Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter
The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span...
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ftmdpi:oai:mdpi.com:/2072-4292/14/18/4538/ 2023-08-20T04:03:56+02:00 Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter Zeli Peng Yinghui Ding Ying Qu Mengsi Wang Xijia Li agris 2022-09-11 application/pdf https://doi.org/10.3390/rs14184538 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14184538 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 18; Pages: 4538 melt pond fraction sea ice Arctic artificial neural network statistical-based temporal filter MODIS Text 2022 ftmdpi https://doi.org/10.3390/rs14184538 2023-08-01T06:26:01Z The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 14 18 4538 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
melt pond fraction sea ice Arctic artificial neural network statistical-based temporal filter MODIS |
spellingShingle |
melt pond fraction sea ice Arctic artificial neural network statistical-based temporal filter MODIS Zeli Peng Yinghui Ding Ying Qu Mengsi Wang Xijia Li Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter |
topic_facet |
melt pond fraction sea ice Arctic artificial neural network statistical-based temporal filter MODIS |
description |
The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces. |
format |
Text |
author |
Zeli Peng Yinghui Ding Ying Qu Mengsi Wang Xijia Li |
author_facet |
Zeli Peng Yinghui Ding Ying Qu Mengsi Wang Xijia Li |
author_sort |
Zeli Peng |
title |
Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter |
title_short |
Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter |
title_full |
Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter |
title_fullStr |
Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter |
title_full_unstemmed |
Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter |
title_sort |
generating a long-term spatiotemporally continuous melt pond fraction dataset for arctic sea ice using an artificial neural network and a statistical-based temporal filter |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14184538 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 14; Issue 18; Pages: 4538 |
op_relation |
Remote Sensing Image Processing https://dx.doi.org/10.3390/rs14184538 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14184538 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
18 |
container_start_page |
4538 |
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1774714351149645824 |