The refraction phenomenon causes ICESat-2 nearshore bathymetry errors by deviating seafloor photons’ coordinates. A refraction correction method based on ATL03 photon parameter tracking was proposed to improve the ICESat-2 bathymetry accuracy. The method begins by searching for sea–air intersections using photon parameters. Instead of relying on mathematical operations, it uses logical relations to establish a relationship between the seafloor and the surface, which improves efficiency. Then, a refraction correction model is designed based on Snell’s law for different sea surface fluctuations. This model is clear and suitable for scholars new to refraction correction. The results show the effectiveness of the proposed method since the RMSE is reduced by 1.8842 m~5.2319 m compared with the raw data. Our method has better tolerance than other methods at different water depth ranges.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), launched in September 2018, has been widely used in forestry and surveying. A high-accuracy digital elevation model (DEM)/digital surface model (DSM) for terrain matching can effectively evaluate the ICESat-2 design requirements and provide essential data support for further study. The conventional terrain-matching methods regard the laser ground track as a whole, ignoring the individual differences caused by the interaction of photons during flight. Therefore, a novel terrain-matching method using a two-dimensional affine transformation model was proposed to describe the deformation of laser tracks. The least-square optimizes the model parameters with the high-accuracy terrain data to obtain the best matching result. The results in McMurdo Dry Valley (MDV), Antarctica, and Zhengzhou (ZZ), China, demonstrate that the proposed method can verify geolocation accuracy and indicate that the average horizontal accuracy of ICESat-2 V5 data is about 3.86 m in MDV and 4.67 m in ZZ. It shows that ICESat-2 has good positioning accuracy, even in mountainous areas with complex terrain. Additionally, the random forest (RF) model was calculated to analyze the influence of four factors on geographic location accuracy. The slope and signal-to-noise ratio (SNR) are considered the crucial factors affecting the accuracy of ICESat-2 data.