Automated Detection and Mapping of Supraglacial Lakes Using Machine Learning From ICESat-2 and Sentinel-2 Data

Although the supraglacial lakes are significant for a comprehensive understanding of the glacier material balance mechanism, obtaining accurate supraglacial lake data by satellite remote sensing is challenging. The lack of 3-D information limits the accuracy of image-based methods, while the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data are limited to satellite trajectories. In this study, the detection and mapping of the supraglacial lakes are implemented by jointly processing the ICESat-2 and Sentinel-2 data. The lake-region images are extracted with the help of ICESat-2 photons, and the lake depths are retrieved. The depths of four supraglacial lakes on the Amery Ice Shelf (AIS), Antarctica, were mapped by trained machine learning methods and Bayesian optimization (BO). The performance of the proposed method is evaluated by comparing it with manual results or self-verification. The results show that joint lake region extraction accuracy is 95.16%. The proposed photon extraction method, combined with BO-categorical boosting (CatBoost), achieves the highest accuracy in retrieval, with a root mean square error (RMSE) of 0.17 m. This study enables automatic operation without input parameters, thus improving the performance and automation level of supraglacial lake observation. With more abundant ICESat-2 data and multispectral images, the proposed method can automatically obtain large-scale surface melting in time and space.