A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning
While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolutio...
Published in: | Remote Sensing of Environment |
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Main Authors: | , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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2023
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Online Access: | http://resolver.tudelft.nl/uuid:b0a752b0-7cd5-41a2-861a-4c51434582a5 https://doi.org/10.1016/j.rse.2023.113950 |
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fttudelft:oai:tudelft.nl:uuid:b0a752b0-7cd5-41a2-861a-4c51434582a5 2024-02-11T09:56:57+01:00 A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning de Roda Husman, S. (author) Lhermitte, S.L.M. (author) Bolibar, J. (author) Izeboud, M. (author) Hu, Zhongyang (author) Shukla, S. (author) van der Meer, Marijn (author) Long, David (author) Wouters, B. (author) 2023 http://resolver.tudelft.nl/uuid:b0a752b0-7cd5-41a2-861a-4c51434582a5 https://doi.org/10.1016/j.rse.2023.113950 en eng http://www.scopus.com/inward/record.url?scp=85179123328&partnerID=8YFLogxK Remote Sensing of Environment: an interdisciplinary journal--0034-4257--eabb69ab-5ad4-4725-92ed-698f8d9ef006 http://resolver.tudelft.nl/uuid:b0a752b0-7cd5-41a2-861a-4c51434582a5 https://doi.org/10.1016/j.rse.2023.113950 © 2023 S. de Roda Husman, S.L.M. Lhermitte, J. Bolibar, M. Izeboud, Zhongyang Hu, S. Shukla, Marijn van der Meer, David Long, B. Wouters Antarctica Enhanced resolution Google Earth Engine Machine learning Microwave remote sensing Surface melt U-Net journal article 2023 fttudelft https://doi.org/10.1016/j.rse.2023.113950 2024-01-24T23:35:27Z While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing. Physical and Space Geodesy Mathematical Geodesy and Positioning Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Shelf Ice Shelves Shackleton Ice Shelf Delft University of Technology: Institutional Repository Antarctic Shackleton Shackleton Ice Shelf ENVELOPE(100.504,100.504,-65.996,-65.996) Remote Sensing of Environment 301 113950 |
institution |
Open Polar |
collection |
Delft University of Technology: Institutional Repository |
op_collection_id |
fttudelft |
language |
English |
topic |
Antarctica Enhanced resolution Google Earth Engine Machine learning Microwave remote sensing Surface melt U-Net |
spellingShingle |
Antarctica Enhanced resolution Google Earth Engine Machine learning Microwave remote sensing Surface melt U-Net de Roda Husman, S. (author) Lhermitte, S.L.M. (author) Bolibar, J. (author) Izeboud, M. (author) Hu, Zhongyang (author) Shukla, S. (author) van der Meer, Marijn (author) Long, David (author) Wouters, B. (author) A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
topic_facet |
Antarctica Enhanced resolution Google Earth Engine Machine learning Microwave remote sensing Surface melt U-Net |
description |
While the influence of surface melt on Antarctic ice shelf stability can be large, the duration and affected area of melt events are often small. Therefore, melt events are difficult to capture with remote sensing, as satellite sensors always face the trade-off between spatial and temporal resolution. To overcome this limitation, we developed UMelt: a surface melt record for all Antarctic ice shelves with a high spatial (500 m) and high temporal (12 h) resolution for the period 2016–2021. Our approach is based on a deep learning model, specifically a U-Net, which was developed in Google Earth Engine. The U-Net combines microwave remote sensing observations from three sources: Sentinel-1, Special Sensor Microwave Imager/Sounder (SSMIS), and Advanced Scatterometer (ASCAT). The U-Net was trained on the Shackleton Ice Shelf for melt seasons 2017–2021, using the fine-scale melt patterns of Sentinel-1 as reference data and SSMIS, ASCAT, a digital elevation model, and multi-year Sentinel-1 melt fraction as predictors. The trained U-Net performed well on the Shackelton Ice Shelf for test melt season 2016–2017 (accuracy: 91.3%; F1-score: 86.9%), and the Larsen C Ice Shelf, which was not considered during training (accuracy: 91.0%; F1-score: 89.3%). Using the trained U-Net model, we have successfully developed the UMelt record. UMelt allows Antarctic-wide surface melt to be detected at a small scale while preserving a high temporal resolution, which could lead to new insights into the response of ice shelves to a changing atmospheric forcing. Physical and Space Geodesy Mathematical Geodesy and Positioning |
format |
Article in Journal/Newspaper |
author |
de Roda Husman, S. (author) Lhermitte, S.L.M. (author) Bolibar, J. (author) Izeboud, M. (author) Hu, Zhongyang (author) Shukla, S. (author) van der Meer, Marijn (author) Long, David (author) Wouters, B. (author) |
author_facet |
de Roda Husman, S. (author) Lhermitte, S.L.M. (author) Bolibar, J. (author) Izeboud, M. (author) Hu, Zhongyang (author) Shukla, S. (author) van der Meer, Marijn (author) Long, David (author) Wouters, B. (author) |
author_sort |
de Roda Husman, S. (author) |
title |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_short |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_full |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_fullStr |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_full_unstemmed |
A high-resolution record of surface melt on Antarctic ice shelves using multi-source remote sensing data and deep learning |
title_sort |
high-resolution record of surface melt on antarctic ice shelves using multi-source remote sensing data and deep learning |
publishDate |
2023 |
url |
http://resolver.tudelft.nl/uuid:b0a752b0-7cd5-41a2-861a-4c51434582a5 https://doi.org/10.1016/j.rse.2023.113950 |
long_lat |
ENVELOPE(100.504,100.504,-65.996,-65.996) |
geographic |
Antarctic Shackleton Shackleton Ice Shelf |
geographic_facet |
Antarctic Shackleton Shackleton Ice Shelf |
genre |
Antarc* Antarctic Antarctica Ice Shelf Ice Shelves Shackleton Ice Shelf |
genre_facet |
Antarc* Antarctic Antarctica Ice Shelf Ice Shelves Shackleton Ice Shelf |
op_relation |
http://www.scopus.com/inward/record.url?scp=85179123328&partnerID=8YFLogxK Remote Sensing of Environment: an interdisciplinary journal--0034-4257--eabb69ab-5ad4-4725-92ed-698f8d9ef006 http://resolver.tudelft.nl/uuid:b0a752b0-7cd5-41a2-861a-4c51434582a5 https://doi.org/10.1016/j.rse.2023.113950 |
op_rights |
© 2023 S. de Roda Husman, S.L.M. Lhermitte, J. Bolibar, M. Izeboud, Zhongyang Hu, S. Shukla, Marijn van der Meer, David Long, B. Wouters |
op_doi |
https://doi.org/10.1016/j.rse.2023.113950 |
container_title |
Remote Sensing of Environment |
container_volume |
301 |
container_start_page |
113950 |
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1790607272520450048 |