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Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Zhu, Yongchao and Tao, Tingye and Yu, Kegen and Qu, Xiaochuan and Li, Shuiping and Wickert, Jens and 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. ISSN 2072-4292.

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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.

Item URL in elib:https://elib.dlr.de/139427/
Document Type:Article
Title:Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, Yongchaoyczhu (at) hfut.edu.cnUNSPECIFIED
Tao, Tingyetaotingye (at) hfut.edu.cnUNSPECIFIED
Yu, Kegenkegen.yu (at) cumt.edu.cnUNSPECIFIED
Qu, Xiaochuanqqxxcc (at) hfut.edu.cnUNSPECIFIED
Li, Shuipinglishuiping (at) hfut.edu.cnUNSPECIFIED
Wickert, JensGeoForschungsZentrum Potsdamhttps://orcid.org/0000-0002-7379-5276
Semmling, MaximilianMaximilian.Semmling (at) dlr.dehttps://orcid.org/0000-0002-5228-8072
Date:November 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs12223751
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:Delay-Doppler Map (DDM); Global Navigation Satellite System-Reflectometry (GNSS-R); decision tree; random forest; sea ice monitoring
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Vorhaben Ionosphäre (old)
Location: Neustrelitz
Institutes and Institutions:Institute for Solar-Terrestrial Physics > Space Weather Observation
Deposited By: Semmling, Dr. Maximilian
Deposited On:25 Jan 2021 10:49
Last Modified:25 Jan 2021 10:49

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