{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:27:22Z","timestamp":1782970042540,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T00:00:00Z","timestamp":1636848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42104019"],"award-info":[{"award-number":["42104019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2108085QD176"],"award-info":[{"award-number":["2108085QD176"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open research fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote sensing, Wuhan University","award":["20P04"],"award-info":[{"award-number":["20P04"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["JZ2020HGTA0087"],"award-info":[{"award-number":["JZ2020HGTA0087"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology","award":["DLLJ202001"],"award-info":[{"award-number":["DLLJ202001"]}]},{"name":"Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University","award":["19-01-03"],"award-info":[{"award-number":["19-01-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs13224577","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7176-218X","authenticated-orcid":false,"given":"Yongchao","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Key Laboratory of Civil Engineering Structures and Materials, Hefei 230009, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingye","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Key Laboratory of Civil Engineering Structures and Materials, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangyang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Key Laboratory of Civil Engineering Structures and Materials, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7710-3073","authenticated-orcid":false,"given":"Kegen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1887-6222","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochuan","family":"Qu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Key Laboratory of Civil Engineering Structures and Materials, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1782-5792","authenticated-orcid":false,"given":"Shuiping","family":"Li","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Anhui Key Laboratory of Civil Engineering Structures and Materials, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Semmling","sequence":"additional","affiliation":[{"name":"German Aerospace Center DLR, Institute for Solar-Terrestrial Physics, 17235 Neustrelitz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Wickert","sequence":"additional","affiliation":[{"name":"German Research Center for Geosciences GFZ, 14473 Potsdam, Germany"},{"name":"Institute of Geodesy and Geoinformation Science, Technische Universit\u00e4t Berlin, 10623 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1038\/nature09051","article-title":"The central role of diminishing sea ice in recent Arctic temperature amplification","volume":"464","author":"Screen","year":"2010","journal-title":"Nature"},{"key":"ref_2","first-page":"1","article-title":"Classification of sea ice types in sentinel-1 SAR images","volume":"2019","author":"Park","year":"2019","journal-title":"Cryosphere Discuss"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.rse.2013.08.035","article-title":"Towards sea ice classification using simulated RADARSAT Constellation Mission compact polarimetric SAR imagery","volume":"140","author":"Dabboor","year":"2014","journal-title":"Remote Sens. 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