{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:44:52Z","timestamp":1782377092913,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"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":["42176175"],"award-info":[{"award-number":["42176175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101443"],"award-info":[{"award-number":["42101443"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271335"],"award-info":[{"award-number":["42271335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sea ice, as an important component of the Earth\u2019s ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model\u2019s generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.<\/jats:p>","DOI":"10.3390\/s24092836","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T10:33:36Z","timestamp":1714386816000},"page":"2836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies\u2014Sea Ice Thickness Inversion in Beaufort Sea"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0682-9157","authenticated-orcid":false,"given":"Yanling","family":"Han","sequence":"first","affiliation":[{"name":"Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6327-7726","authenticated-orcid":false,"given":"Zhenling","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-9808","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Key Laboratory of Fisheries Information, Ministry of Agriculture, College of Information, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"ref_1","unstructured":"Wadhams, P. (2000). Ice in the Ocean, CRC Press. [1st ed.]."},{"key":"ref_2","first-page":"351","article-title":"Advances in Sea lce Concentration Retrieval Based on Satellite Remote Sensing","volume":"40","author":"Xie","year":"2022","journal-title":"Adv. Mar. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1080\/15481603.2021.1943213","article-title":"Retrieval of daily sea ice thickness from AMSR2 passive microwave data using ensemble convolutional neural networks","volume":"58","author":"Chi","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12453","DOI":"10.1029\/93JC00939","article-title":"Passive microwave remote sensing of thin sea ice using principal component analysis","volume":"98","author":"Wensnahan","year":"1993","journal-title":"J. Geophys. Res."},{"key":"ref_5","first-page":"1467","article-title":"Comparison of Sea Ice Thickness Retrieval Algorithms from CryoSat-2 Satellite Altimeter Data","volume":"40","author":"Ji","year":"2015","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2014.2365215","article-title":"SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data","volume":"8","author":"Liu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","unstructured":"Karvonen, J., Simila, M., Hallikainen, M., and Haas, C. (2005, January 29). Estimation of equivalent deformed ice thickness from Baltic Sea ice SAR imagery. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Republic of Korea."},{"key":"ref_8","unstructured":"Sanden, J., and Drouin, H. (2011, January 18\u201322). Satellite SAR Observations of River Ice Cover: A RADARSAT-2 (C-band) and ALOS PALSAR (L-band) Comparison. Proceedings of the 16th Workshop on River Ice, Winnipeg, MB, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3189\/172756406781811420","article-title":"Sea-Ice thickness retrieval in the Sea of Okhotsk using dual-polarization SAR data","volume":"44","author":"Nakamura","year":"2006","journal-title":"Ann. Glaciol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2008.2011061","article-title":"Observation of Sea-Ice Thickness Using ENVISAT Data from Lutzow-Holm Bay, East Antarctica","volume":"6","author":"Nakamura","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/TGRS.2011.2160070","article-title":"Characterization of Arctic Sea Ice Thickness Using High-Resolution Spaceborne Polarimetric SAR Data","volume":"50","author":"Kim","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/36.718862","article-title":"Thin saline ice thickness retrieval using time-series C-band polarimetric radar measurements","volume":"36","author":"Shih","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2020JC016371","DOI":"10.1029\/2020JC016371","article-title":"Physical Properties of Summer Sea Ice in the Pacific Sector of the Arctic during 2008\u20132018","volume":"125","author":"Wang","year":"2020","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_14","unstructured":"Krishfield, R., and Proshutinsky, A. (2006). Bgos uls Data Processing Procedure, Woods Hole Oceanographic Institution."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rothrock, D.A., Percival, D.B., and Wenshanan, M. (2008). The decline in arctic sea-ice thickness: Separating the spatial, annual, and interannual variability in a quarter century of submarine data. J. Geophys. Res. Ocean., 113.","DOI":"10.1029\/2007JC004252"},{"key":"ref_16","unstructured":"Shi, L., Karvonen, J., and Cheng, B. (2014, January 13\u201318). Sea ice thickness retrieval from SAR imagery over Bohai sea. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2016.03.003","article-title":"Detection of melt onset over the northern Canadian Arctic Archipelago sea ice from RADARSAT, 1997\u20132014","volume":"178","author":"Mahmud","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"42","article-title":"Relationship of localincidence angle with satellite radar backscatter for different surface conditions","volume":"24","author":"Ogrady","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1515\/geo-2016-0029","article-title":"Incidence angle Normalization of Wide Swath SAR Data for Oceanographic Applications","volume":"8","author":"Topouzelis","year":"2016","journal-title":"Open Geosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100","DOI":"10.5670\/oceanog.2013.33","article-title":"Sea Ice Monitoring by Synthetic Aperture Radar","volume":"26","author":"Dierking","year":"2013","journal-title":"Oceanography"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"737","DOI":"10.5194\/tc-17-737-2023","article-title":"The effects of surface roughness on the calculated, spectral, conical-conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice","volume":"17","author":"Lamare","year":"2023","journal-title":"Cryosphere"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.5194\/tc-11-1607-2017","article-title":"A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data","volume":"11","author":"Ricker","year":"2017","journal-title":"Cryosphere"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"026016","DOI":"10.1117\/1.JRS.12.026016","article-title":"Combining active learning and transductive support vector machines for sea ice detection","volume":"12","author":"Han","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.5194\/tc-14-2629-2020","article-title":"Classification of Sea Ice Types in Sentinel\u20131 Synthetic Aperture Radar Images","volume":"14","author":"Park","year":"2020","journal-title":"Cryosphere"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2836\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:36:13Z","timestamp":1760106973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092836"],"URL":"https:\/\/doi.org\/10.3390\/s24092836","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,29]]}}}