This paper first individually evaluated the performance of the newly released Global Ecosystem Dynamics Investigation (GEDI) and the successor of the Ice, Cloud, and Land Elevation Satellite mission (ICESat-2) for inland lake level retrieval over four typical lakes (Chaohu Lake, Hongze Lake, Gaoyou Lake and Taihu Lake) using in situ gauge data, then the lake levels of the two missions were combined to derive long time-series lake water levels. A comparison of the mission results with in situ water levels validated the accuracy of the ICESat-2 with R varying from 0.957 to 0.995, MAE 0.03 m-0.10 m and RMSE 0.04 m-0.13 m; however, larger bias occurred in GEDI results with R spanning from 0.560 to 0.952, MAE 0.31 m-0.38 m and RMSE 0.35 m-0.46 m. Before the lake levels were combined, GEDI bias correction was carried out. The correlation coefficients and annual change rate differences between the combined and the in situ data were 0.964 and 0.06 m/yr, 0.852 and 0.05 m/yr, 0.888 and 0.05 m/yr, and 0.899 and 0.02 m/yr for Lake Chaohu, Hongze, Gaoyou and Taihu, respectively. Except for individual months and seasonal differences caused by GEDI estimations, the general trend of monthly, seasonal, and annual dynamics of inland lake water levels captured by combined GEDI and ICESat-2 missions were consistent with measurements from hydrological stations. These encouraging results demonstrate that combining the two missions has great potential for frequent and accurate lake level monitoring and could be a valuable resource for the study of hydrological and climatic change.

Accurate shallow water bathymetry data are essential for coastal construction and management, marine traffic, and shipping. With the development of remote sensing satellites and sensors, the satellite-derived bathymetry (SDB) method has been widely used for bathymetry in shallow water areas. However, traditional satellite bathymetry requires in-situ bathymetric data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with the advanced high-resolution topographic laser altimeter system (ATLAS) provides a new technical tool and makes up for the shortcomings of traditional bathymetric methods in shallow waters. In this study, a new method is proposed to automatically detect photons reflected from the shallow seafloor with ICESat-2 altimetry data. Two satellite bathymetry models were trained, to obtain shallow water depth from Sentinel-2 satellite images. First, sea surface and seafloor signal photons from ICESat-2 were detected in the Oahu (in the U.S. Hawaiian Islands) and St. Thomas (in the U.S. Virgin Islands) sampling areas, to obtain water depths along the surface track. The results show that the RMSE is between 0.35 and 0.71 m and the R2 is greater than 0.92, when compared to the airborne LiDAR bathymetry (ALB) data in the field. Second, the ICESat-2 bathymetric points from Oahu Island are used to train the Back Propagation (BP) neural network model and obtain the SDB. The RMSE is between 0.97 and 1.43 m and the R2 is between 0.90 and 0.96, which are better than the multi-band ratio model with RMSE of 1.03–1.57 m and R2 of 0.89–0.95. The results show that the BP neural network model can effectively improve bathymetric accuracy, when compared to the traditional multi-band ratio model. This approach can obtain shallow water bathymetry more easily, without the in-situ bathymetric data. Therefore, it extends to a greater extent with the free ICESat-2 and Sentinel-2 satellite data for bathymetry in shallow water areas, such as coastal, island and inland water bodies.

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