{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T21:10:53Z","timestamp":1722460253753},"reference-count":59,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,14]],"date-time":"2020-11-14T00:00:00Z","timestamp":1605312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["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","19-01-03","1808085MD105"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871313"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"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.<\/jats:p>","DOI":"10.3390\/rs12223751","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T02:48:52Z","timestamp":1605581332000},"page":"3751","source":"Crossref","is-referenced-by-count":10,"title":["Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"http:\/\/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":"Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang 330013, China"}]},{"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"}]},{"given":"Kegen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"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"}]},{"ORCID":"http:\/\/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"}]},{"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"}]},{"given":"Maximilian","family":"Semmling","sequence":"additional","affiliation":[{"name":"German Aerospace Center DLR, Institute for Solar-Terrestrial Physics, 17235 Neustrelitz, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,14]]},"reference":[{"key":"ref_1","first-page":"1334","article-title":"The central role of diminishing sea ice in recent Arctic temperature amplification","volume":"464","author":"Screen","year":"2010","journal-title":"Nat. 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