AN AUTOMATIC EXTRACTION METHOD FOR ANTARCTIC SUBGLACIAL LAKE BASED ON RADIO ECHO SOUNDING DATA

During the melting season, substantial quantities of surface meltwater converge in topographically depressed regions, forming supraglacial lakes (SGLs). We extract SGL area and profile depth using remote sensing data and then invert the depth of entire SGLs based on the machine learning method. By applying the above-mentioned methods, we capture the volumetric evolution of SGLs throughout the entire melt season of 2022 in southwestern Greenland.

This study focuses on the long-term subglacial hydrological activity of the Recovery Ice Stream in East Antarctica. We update the outlines of reported lakes based on their recent activity and identify 14 new lakes. A 21-year record of elevation changes of these lakes is constructed using multi-mission altimetry data. We also reveal spatial variability in elevation change patterns within several typical individual lakes. Our hydrological network confirms hydraulic connectivity among these lakes.

We propose an innovative multi-satellite DEM adjustment model (MDAM) that removes biases in elevation between sub-DEMs across ice shelves. Our results reveal quantitative 3D structural and mélange features of a ~50 km long rift. For first time, we found that while the mélange elevation decreased from 2014–2021, the mélange inside the rift experienced a rapid expansion, attributing to newly calved shelf ice from rift walls, associated rift widening, and other rift-mélange interaction factors.