ICESAT-2 Shallow Bathymetric Mapping Based on a Size and Direction Adaptive Filtering Algorithm

The US Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) satellite adopts 532 nm single-photon lidar with shallow sea bathymetry capability. In order to realize high-precision and automated shallow sea bathymetric mapping based on ICESat-2 photon data, an adaptive underwater point denoising algorithm that considers the search direction and search size is proposed in this article, and a detailed data processing process is discussed to further verify the technical feasibility. The accuracy of direct bathymetry and active–passive fusion bathymetry from ICESat-2 is systematically analyzed using airborne in situ data. First, the underwater photon points are separated by surface position identification; then, the signal point cloud search strategy is improved, the search size increases with water depth, the search angle is rotated and the direction of the maximum number of point clouds is taken as the main direction, and the threshold is automatically determined by histogram Gaussian fitting of the point cloud density to achieve automatic underwater signal extraction; then, the refraction correction is carried out based on the light geometry and the water depth is obtained; finally, the active–passive fusion bathymetry is performed by combining the optical remote sensing images WorldView-2 and Sentinel-2, and the accuracy is verified by using the airborne lidar bathymetric data provided by NOAA. The experimental results show that the proposed denoising algorithm can accurately discriminate the underwater signal/noise, and the overall accuracy is better than 86%; the root-mean-square error (RMSE) of ICESat-2 direct bathymetry is between 0.42 and 0.98 m; the RMSE of active–passive fusion bathymetry is between 0.84 and 1.88 m. Our workflow and experimental results demonstrate a means of using ICESat-2 to produce relatively accurate bathymetric maps in shallow, clear water environments.