A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets

Abstract

Abstract. Water depths of supraglacial lakes on the ice sheets are difficult to monitor continuously due the lakes' ephemeral nature and inaccessible locations. Supraglacial lakes have been linked to ice shelf collapse in Antarctica and accelerated flow of grounded ice in Greenland. However, the impact of supraglacial lakes on ice dynamics has not been quantified 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 NASA's Ice, Cloud and land Elevation Satellite (ICESat-2) 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 (SuRRF) – which together provide a fully automated and scalable method for lake detection and along-track depth determination from ATL03 data and establish a framework for its large-scale implementation using distributed high-throughput computing. We report FLUID–SuRRF algorithm performance over two regions known to have significant surface melt – central West Greenland and the Amery Ice Shelf catchment in East Antarctica – during two melt seasons. FLUID–SuRRF reveals a total of 1249 ICESat-2 lake segments up to 25 m deep, with more water during higher-melt years. In the absence of ground-truth data, manual annotation of test data suggests that our method reliably detects melt lakes along ICESat-2's ground tracks whenever the lake bed is visible or partially visible and estimates water depths with a mean absolute error <0.27 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.