Supraglacial Lake Depth Retrieval from ICESat-2 and Multispectral Imagery Datasets

Supraglacial lakes play an important role in the surface mass balance of ice sheets. With global warming, supraglacial lakes may become more extensive on ice sheet surfaces than they currently are. Therefore, accurate estimation of the volume of supraglacial lakes is important for characterizing their impact on ice sheets. In this study, we present a machine learning-based method for estimating the depth of supraglacial lakes through the combination of ICESat-2 ATL03 data with multispectral imagery. We tested this method via Landsat-8 and Sentinel-2 imagery and evaluated the accuracy of the algorithm on 7 test lakes on the Greenland Ice Sheet. Our results show that machine learning-based algorithms achieve better accuracy than traditional regression or physics-based methods do, especially for deeper lakes. The best accuracy was achieved when extreme gradient boosting was applied to a Sentinel-2 L1C image, with root mean square error, mean absolute error, and median absolute error values of 0.54 m, 0.43 m, and 0.36 m, respectively. Furthermore, we evaluated the effects of atmospheric corrections of multispectral imagery in the retrieval of supraglacial lake depth. On the basis of our results, we recommend the direct use of top-of-atmosphere reflectance products in mapping supraglacial lake bathymetry because of the low performance of atmospheric corrections for water and snow/ice in both the Landsat-8 and Sentinel-2 datasets. This study is expected to provide a more efficient method for estimating the depth of supraglacial lakes and laying the foundation for accurately quantifying meltwater volumes over large surface areas in subsequent studies.