Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach
During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper pro...
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ftwhioceojs:oai:ojs.ojs.whioce.com:article/692 2023-05-15T13:10:56+02:00 Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach Liu 1, Qi Zhang 1, Yawen 2018-08-16 application/pdf https://ojs.whioce.com/index.php/som/article/view/692 https://doi.org/10.18063/som.v3i3.692 eng eng Whioce Publishing Pte Ltd https://ojs.whioce.com/index.php/som/article/view/692/509 https://ojs.whioce.com/index.php/som/article/view/692 doi:10.18063/som.v3i3.692 Copyright (c) 2018 Satellite Oceanography and Meteorology Satellite Oceanography and Meteorology; Pre-published article 2424-9505 2424-8959 Multi-layer neural network high-resolution imagery melt pond fraction info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2018 ftwhioceojs https://doi.org/10.18063/som.v3i3.692 2022-04-06T07:28:29Z During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results. Article in Journal/Newspaper albedo Arctic Sea ice Whioce Journals Arctic Satellite Oceanography and Meteorology 3 3 |
institution |
Open Polar |
collection |
Whioce Journals |
op_collection_id |
ftwhioceojs |
language |
English |
topic |
Multi-layer neural network high-resolution imagery melt pond fraction |
spellingShingle |
Multi-layer neural network high-resolution imagery melt pond fraction Liu 1, Qi Zhang 1, Yawen Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach |
topic_facet |
Multi-layer neural network high-resolution imagery melt pond fraction |
description |
During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multi-period visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results. |
format |
Article in Journal/Newspaper |
author |
Liu 1, Qi Zhang 1, Yawen |
author_facet |
Liu 1, Qi Zhang 1, Yawen |
author_sort |
Liu 1, Qi |
title |
Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach |
title_short |
Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach |
title_full |
Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach |
title_fullStr |
Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach |
title_full_unstemmed |
Applying high-resolution visible imagery to satellite melt pond fraction retrieval: A neural network approach |
title_sort |
applying high-resolution visible imagery to satellite melt pond fraction retrieval: a neural network approach |
publisher |
Whioce Publishing Pte Ltd |
publishDate |
2018 |
url |
https://ojs.whioce.com/index.php/som/article/view/692 https://doi.org/10.18063/som.v3i3.692 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Sea ice |
genre_facet |
albedo Arctic Sea ice |
op_source |
Satellite Oceanography and Meteorology; Pre-published article 2424-9505 2424-8959 |
op_relation |
https://ojs.whioce.com/index.php/som/article/view/692/509 https://ojs.whioce.com/index.php/som/article/view/692 doi:10.18063/som.v3i3.692 |
op_rights |
Copyright (c) 2018 Satellite Oceanography and Meteorology |
op_doi |
https://doi.org/10.18063/som.v3i3.692 |
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Satellite Oceanography and Meteorology |
container_volume |
3 |
container_issue |
3 |
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1766245305978716160 |