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

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 \(25 \mathrm{\,m}\) 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 \(<0.27 \mathrm{m\,}\). These results imply that our proposed framework has the potential to generate a comprehensive data product of accurate meltwater depths across both ice sheets.

Links, data and code:

• Link to the paper and discussion: https://doi.org/10.5194/tc-18-5173-2024
• Paper PDF
• Data set for all detected lakes, with depths and imagery plots
• Source code: Zenodo, GitHub
• Data and code for figure reproduction: Zenodo, GitHub

Some figures of the main results

(all figures are in the README of this GitHub repo.)

Center maps: FLUID/SURFF algorithm testing in Greenland. Locations of melt lakes detected in ATL03 data for the Greenland Ice Sheet’s Central West drainage basin for melt seasons 2019 and 2020, mapped over the corresponding seasons’ maximum meltwater extent from Landsat 8. a)-j): Examples showing the underlying ATL03 photon clouds and water depths calculated by SuRRF, for some of the lakes shown on the maps. Numbers in lower right of panels are SuRFF lake quality scores.

Center maps: FLUID/SuRFF algorithm testing in Antarctica. Locations of melt lakes that FLUID detected in ATL03 data for the Antarctic Ice Sheet’s B-C drainage basin for melt seasons 2019-20 and 2020-21, mapped over the corresponding seasons’ maximum meltwater extent from Landsat 8. Panels a-j: Examples showing the underlying ATL03 photon clouds and water depths calculated by SuRRF, for some of the lakes shown on the maps. Numbers in lower right of panels are SuRFF lake quality scores.

Recommended citation: Arndt, P. S. and Fricker, H. A.: A framework for automated supraglacial lake detection and depth retrieval in ICESat-2 photon data across the Greenland and Antarctic ice sheets, The Cryosphere, 18, 5173–5206, 2024. https://doi.org/10.5194/tc-18-5173-2024