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Article

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

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Str. 20, 82234 Wessling, Germany
2
Institute of Geography and Geology, University Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 197; https://doi.org/10.3390/rs13020197
Received: 9 December 2020 / Revised: 4 January 2021 / Accepted: 6 January 2021 / Published: 8 January 2021
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 km2) 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 F1-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter. View Full-Text
Keywords: Antarctica; Antarctic ice sheet; supraglacial lakes; ice sheet hydrology; Sentinel-1; remote sensing; machine learning; deep learning; semantic segmentation; convolutional neural network Antarctica; Antarctic ice sheet; supraglacial lakes; ice sheet hydrology; Sentinel-1; remote sensing; machine learning; deep learning; semantic segmentation; convolutional neural network
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MDPI and ACS Style

Dirscherl, M.; Dietz, A.J.; Kneisel, C.; Kuenzer, C. A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning. Remote Sens. 2021, 13, 197. https://doi.org/10.3390/rs13020197

AMA Style

Dirscherl M, Dietz AJ, Kneisel C, Kuenzer C. A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning. Remote Sensing. 2021; 13(2):197. https://doi.org/10.3390/rs13020197

Chicago/Turabian Style

Dirscherl, Mariel; Dietz, Andreas J.; Kneisel, Christof; Kuenzer, Claudia. 2021. "A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning" Remote Sens. 13, no. 2: 197. https://doi.org/10.3390/rs13020197

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