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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ftdoajarticles:oai:doaj.org/article:049099650a234f1e83fecf3f9dfd0523 2023-05-15T13:52:42+02:00 Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data Yongchao Zhu Tingye Tao Kegen Yu Xiaochuan Qu Shuiping Li Jens Wickert Maximilian Semmling 2020-11-01T00:00:00Z https://doi.org/10.3390/rs12223751 https://doaj.org/article/049099650a234f1e83fecf3f9dfd0523 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/22/3751 https://doaj.org/toc/2072-4292 doi:10.3390/rs12223751 2072-4292 https://doaj.org/article/049099650a234f1e83fecf3f9dfd0523 Remote Sensing, Vol 12, Iss 3751, p 3751 (2020) Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12223751 2022-12-31T03:19:35Z 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. Article in Journal/Newspaper Antarc* Antarctic Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Antarctic Arctic The Antarctic Remote Sensing 12 22 3751 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring Science Q |
spellingShingle |
Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring Science Q Yongchao Zhu Tingye Tao Kegen Yu Xiaochuan Qu Shuiping Li Jens Wickert Maximilian Semmling Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data |
topic_facet |
Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring Science Q |
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 |
Article in Journal/Newspaper |
author |
Yongchao Zhu Tingye Tao Kegen Yu Xiaochuan Qu Shuiping Li Jens Wickert Maximilian Semmling |
author_facet |
Yongchao Zhu Tingye Tao Kegen Yu Xiaochuan Qu Shuiping Li Jens Wickert Maximilian Semmling |
author_sort |
Yongchao Zhu |
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 |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12223751 https://doaj.org/article/049099650a234f1e83fecf3f9dfd0523 |
geographic |
Antarctic Arctic The Antarctic |
geographic_facet |
Antarctic Arctic The Antarctic |
genre |
Antarc* Antarctic Arctic Sea ice |
genre_facet |
Antarc* Antarctic Arctic Sea ice |
op_source |
Remote Sensing, Vol 12, Iss 3751, p 3751 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/22/3751 https://doaj.org/toc/2072-4292 doi:10.3390/rs12223751 2072-4292 https://doaj.org/article/049099650a234f1e83fecf3f9dfd0523 |
op_doi |
https://doi.org/10.3390/rs12223751 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
22 |
container_start_page |
3751 |
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1766257155960209408 |