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Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers

Zhu, Yongchao and Tao, Tingye and Li, Jiangyang and Yu, Kegen and Wang, Lei and Qu, Xiaochuan and Li, Shuiping and Semmling, Maximilian and 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. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/13/22/4577

Abstract

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.

Item URL in elib:https://elib.dlr.de/145661/
Document Type:Article
Title:Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, Yongchaoyczhu (at) hfut.edu.cnUNSPECIFIED
Tao, Tingyetaotingye (at) hfut.edu.cnUNSPECIFIED
Li, Jiangyang2019110581 (at) mail.hfut.edu.cnUNSPECIFIED
Yu, Kegenkegen.yu (at) cumt.edu.cnUNSPECIFIED
Wang, Leilei.wang (at) whu.edu.cnUNSPECIFIED
Qu, Xiaochuanqqxxcc (at) hfut.edu.cnUNSPECIFIED
Li, Shuipinglishuiping (at) hfut.edu.cnUNSPECIFIED
Semmling, MaximilianMaximilian.Semmling (at) dlr.dehttps://orcid.org/0000-0002-5228-8072
Wickert, JensGeoForschungsZentrum Potsdamhttps://orcid.org/0000-0002-7379-5276
Date:2021
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs13224577
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:GNSS-R; Delay-Doppler Map; machine learning; sea ice classification; TDS-1
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication, Navigation, Quantum Technology
DLR - Research area:Raumfahrt
DLR - Program:R KNQ - Communication, Navigation, Quantum Technology
DLR - Research theme (Project):R - Ionosphere
Location: Neustrelitz
Institutes and Institutions:Institute for Solar-Terrestrial Physics
Deposited By: Semmling, Dr. Maximilian
Deposited On:02 Dec 2021 07:13
Last Modified:02 Dec 2021 07:13

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