Reconstructing inland lake bathymetry in optically complex waters by fusing ICESat-2 altimetry and Sentinel-2 Imagery: A case study of Lake Balkhash

Study region Lake Balkhash, a large endorheic lake in southeastern Kazakhstan, Central Asia, with strong spatial variation in water clarity. Study focus Accurate lake water storage estimation in arid regions requires reliable bathymetry, but large optically complex lakes remain difficult to map because of limited in situ data and rapid signal attenuation. This study develops a bathymetric retrieval framework for Lake Balkhash by integrating ICESat-2 photon-counting LiDAR and multi-temporal Sentinel-2 imagery. An adaptive DBSCAN-based algorithm was used to extract lakebed photons from noisy photon clouds, and the resulting depth samples were combined with Sentinel-2 spectral features in a Random Forest model to produce a continuous high-resolution bathymetric map. New hydrological insight for the region The proposed fusion framework performed well in reconstructing lakebed topography under varying water-clarity conditions, achieving an RMSE of 1.046 m against independent in situ validation data. The reconstructed bathymetry reveals clear morphological differences between the two sub-basins, suggesting that the shallow western sub-basin is more sensitive to hydrological fluctuations than the deeper saline eastern sub-basin. By establishing level–area–volume relationships, we reconstructed a long-term record of lake water storage changes. This record indicates a renewed decline in water storage during 2019–2024. These findings demonstrate the limitations of traditional area-based monitoring and provide a volumetric baseline for water resource assessment in Central Asia.