{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T17:29:00Z","timestamp":1772126940833,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"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":["41976165"],"award-info":[{"award-number":["41976165"]}],"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":["FY-APP-2022.0610"],"award-info":[{"award-number":["FY-APP-2022.0610"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"pilot program of Fengyun satellite applications (2022)","award":["41976165"],"award-info":[{"award-number":["41976165"]}]},{"name":"pilot program of Fengyun satellite applications (2022)","award":["FY-APP-2022.0610"],"award-info":[{"award-number":["FY-APP-2022.0610"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Polar-orbiting satellites have been widely used for detecting sea fog because of their wide coverage and high spatial and spectral resolution. FengYun-3D (FY-3D) is a Chinese satellite that provides global sea fog observation. From January 2021 to October 2022, the backscatter and virtual file manager products from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) were used to label samples of different atmospheric conditions in FY-3D images, including clear sky, sea fog, low stratus, fog below low stratus, mid\u2013high-level clouds, and fog below the mid\u2013high-level clouds. A 13-dimensional feature matrix was constructed after extracting and analyzing the spectral and texture features of these samples. In order to detect daytime sea fog using a 13-dimensional feature matrix and CALIPSO sample labels, four supervised classification models were developed, including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Network. The accuracy of each model was evaluated and compared using a 10-fold cross-validation procedure. The study found that the SVM, KNN, and Neural Network performed equally well in identifying low stratus, with 85% to 86% probability of detection (POD). As well as identifying the basic components of sea fog, the SVM model demonstrated the highest POD (93.8%), while the KNN had the lowest POD (92.4%). The study concludes that the SVM, KNN, and Neural Network can effectively distinguish sea fog from low stratus. The models, however, were less effective at detecting sub-cloud fog, with only 11.6% POD for fog below low stratus, and 57.4% POD for fog below mid\u2013high-level clouds. In light of this, future research should focus on improving sub-cloud fog detection by considering cloud layers.<\/jats:p>","DOI":"10.3390\/rs15092283","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T01:28:28Z","timestamp":1682558908000},"page":"2283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Automatic Detection of Daytime Sea Fog Based on Supervised Classification Techniques for FY-3D Satellite"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-7302","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-3991","authenticated-orcid":false,"given":"Zhongfeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"given":"Dongzhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9002-3625","authenticated-orcid":false,"given":"Md. Arfan","family":"Ali","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center for West Ecological Safety (CIWES), Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"},{"name":"Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"given":"Chenyue","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"given":"Yuanzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China"}]},{"given":"Kuo","family":"Liao","sequence":"additional","affiliation":[{"name":"Fujian Institute of Meteorological Sciences, Fuzhou 350008, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.7780\/kjrs.2016.32.4.1","article-title":"Fundamental Research on Spring Season Daytime Sea Fog Detection Using MODIS in the Yellow Sea","volume":"32","author":"Jeon","year":"2016","journal-title":"Korean J. 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