A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets

Abstract. Supraglacial lakes on the ice sheets have been linked to ice shelf collapse in Antarctica and accelerated flow of grounded ice in Greenland. However, it is difficult to quantify the impact of supraglacial lakes on ice dynamics accurately enough to predict their contribution to future mass loss and sea level rise. This is largely because ice-sheet-wide assessments of meltwater volumes rely on models that are poorly constrained due to a lack of accurate depth measurements. Various recent case studies have demonstrated that accurate supraglacial lake depths can be obtained from ICESat-2’s ATL03 photon-level data product. ATL03 comprises hundreds of terabytes of unstructured point cloud data, which has made it challenging to use this bathymetric capability at scale. Here, we present two new algorithms – Flat Lake and Underlying Ice Detection (FLUID) and Surface Removal and Robust Fit (SuRFF) – which together provide a fully automated and scalable method for lake detection and depth determination from ATL03 data, and establish a framework for its large-scale implementation using distributed high-throughput computing. We report FLUID/SuRFF algorithm performance over two regions known to have significant surface melt – Central West Greenland and Amery Ice Shelf catchment in East Antarctica – during two melt seasons. FLUID/SuRFF reveals a total of 1249 lakes up to 25 m deep, with more water during higher melt years. In absence of ground truth data, manual annotation of test data suggests that our method reliably detects melt lakes whenever a bathymetric signal is discernible, and estimates water depths with a mean absolute error of 0.28 m. These results imply that our proposed framework has the potential to generate a comprehensive data product of accurate meltwater depths across both ice sheets.