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Remote Sensing
Volume 14
Issue 19
10.3390/rs14194998
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Article

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

by
Di Jiang
1,2,3,
Xinwu Li
1,4,*,
Ke Zhang
1,4,
Sebastián Marinsek
5,
Wen Hong
1,2,3 and
Yirong Wu
1,2,3
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China
4
Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, Beijing 100094, China
5
Instituto Antártico Argentino, Bueno Aires B1650HMK, Argentina
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(19), 4998; https://doi.org/10.3390/rs14194998
Submission received: 30 July 2022 / Revised: 19 September 2022 / Accepted: 5 October 2022 / Published: 8 October 2022
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications II)
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Abstract

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.
Keywords: supraglacial lake; SAR; deep learning; Greenland supraglacial lake; SAR; deep learning; Greenland

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MDPI and ACS Style

Jiang, D.; Li, X.; Zhang, K.; Marinsek, S.; Hong, W.; Wu, Y. Automatic Supraglacial Lake Extraction in Greenland Using Sentinel-1 SAR Images and Attention-Based U-Net. Remote Sens. 2022, 14, 4998. https://doi.org/10.3390/rs14194998

AMA Style

Jiang D, Li X, Zhang K, Marinsek S, Hong W, Wu Y. Automatic Supraglacial Lake Extraction in Greenland Using Sentinel-1 SAR Images and Attention-Based U-Net. Remote Sensing. 2022; 14(19):4998. https://doi.org/10.3390/rs14194998

Chicago/Turabian Style

Jiang, Di, Xinwu Li, Ke Zhang, Sebastián Marinsek, Wen Hong, and Yirong Wu. 2022. "Automatic Supraglacial Lake Extraction in Greenland Using Sentinel-1 SAR Images and Attention-Based U-Net" Remote Sensing 14, no. 19: 4998. https://doi.org/10.3390/rs14194998

APA Style

Jiang, D., Li, X., Zhang, K., Marinsek, S., Hong, W., & Wu, Y. (2022). Automatic Supraglacial Lake Extraction in Greenland Using Sentinel-1 SAR Images and Attention-Based U-Net. Remote Sensing, 14(19), 4998. https://doi.org/10.3390/rs14194998

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