Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data
Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are...
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Multidisciplinary Digital Publishing Institute (MDPI)
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ftdlr:oai:elib.dlr.de:139427 2023-05-15T13:53:09+02:00 Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data Zhu, Yongchao Tao, Tingye Yu, Kegen Qu, Xiaochuan Li, Shuiping Wickert, Jens Semmling, Maximilian 2020-11 application/pdf https://elib.dlr.de/139427/ https://elib.dlr.de/139427/1/manuscript.preprint.pdf en eng Multidisciplinary Digital Publishing Institute (MDPI) https://elib.dlr.de/139427/1/manuscript.preprint.pdf Zhu, Yongchao und Tao, Tingye und Yu, Kegen und Qu, Xiaochuan und Li, Shuiping und Wickert, Jens und Semmling, Maximilian (2020) Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data. Remote Sensing, 12 (3751). Multidisciplinary Digital Publishing Institute (MDPI). DOI:10.3390/rs12223751 <https://doi.org/10.3390/rs12223751> ISSN 2072-4292 Weltraumwetterbeobachtung Zeitschriftenbeitrag PeerReviewed 2020 ftdlr https://doi.org/10.3390/rs12223751 2021-02-01T00:03:26Z Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches. Other Non-Article Part of Journal/Newspaper Antarc* Antarctic Arctic Sea ice German Aerospace Center: elib - DLR electronic library Arctic Antarctic The Antarctic Remote Sensing 12 22 3751 |
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Open Polar |
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German Aerospace Center: elib - DLR electronic library |
op_collection_id |
ftdlr |
language |
English |
topic |
Weltraumwetterbeobachtung |
spellingShingle |
Weltraumwetterbeobachtung Zhu, Yongchao Tao, Tingye Yu, Kegen Qu, Xiaochuan Li, Shuiping Wickert, Jens Semmling, Maximilian Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
topic_facet |
Weltraumwetterbeobachtung |
description |
Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches. |
format |
Other Non-Article Part of Journal/Newspaper |
author |
Zhu, Yongchao Tao, Tingye Yu, Kegen Qu, Xiaochuan Li, Shuiping Wickert, Jens Semmling, Maximilian |
author_facet |
Zhu, Yongchao Tao, Tingye Yu, Kegen Qu, Xiaochuan Li, Shuiping Wickert, Jens Semmling, Maximilian |
author_sort |
Zhu, Yongchao |
title |
Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
title_short |
Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
title_full |
Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
title_fullStr |
Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
title_full_unstemmed |
Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
title_sort |
machine learning-aided sea ice monitoring using feature sequences extracted from spaceborne gnss-reflectometry data |
publisher |
Multidisciplinary Digital Publishing Institute (MDPI) |
publishDate |
2020 |
url |
https://elib.dlr.de/139427/ https://elib.dlr.de/139427/1/manuscript.preprint.pdf |
geographic |
Arctic Antarctic The Antarctic |
geographic_facet |
Arctic Antarctic The Antarctic |
genre |
Antarc* Antarctic Arctic Sea ice |
genre_facet |
Antarc* Antarctic Arctic Sea ice |
op_relation |
https://elib.dlr.de/139427/1/manuscript.preprint.pdf Zhu, Yongchao und Tao, Tingye und Yu, Kegen und Qu, Xiaochuan und Li, Shuiping und Wickert, Jens und Semmling, Maximilian (2020) Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data. Remote Sensing, 12 (3751). Multidisciplinary Digital Publishing Institute (MDPI). DOI:10.3390/rs12223751 <https://doi.org/10.3390/rs12223751> ISSN 2072-4292 |
op_doi |
https://doi.org/10.3390/rs12223751 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
22 |
container_start_page |
3751 |
_version_ |
1766258127908372480 |