A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning

• Published in: Remote Sensing
• Main Authors: Mariel Dirscherl, Andreas J. Dietz, Christof Kneisel, Claudia Kuenzer
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: Antarctica; Antarctic ice sheet; supraglacial lakes; ice sheet hydrology; Sentinel-1; remote sensing; Science; Q; Antarctic; Austral; George VI Ice Shelf; Greenland; Pyramid; Antarc*; Ice Sheet; Ice Shelf
• Online Access: https://doi.org/10.3390/rs13020197; https://doaj.org/article/aab7d1569523473baa9ba9baf93ab48c

Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km 2 ) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a <semantics> F 1 </semantics> -score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering ...