Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected o...
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Multidisciplinary Digital Publishing Institute (MDPI)
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Online Access: | https://elib.dlr.de/145661/ https://elib.dlr.de/145661/1/2021-ZhuY-001.pdf https://www.mdpi.com/2072-4292/13/22/4577 |
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ftdlr:oai:elib.dlr.de:145661 2024-01-14T10:04:58+01:00 Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers Zhu, Yongchao Tao, Tingye Li, Jiangyang Yu, Kegen Wang, Lei Qu, Xiaochuan Li, Shuiping Semmling, Maximilian Wickert, Jens 2021-11-15 application/pdf https://elib.dlr.de/145661/ https://elib.dlr.de/145661/1/2021-ZhuY-001.pdf https://www.mdpi.com/2072-4292/13/22/4577 en eng Multidisciplinary Digital Publishing Institute (MDPI) https://elib.dlr.de/145661/1/2021-ZhuY-001.pdf Zhu, Yongchao und Tao, Tingye und Li, Jiangyang und Yu, Kegen und Wang, Lei und Qu, Xiaochuan und Li, Shuiping und Semmling, Maximilian und Wickert, Jens (2021) Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers. Remote Sensing. Multidisciplinary Digital Publishing Institute (MDPI). doi:10.3390/rs13224577 <https://doi.org/10.3390/rs13224577>. ISSN 2072-4292. Institut für Solar-Terrestrische Physik Zeitschriftenbeitrag PeerReviewed 2021 ftdlr https://doi.org/10.3390/rs13224577 2023-12-18T00:23:58Z The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications. Article in Journal/Newspaper Arctic Climate change Sea ice German Aerospace Center: elib - DLR electronic library Arctic Remote Sensing 13 22 4577 |
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Open Polar |
collection |
German Aerospace Center: elib - DLR electronic library |
op_collection_id |
ftdlr |
language |
English |
topic |
Institut für Solar-Terrestrische Physik |
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Institut für Solar-Terrestrische Physik Zhu, Yongchao Tao, Tingye Li, Jiangyang Yu, Kegen Wang, Lei Qu, Xiaochuan Li, Shuiping Semmling, Maximilian Wickert, Jens Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers |
topic_facet |
Institut für Solar-Terrestrische Physik |
description |
The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications. |
format |
Article in Journal/Newspaper |
author |
Zhu, Yongchao Tao, Tingye Li, Jiangyang Yu, Kegen Wang, Lei Qu, Xiaochuan Li, Shuiping Semmling, Maximilian Wickert, Jens |
author_facet |
Zhu, Yongchao Tao, Tingye Li, Jiangyang Yu, Kegen Wang, Lei Qu, Xiaochuan Li, Shuiping Semmling, Maximilian Wickert, Jens |
author_sort |
Zhu, Yongchao |
title |
Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers |
title_short |
Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers |
title_full |
Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers |
title_fullStr |
Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers |
title_full_unstemmed |
Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers |
title_sort |
spaceborne gnss-r for sea ice classification using machine learning classifiers |
publisher |
Multidisciplinary Digital Publishing Institute (MDPI) |
publishDate |
2021 |
url |
https://elib.dlr.de/145661/ https://elib.dlr.de/145661/1/2021-ZhuY-001.pdf https://www.mdpi.com/2072-4292/13/22/4577 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_relation |
https://elib.dlr.de/145661/1/2021-ZhuY-001.pdf Zhu, Yongchao und Tao, Tingye und Li, Jiangyang und Yu, Kegen und Wang, Lei und Qu, Xiaochuan und Li, Shuiping und Semmling, Maximilian und Wickert, Jens (2021) Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers. Remote Sensing. Multidisciplinary Digital Publishing Institute (MDPI). doi:10.3390/rs13224577 <https://doi.org/10.3390/rs13224577>. ISSN 2072-4292. |
op_doi |
https://doi.org/10.3390/rs13224577 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
22 |
container_start_page |
4577 |
_version_ |
1788059390488608768 |