Arctic supraglacial lake derived bathymetry combining ICESat-2 and spectral stratification of satellite imagery

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. This study introduces a novel approach using ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) and Sentinel-2 data to derive supraglacial lake bathymetry. By considering the varying reflectance characteristics across different spectral bands in the water column, we conduct a satellite-derived bathymetry (SDB) method based on spectral stratification using the Otsu algorithm (maximum between-class variance method). Integrating the spectral stratification method with the classical log-transformed linear regression model (Lyzenga model), we perform accurate bathymetric inversion on multispectral satellite imagery. To verify the effectiveness of the proposed method, we apply it to four representative lakes on the Greenland Ice Sheet (GrIS), using ArcticDEM (Arctic Digital Elevation Model) as reference data. Experimental results demonstrate improved accuracy compared to the classical Lyzenga model, with reductions in root mean square error (RMSE) and mean absolute error (MAE) by up to 13.0 % and 14.0 %, decreasing from 0.54 m to 0.47 m and from 0.43 m to 0.37 m, respectively. The enhanced accuracy and scalability of our approach improve the ability to monitor large-scale volume changes in Arctic supraglacial lakes, providing valuable insights into their response to climate change.