Automatic Supraglacial Lake Extraction in Greenland Using Sentinel-1 SAR Images and Attention-Based U-Net

With global warming, supraglacial lakes play an important role in ice sheet stability and climate change. They are not only the main factors affecting mass balance and sea-level rise but also the key units of surface runoff storage and mass loss. To automatically map the spatiotemporal distribution...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Di Jiang, Xinwu Li, Ke Zhang, Sebastián Marinsek, Wen Hong, Yirong Wu
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
supraglacial lake
SAR
deep learning
Greenland
Science
Q
glacier
Ice Sheet
Jakobshavn
Online Access:https://doi.org/10.3390/rs14194998
https://doaj.org/article/0efcece967994757b63e571f918edd8a
Description
Summary:With global warming, supraglacial lakes play an important role in ice sheet stability and climate change. They are not only the main factors affecting mass balance and sea-level rise but also the key units of surface runoff storage and mass loss. To automatically map the spatiotemporal distribution of supraglacial lakes in Greenland, this paper proposes an attention-based U-Net model with Sentinel-1 SAR imagery. The extraction results show that compared with the traditional network, this method obtains a higher validation coefficient, with an F1 score of 0.971, and it is spatiotemporally transferable, able to realize the extraction of supraglacial lakes in complex areas without ignoring small lakes. In addition, we conducted a case study in the Jakobshavn region and found that the supraglacial lake area peaked in advance between spring and summer due to extreme melting events from 2017 to 2021. Meanwhile, the supraglacial lakes near the 79°N Glacier tended to expand inland during the melting season.

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