Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ...
Abstract: Supraglacial lakes are an important parameter for understanding the current and future state of the Greenland Ice Sheet. Previous studies have focused on mapping supraglacial lake extent using optical and radar imagery, while lake depth is more difficult to estimate due to sparse temporal...
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ftdatacite:10.5281/zenodo.7981530 2023-06-11T04:12:10+02:00 Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... Robinson, Abigail Elizabeth Völgyes, David Vermeer, Martijn Fantin, Daniele Stefano Maria Sørensen, Louise Sandberg Kruse, Mikkel Aaby Frosch, Sabine 2023 https://dx.doi.org/10.5281/zenodo.7981530 https://zenodo.org/record/7981530 en eng Zenodo https://dx.doi.org/10.5281/zenodo.7981531 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess ConferencePaper Article 2023 ftdatacite https://doi.org/10.5281/zenodo.798153010.5281/zenodo.7981531 2023-06-01T12:19:14Z Abstract: Supraglacial lakes are an important parameter for understanding the current and future state of the Greenland Ice Sheet. Previous studies have focused on mapping supraglacial lake extent using optical and radar imagery, while lake depth is more difficult to estimate due to sparse temporal and spatial coverage of laser altimeters such as ICESat-2. We present a supervised deep learning approach to predict lake extent and depth based on the subtle spectral signatures acquired from Sentinel2 imagery. The model is trained on an existing lake extent product and elevation profiles derived from ICESat-2. The output of this approach is a proof-of-concept study whereby deep learning can utilise contextual information from the input image to produce a lake depth and extent prediction. The preliminary results show that the methodology is feasible as the output model successfully produces a reasonable lake extent and depth prediction despite data limitations. This work forms part of the European Space Agency’s ... Conference Object Greenland Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) Greenland |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
Abstract: Supraglacial lakes are an important parameter for understanding the current and future state of the Greenland Ice Sheet. Previous studies have focused on mapping supraglacial lake extent using optical and radar imagery, while lake depth is more difficult to estimate due to sparse temporal and spatial coverage of laser altimeters such as ICESat-2. We present a supervised deep learning approach to predict lake extent and depth based on the subtle spectral signatures acquired from Sentinel2 imagery. The model is trained on an existing lake extent product and elevation profiles derived from ICESat-2. The output of this approach is a proof-of-concept study whereby deep learning can utilise contextual information from the input image to produce a lake depth and extent prediction. The preliminary results show that the methodology is feasible as the output model successfully produces a reasonable lake extent and depth prediction despite data limitations. This work forms part of the European Space Agency’s ... |
format |
Conference Object |
author |
Robinson, Abigail Elizabeth Völgyes, David Vermeer, Martijn Fantin, Daniele Stefano Maria Sørensen, Louise Sandberg Kruse, Mikkel Aaby Frosch, Sabine |
spellingShingle |
Robinson, Abigail Elizabeth Völgyes, David Vermeer, Martijn Fantin, Daniele Stefano Maria Sørensen, Louise Sandberg Kruse, Mikkel Aaby Frosch, Sabine Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... |
author_facet |
Robinson, Abigail Elizabeth Völgyes, David Vermeer, Martijn Fantin, Daniele Stefano Maria Sørensen, Louise Sandberg Kruse, Mikkel Aaby Frosch, Sabine |
author_sort |
Robinson, Abigail Elizabeth |
title |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... |
title_short |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... |
title_full |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... |
title_fullStr |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... |
title_full_unstemmed |
Deep learning-based supraglacial lake extent and depth detection on the Greenland Ice Sheet by combining ICESat-2 and Sentinel-2 data ... |
title_sort |
deep learning-based supraglacial lake extent and depth detection on the greenland ice sheet by combining icesat-2 and sentinel-2 data ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.7981530 https://zenodo.org/record/7981530 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_relation |
https://dx.doi.org/10.5281/zenodo.7981531 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.798153010.5281/zenodo.7981531 |
_version_ |
1768387849410838528 |