Optimal approach to utilize multiple-pass ICESat-2 ATL03 data for satellite-derived empirical bathymetry

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) that carries an Advanced Topographic Laser Altimeter System (ATLAS) is a highly successful earth observing system. Utilizing ICESat-2′s data product for satellite-derived empirical bathymetry helps the latter to be totally independent of ground data and really based on satellites. Normally, ICESat-2′s ATLAS instrument can provide multiple-pass global geolocated data (i.e., ATL03), which are typically collected from the satellite’s multiple passes along the orbit, for a bathymetry area. How this type of data is efficiently used for empirical bathymetry is a tricky and unsolved problem. This article aims to find a solution to an optimal or near-optimal use of such multiple-pass ICESat-2 ATL03 data for satellite-derived empirical bathymetry, by observing and analyzing the bathymetric performance of their various possible combinations. The focus is to solve a problem of how model calibration data, whose depths come from multiple-pass ICESat-2 ATL03 data and whose logarithmic blue/green band ratios come from satellite multispectral images, are refined to remove their irrational components and retain their useful information. Related experiments were conducted in Saipan and Qilianyu study areas, with WorldView-2, Sentinel-2 and Landsat 8 multispectral images and multiple-pass ICESat-2 ATL03 data. The experiments showed that using any individual pass of multiple-pass ICESat-2 ATL03 data can hardly achieve a desired accuracy in the entire bathymetry area, and that using entire multiple-pass ICESat-2 ATL03 data cannot either necessarily provide an optimal bathymetric result. An optimal way of utilizing multiple-pass ICESat-2 ATL03 data for empirical bathymetry is that the entire multiple-pass ICESat-2 ATL03 data are used to form model calibration data first, and then the resulting calibration data are cleaned with the proposed Isolation Forest-based data refinement method in this article, and last the cleaned calibration data are used for bathymetric model training. The proposed data refinement method is highly effective for model calibration data cleaning, especially for removing those illogical data points close to main skeletons of the data. Applying this refinement method to empirical bathymetry enables the latter to estimate more robust bathymetry from satellite images even in turbid shallow water areas.