Full metadata record
DC Field | Value | Language |
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dc.citation.endPage | 7837 | - |
dc.citation.startPage | 7819 | - |
dc.citation.title | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | - |
dc.citation.volume | 16 | - |
dc.contributor.author | Sim, Seongmun | - |
dc.contributor.author | Im, Jungho | - |
dc.date.accessioned | 2023-12-21T11:46:12Z | - |
dc.date.available | 2023-12-21T11:46:12Z | - |
dc.date.created | 2023-10-04 | - |
dc.date.issued | 2023-08 | - |
dc.description.abstract | Ocean-fog is a type of fog that forms over the ocean and has a visibility of less than 1 km. Ocean-fog frequently causes incidents over oceanic and coastal regions; ocean-fog detection is required regardless of the time of day. Ocean-fog has distinct thermo-optical properties, and spatially and temporally extensive ocean-fog detection methods based on geostationary satellites are typically employed. Infrared (IR) channels of Himawari-8 were used to construct three machine-learning models for the continuous detection of ocean-fog. In contrast, visible channels are valid only during the daytime. As control models, we used fog products from the National Meteorological Satellite Center (NMSC) and machine-learning models trained by adding a visible channel. The extreme gradient boosting model utilizing IR channels corrected ocean-fog perfectly day and night, with the highest F1 score of 97.93% and a proportion correct (PC) of 98.59% throughout the day. In contrast, the NMSC product had a probability of detection of 87.14%, an F1 score of 93.13%, and a PC of 71.9%. As demonstrated by the qualitative evaluation, the NMSC product overestimates clouds over small and coarsely textured ocean-fog regions. In contrast, the proposed model distinguishes between ocean-fog, clear skies, and clouds at the pixel scale. The Shapley additive explanation analysis demonstrated that the difference between channels 14 and 7 was very useful for ocean-fog detection at night, and its extremely low values contributed significantly to distinguishing nonfog during the daytime. Channel 15, affected by water vapor absorption, contributed most to ocean-fog detection among atmospheric window channels. The research findings can be used to improve operational ocean-fog detection and forecasting. | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, v.16, pp.7819 - 7837 | - |
dc.identifier.doi | 10.1109/JSTARS.2023.3308041 | - |
dc.identifier.issn | 1939-1404 | - |
dc.identifier.scopusid | 2-s2.0-85168737105 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/65892 | - |
dc.identifier.wosid | 001063190700004 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Improved Ocean-Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology | - |
dc.relation.journalResearchArea | Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Himawari-8 | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | ocean-fog | - |
dc.subject.keywordAuthor | Shapley additive explanation (SHAP) | - |
dc.subject.keywordAuthor | whole-day | - |
dc.subject.keywordAuthor | extreme gradient boosting (XGB) | - |
dc.subject.keywordPlus | DIURNAL CYCLE | - |
dc.subject.keywordPlus | WATER-VAPOR | - |
dc.subject.keywordPlus | YELLOW SEA | - |
dc.subject.keywordPlus | TEMPERATURE | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | SUMMER | - |
dc.subject.keywordPlus | COVER | - |
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