Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data

Abstract

The grounding line marks the transition between a glacier's floating and grounded parts and serves as a crucial parameter for monitoring sea level changes and assessing glacier retreat. The Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for grounding line mapping currently requires the involvement of human experts, which becomes challenging with the continuously growing volume of grounding line data available for every Antarctic glacier. While a deep learning approach has been recently proposed for mapping grounding lines over C-band Sentinel-1 DInSAR data, its effectiveness has not been assessed over X-Band COSMO-SkyMed DInSAR data. Similarly, the applicability of an analytical algorithm developed for X-band TerraSAR-X DInSAR data has not been evaluated over a large diverse dataset. Here we apply both techniques to map grounding lines over a large X-band COSMO-SkyMed DInSAR dataset from 2020 to 2022, covering Stancomb-Wills, Veststraumen, Jutulstraumen, Moscow University, and Rennick Antarctic glaciers. We determine strengths and limitations of each algorithm, compare their performance with manual mapping and provide recommendations for choosing appropriate data processing methods for effective grounding line mapping. We also note that since 1996, Moscow University glacier's main trunk was retreating at a rate of 340 ± 80 m/year, while the other four glaciers experienced no retreat. Considering the grounding zone widths, which represent the difference between the high and low tide grounding line positions during a tidal cycle, we detect a grounding zone of 9.7 km over Veststraumen Glacier, which is almost six times larger than the average grounding zone of the other four glaciers.