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The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 \u03bcm and 116.59 \u03bcm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 \u03bcm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 \u03bcm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis.<\/jats:p>","DOI":"10.3390\/rs15225415","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T07:33:18Z","timestamp":1700292798000},"page":"5415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Combined Retrieval of Oil Film Thickness Using Hyperspectral and Thermal Infrared Remote Sensing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4582-1852","authenticated-orcid":false,"given":"Junfang","family":"Yang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yabin","family":"Hu","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongwei","family":"Li","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"},{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.isprsjprs.2018.09.017","article-title":"Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer","volume":"146","author":"Shi","year":"2018","journal-title":"ISPRS J. 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