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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched on September 15, 2018. It is the first photon-counting laser altimeter satellite, which is of great significance for the research into laser altimetry. ICESat-2 is, however, highly sensitive and susceptible to environmental influences. In addition to surface returns, a lot of nonsurface photons are found in the data. It is, therefore, necessary to study an effective method to separate the surface signal from background information. In this article, we review the existing surface detection methods for photon point cloud data and select seven methods for comparison. Four sources of photon-counting data were considered in the experiments: The Multiple Altimeter Beam Experimental Lidar (MABEL), the Chinese Multibeam LiDAR, The Advanced Topographic Laser Altimeter System (ICESat-2&#x002F;ATLAS), and MATLAS (using MABEL data to simulate the expected ATLAS photon point cloud). Four scenarios of land, land ice, sea ice, and ocean were also considered. Each surface detection method was tested in 12 experiments, and the different methods were finally compared by qualitative and quantitative measures. We were, thus, able to establish the advantages and disadvantages of each method, which will be of great significance for scholars studying surface detection methods.

A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data