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

dc.contributor.authorZhu, Yongchao
dc.contributor.authorTao, Tingye
dc.contributor.authorLi, Jiangyang
dc.contributor.authorYu, Kegen
dc.contributor.authorWang, Lei
dc.contributor.authorQu, Xiaochuan
dc.contributor.authorLi, Shuiping
dc.contributor.authorSemmling, Maximilian
dc.contributor.authorWickert, Jens
dc.date.accessioned2021-12-13T12:46:19Z
dc.date.available2021-12-13T12:46:19Z
dc.date.issued2021-11-14
dc.date.updated2021-12-02T16:50:09Z
dc.description.abstractThe 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.en
dc.identifier.eissn2072-4292
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/14049
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12822
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherGNSS-Ren
dc.subject.otherDelay-Doppler Mapen
dc.subject.othermachine learningen
dc.subject.othersea ice classificationen
dc.subject.otherTDS-1en
dc.titleSpaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiersen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber4577en
dcterms.bibliographicCitation.doi10.3390/rs13224577en
dcterms.bibliographicCitation.issue22en
dcterms.bibliographicCitation.journaltitleRemote Sensingen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume13en
tub.accessrights.dnbfreeen
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Geodäsie und Geoinformationstechnik::FG GNSS-Fernerkundung, Navigation und Positionierungde
tub.affiliation.facultyFak. 6 Planen Bauen Umweltde
tub.affiliation.groupFG GNSS-Fernerkundung, Navigation und Positionierungde
tub.affiliation.instituteInst. Geodäsie und Geoinformationstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

Files

Original bundle
Now showing 1 - 1 of 1
Loading…
Thumbnail Image
Name:
remotesensing-13-04577-v2.pdf
Size:
57.83 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.86 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections