Water depth estimation models based on optical satellite images often require ground-truth data for supervised training procedures. However, in the South China Sea (SCS), the ground truth data are limited or outdated. Therefore, it is challenging to derive reasonable water depths around islands or coral reefs without prior knowledge. ICESat-2 is a space-borne LiDAR satellite launched in September 2018 that provides geolocated height at the laser footprint on a global scale and opens an opportunity for bathymetric mapping in regions normally inaccessible. The combination of ICESat-2 data with Sentinel-2 optical images is developed to extend the application of satellite-derived bathymetry (SDB). Three SDB algorithms, including ratio transform (RT), multiple linear regression, and classification-based (CB) algorithms, are applied to investigate the water-depth retrieval capabilities. Five islands located in different parts of the SCS are selected, analyzed, and evaluated with the support of Google Earth Engine. Comparing the ICESat-2 water depth profiles against airborne LiDAR data, the statistical indexes of R² and RMSE reached 0.99 and 0.31 m, respectively. This demonstrates the suitability of using ICESat-2 data as a reliable data source in shallow water. On the other hand, the CB model is used to address the issue of heterogeneity by dividing the target islands into groups based on spectral characteristics. The results show that an integration of ICESat-2 and Sentinel-2 imageries can achieve R² at 0.75 to 0.95 and RMSE at 0.66 to 1.87 m with the deepest pixels at 19 to 32 m across these five islands. We conclude that the ICESat-2/Sentinel-2 synergy scheme is capable of overcoming current limitations in various regions and thus can fill the gaps in bathymetry charts.