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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ftmdpi:oai:mdpi.com:/2072-4292/12/22/3751/ 2023-08-20T04:02:25+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 agris 2020-11-14 application/pdf https://doi.org/10.3390/rs12223751 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12223751 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 22; Pages: 3751 Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring Text 2020 ftmdpi https://doi.org/10.3390/rs12223751 2023-08-01T00:27:58Z 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. Text Antarc* Antarctic Arctic Sea ice MDPI Open Access Publishing Arctic Antarctic The Antarctic Remote Sensing 12 22 3751 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring |
spellingShingle |
Delay-Doppler Map (DDM) Global Navigation Satellite System-Reflectometry (GNSS-R) decision tree random forest sea ice monitoring 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 |
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 |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12223751 |
op_coverage |
agris |
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_source |
Remote Sensing; Volume 12; Issue 22; Pages: 3751 |
op_relation |
https://dx.doi.org/10.3390/rs12223751 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
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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1774712847306063872 |