Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice
In order to satisfy the demand of key sea ice parameters, including melt pond depth H p and underlying ice thickness H i , in studies of Arctic sea ice change in summer, four algorithms of retrieving H p and H i were compared and validated by using optical data of melt ponds from field observations....
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ftdoajarticles:oai:doaj.org/article:ef63b392bd4640519e5e2f322cb69b08 2023-05-15T14:47:07+02:00 Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice Hang Zhang Peng Lu Miao Yu Jiaru Zhou Qingkai Wang Zhijun Li Limin Zhang 2022-06-01T00:00:00Z https://doi.org/10.3390/rs14122831 https://doaj.org/article/ef63b392bd4640519e5e2f322cb69b08 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/12/2831 https://doaj.org/toc/2072-4292 doi:10.3390/rs14122831 2072-4292 https://doaj.org/article/ef63b392bd4640519e5e2f322cb69b08 Remote Sensing, Vol 14, Iss 2831, p 2831 (2022) Arctic sea ice melt pond optical data remote sensing Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14122831 2022-12-30T22:59:36Z In order to satisfy the demand of key sea ice parameters, including melt pond depth H p and underlying ice thickness H i , in studies of Arctic sea ice change in summer, four algorithms of retrieving H p and H i were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of H p . For the retrieval of H i , Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of H i for Lu18 agreed better with field measurements for thin ice ( H i < 1 m) than that for thick ice, but those results of H p were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated H p , while showing a good agreement with H p < 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve H p and H i . Malimka18 also showed the ability to retrieve H i , except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of H p and H i under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer. Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 14 12 2831 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic sea ice melt pond optical data remote sensing Science Q |
spellingShingle |
Arctic sea ice melt pond optical data remote sensing Science Q Hang Zhang Peng Lu Miao Yu Jiaru Zhou Qingkai Wang Zhijun Li Limin Zhang Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice |
topic_facet |
Arctic sea ice melt pond optical data remote sensing Science Q |
description |
In order to satisfy the demand of key sea ice parameters, including melt pond depth H p and underlying ice thickness H i , in studies of Arctic sea ice change in summer, four algorithms of retrieving H p and H i were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of H p . For the retrieval of H i , Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of H i for Lu18 agreed better with field measurements for thin ice ( H i < 1 m) than that for thick ice, but those results of H p were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated H p , while showing a good agreement with H p < 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve H p and H i . Malimka18 also showed the ability to retrieve H i , except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of H p and H i under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer. |
format |
Article in Journal/Newspaper |
author |
Hang Zhang Peng Lu Miao Yu Jiaru Zhou Qingkai Wang Zhijun Li Limin Zhang |
author_facet |
Hang Zhang Peng Lu Miao Yu Jiaru Zhou Qingkai Wang Zhijun Li Limin Zhang |
author_sort |
Hang Zhang |
title |
Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice |
title_short |
Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice |
title_full |
Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice |
title_fullStr |
Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice |
title_full_unstemmed |
Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice |
title_sort |
comparison of pond depth and ice thickness retrieval algorithms for summer arctic sea ice |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14122831 https://doaj.org/article/ef63b392bd4640519e5e2f322cb69b08 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing, Vol 14, Iss 2831, p 2831 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/12/2831 https://doaj.org/toc/2072-4292 doi:10.3390/rs14122831 2072-4292 https://doaj.org/article/ef63b392bd4640519e5e2f322cb69b08 |
op_doi |
https://doi.org/10.3390/rs14122831 |
container_title |
Remote Sensing |
container_volume |
14 |
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12 |
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2831 |
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1766318256676667392 |