Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network
The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of...
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ftdoajarticles:oai:doaj.org/article:1849ea4c99aa469293a20ee80ffa60eb 2023-05-15T13:11:18+02:00 Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network Yifan Ding Xiao Cheng Jiping Liu Fengming Hui Zhenzhan Wang Shengzhe Chen 2020-08-01T00:00:00Z https://doi.org/10.3390/rs12172746 https://doaj.org/article/1849ea4c99aa469293a20ee80ffa60eb EN eng MDPI AG https://www.mdpi.com/2072-4292/12/17/2746 https://doaj.org/toc/2072-4292 doi:10.3390/rs12172746 2072-4292 https://doaj.org/article/1849ea4c99aa469293a20ee80ffa60eb Remote Sensing, Vol 12, Iss 2746, p 2746 (2020) melt pond fraction Arctic sea ice deep neural network Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12172746 2022-12-31T11:08:41Z The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season. Article in Journal/Newspaper albedo Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 12 17 2746 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
melt pond fraction Arctic sea ice deep neural network Science Q |
spellingShingle |
melt pond fraction Arctic sea ice deep neural network Science Q Yifan Ding Xiao Cheng Jiping Liu Fengming Hui Zhenzhan Wang Shengzhe Chen Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network |
topic_facet |
melt pond fraction Arctic sea ice deep neural network Science Q |
description |
The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season. |
format |
Article in Journal/Newspaper |
author |
Yifan Ding Xiao Cheng Jiping Liu Fengming Hui Zhenzhan Wang Shengzhe Chen |
author_facet |
Yifan Ding Xiao Cheng Jiping Liu Fengming Hui Zhenzhan Wang Shengzhe Chen |
author_sort |
Yifan Ding |
title |
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network |
title_short |
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network |
title_full |
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network |
title_fullStr |
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network |
title_full_unstemmed |
Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network |
title_sort |
retrieval of melt pond fraction over arctic sea ice during 2000–2019 using an ensemble-based deep neural network |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12172746 https://doaj.org/article/1849ea4c99aa469293a20ee80ffa60eb |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Sea ice |
genre_facet |
albedo Arctic Sea ice |
op_source |
Remote Sensing, Vol 12, Iss 2746, p 2746 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/17/2746 https://doaj.org/toc/2072-4292 doi:10.3390/rs12172746 2072-4292 https://doaj.org/article/1849ea4c99aa469293a20ee80ffa60eb |
op_doi |
https://doi.org/10.3390/rs12172746 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
17 |
container_start_page |
2746 |
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1766246812007530496 |