Automated extraction for Supraglacial lake in Greenland using Sentinel-1 SAR Imagery

Supraglacial lakes have a great impact on the mass balance and dynamics of Greenland ice sheet. While the current studies on these lakes mainly utilize optical observation data, the validity is poor, and it is impossible to conduct spatio-temporal analyses. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) data. By processing Sentinel-1 SAR dual-polarized imagery, a train dataset of 2664 image patches are formed. We use U-Net for segmentation and the associated Dice coefficient could reach higher than 95%. Besides, the terrain shadow is removed by DEM. The results are validated by supraglacial lake detection using Landsat 8. We will integrate more training data and optimize the feasibility of the model in future work.