Recognizing the Shape and Size of Tundra Lakes in Synthetic Aperture Radar (SAR) Images Using Deep Learning Segmentation

Denis Demchev, Ivan Sudakow, Alexander Khodos, Irina Abramova, Dmitry Lyakhov, Dominik L. Michels

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere, and it is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of global climate processes, and is still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) is a suitable tool for tundra lakes recognition and monitoring of their changes. However, manual analysis of lake boundaries can be slow and inefficient; therefore, reliable automated algorithms are required. To address this issue, we propose a two-stage approach, comprising instance deep-learning-based segmentation by U-Net, followed by semantic segmentation based on a watershed algorithm for separating touching and overlapping lakes. Implementation of this concept is essential for accurate sizes and shapes estimation of an individual lake. Here, we evaluated the performance of the proposed approach on lakes, manually extracted from tens of C-band SAR images from Sentinel-1, which were collected in the Yamal Peninsula and Alaska areas in the summer months of 2015–2022. An accuracy of 0.73, in terms of the Jaccard similarity index, was achieved. The lake’s perimeter, area and fractal sizes were estimated, based on the algorithm framework output from hundreds of SAR images. It was recognized as lognormal distributed. The evaluation of the results indicated the efficiency of the proposed approach for accurate automatic estimation of tundra lake shapes and sizes, and its potential to be used for further studies on tundra lake dynamics, in the context of global climate change, aimed at revealing new factors that could cause the planet to warm or cool.
Original languageEnglish (US)
Pages (from-to)1298
JournalRemote Sensing
Issue number5
StatePublished - Feb 26 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-03-02
Acknowledgements: This research was funded by the Russian Science Foundation (RSF), project # 21-71-10052. D.L. and D.M. were partially supported by KAUST baseline funding. The authors thank Viktoria Kharchenko for manual labeling of part of the Sentinel-1 training and validation images

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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