Abstract. Arctic supraglacial lakes volume changes serve as critical indicators of global temperature fluctuations. Accurate lake depth measurements are essential for reliable volume estimation, yet traditional bathymetry methods (e.g., airborne LiDAR and shipborne sonar) face significant challenges and high costs in the harsh Arctic environment, and are also inadequate for capturing the rapid temporal variability in lake depth. To address this, we utilized satellite-derived bathymetry (SDB) to achieve supraglacial lake water depth inversion. Specifically, three approaches were tested: (i) applying the radiative transfer equation (RTE) model (Philpot RTE model) using only Sentinel-2 multispectral optical imagery; (ii) combining ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) single photon-counting laser altimetry data with Sentinel-2 imagery through a semi-empirical log-transformed linear regression model (Lyzenga model); and (iii) a proposed novel approach that optimizes the model by considering the varying reflectance characteristics across different spectral bands in the water column. For the proposed method, we propose an SDB approach based on spectral stratification using the Otsu algorithm (maximum between-class variance method), and further integrate the spectral stratification with the Lyzenga model. To validate the effectiveness of the proposed method, we applied it to four relatively large and morphologically intact lakes on the southwest Greenland Ice Sheet (GrIS), using time-stamped ArcticDEM (Arctic Digital Elevation Model) strips as reference data. Compared with the Philpot RTE model, the root mean square error (RMSE) and mean absolute error (MAE) were reduced by up to 86.6 % and 89.0 %, respectively; compared with the traditional Lyzenga model, the RMSE and MAE decreased by up to 13.0 % and 14.0 %, respectively. The enhanced accuracy of our approach improves the ability to monitor volume changes in GrIS supraglacial lakes, providing valuable insights into their response to environmental changes.
Lv et al. (Thu,) studied this question.