A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery
Abstract Melt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of me...
Published in: | Journal of Geophysical Research: Oceans |
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ftunivhelsihelda:oai:helda.helsinki.fi:10138/327190 2024-01-07T09:38:04+01:00 A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery Mingfeng, Wang Jie, Su Landy, Jack Leppäranta, Matti Lei, Guan Institute for Atmospheric and Earth System Research (INAR) Department of Physics 2021-02-26T23:03:06Z 14 application/pdf http://hdl.handle.net/10138/327190 eng eng American Geophysical Union 10.1029/2019JC015716 Mingfeng , W , Jie , S , Landy , J , Leppäranta , M & Lei , G 2020 , ' A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery ' , Journal of Geophysical Research : Oceans , vol. 125 , no. 10 , e2019JC015716 . https://doi.org/10.1029/2019JC015716 RIS: urn:C30A8EBED79B2B7AB7924CBE3F7385CF e31d710d-5751-4898-a207-65a6d80d62db http://hdl.handle.net/10138/327190 000612077200004 openAccess info:eu-repo/semantics/openAccess Melt ponds Sea ice Remote sensing Arctic 114 Physical sciences 119 Other natural sciences Article acceptedVersion 2021 ftunivhelsihelda 2023-12-14T00:10:38Z Abstract Melt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of melt pond fraction (MPF) data for studying these processes. MPF information has generally been acquired from optical imagery. Conventional MPF algorithms based on high-resolution optical sensors have treated melt ponds as features with constant reflectance; however, the spectral reflectance of ponds can vary greatly, even at a local scale. Here we use Sentinel-2 imagery to demonstrate those previous algorithms assuming fixed melt pond-reflectance greatly underestimate MPF. We propose a new algorithm (?LinearPolar?) based on the polar coordinate transformation that treats melt ponds as variable-reflectance features and calculates MPF across the vector between melt pond and bare ice axes. The angular coordinate ? of the polar coordinate system, which is only associated with pond fraction rather than reflectance, is used to determinate MPF. By comparing the new algorithm and previous methods with IceBridge optical imagery data, across a variety of Sentinel-2 images with melt ponds at various stages of development, we show that the RMSE value of the LinearPolar algorithm is about 30% lower than for the previous algorithms. Moreover, based on a sensitivity test, the new algorithm is also less sensitive to the subjective threshold for melt pond reflectance than previous algorithms. Peer reviewed Article in Journal/Newspaper albedo Arctic Sea ice HELDA – University of Helsinki Open Repository Arctic Journal of Geophysical Research: Oceans 125 10 |
institution |
Open Polar |
collection |
HELDA – University of Helsinki Open Repository |
op_collection_id |
ftunivhelsihelda |
language |
English |
topic |
Melt ponds Sea ice Remote sensing Arctic 114 Physical sciences 119 Other natural sciences |
spellingShingle |
Melt ponds Sea ice Remote sensing Arctic 114 Physical sciences 119 Other natural sciences Mingfeng, Wang Jie, Su Landy, Jack Leppäranta, Matti Lei, Guan A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
topic_facet |
Melt ponds Sea ice Remote sensing Arctic 114 Physical sciences 119 Other natural sciences |
description |
Abstract Melt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of melt pond fraction (MPF) data for studying these processes. MPF information has generally been acquired from optical imagery. Conventional MPF algorithms based on high-resolution optical sensors have treated melt ponds as features with constant reflectance; however, the spectral reflectance of ponds can vary greatly, even at a local scale. Here we use Sentinel-2 imagery to demonstrate those previous algorithms assuming fixed melt pond-reflectance greatly underestimate MPF. We propose a new algorithm (?LinearPolar?) based on the polar coordinate transformation that treats melt ponds as variable-reflectance features and calculates MPF across the vector between melt pond and bare ice axes. The angular coordinate ? of the polar coordinate system, which is only associated with pond fraction rather than reflectance, is used to determinate MPF. By comparing the new algorithm and previous methods with IceBridge optical imagery data, across a variety of Sentinel-2 images with melt ponds at various stages of development, we show that the RMSE value of the LinearPolar algorithm is about 30% lower than for the previous algorithms. Moreover, based on a sensitivity test, the new algorithm is also less sensitive to the subjective threshold for melt pond reflectance than previous algorithms. Peer reviewed |
author2 |
Institute for Atmospheric and Earth System Research (INAR) Department of Physics |
format |
Article in Journal/Newspaper |
author |
Mingfeng, Wang Jie, Su Landy, Jack Leppäranta, Matti Lei, Guan |
author_facet |
Mingfeng, Wang Jie, Su Landy, Jack Leppäranta, Matti Lei, Guan |
author_sort |
Mingfeng, Wang |
title |
A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
title_short |
A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
title_full |
A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
title_fullStr |
A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
title_full_unstemmed |
A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
title_sort |
new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery |
publisher |
American Geophysical Union |
publishDate |
2021 |
url |
http://hdl.handle.net/10138/327190 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Sea ice |
genre_facet |
albedo Arctic Sea ice |
op_relation |
10.1029/2019JC015716 Mingfeng , W , Jie , S , Landy , J , Leppäranta , M & Lei , G 2020 , ' A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery ' , Journal of Geophysical Research : Oceans , vol. 125 , no. 10 , e2019JC015716 . https://doi.org/10.1029/2019JC015716 RIS: urn:C30A8EBED79B2B7AB7924CBE3F7385CF e31d710d-5751-4898-a207-65a6d80d62db http://hdl.handle.net/10138/327190 000612077200004 |
op_rights |
openAccess info:eu-repo/semantics/openAccess |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
125 |
container_issue |
10 |
_version_ |
1787429469641768960 |