); [EMAIL_ADDRESS] (L.J. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] (P.D. ); [EMAIL_ADDRESS] (C.Q. ); [EMAIL_ADDRESS] (L.J. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] (P.D. ); [EMAIL_ADDRESS] (C.Q. ); [EMAIL_ADDRESS] (L.J. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] (P.D. ); [EMAIL_ADDRESS] (C.Q. ); [EMAIL_ADDRESS] (L.J. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] (P.D. ); [EMAIL_ADDRESS] (C.Q. ); [EMAIL_ADDRESS] (L.J. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] (P.D. ); [EMAIL_ADDRESS] (C.Q. ); [EMAIL_ADDRESS] (L.J. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China; [EMAIL_ADDRESS] (P.D. ); [EMAIL_ADDRESS] (C.Q. ); s1701006@s.  
and U-Net deep learning method [6] were used for sea fog detection based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. An unsupervised learning algorithm was used for sea fog detection based on Communication, Ocean, and Meteorological Satellite (COMS) data [7], while a dual-satellite method was proposed by [8], which combined Himawari-8 and FY-4A satellite data to detect sea fog at dawn. The authors of [9] proposed a decision tree approach that combined Himawari-8 and Geostationary Ocean Color Imager (GOCI) satellite data to detect sea fog. However, ship navigation risk assessments require long-term and daily sea fog data, which limits existing sea fog detection methods based on multi-source remote sensing satellites or satellites with a long revisit period. Himawari-8 is a geostationary orbit satellite launched by Japan in 2014, which can provide stable remote sensing images at intervals of 10 min [10], but few sea fog detection studies based on individual Himawari-8 satellite data have been conducted. The authors of [11] proposed a normalized difference snow index (NDSI) threshold algorithm for sea fog detection, but they noted that distinguishing between low clouds and sea fog could be further improved. Therefore, establishing a sea fog identification method based on Himawari-8 satellite data, as well as deriving a navigation risk estimation model based on remote sensing data, are great challenges for both research fields. In navigation risk estimation research, weighting methods and analysis methods have been used to construct navigation risk assessment models. Some examples include the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) [12], formal safety assessment (FSA) [13], and Fuzzy reasoning [14], which were used to construct navigation risk estimation models. The authors of [15] analyzed navigation risks in the Arctic with a Bayesian network. According to these studies, due to a lack of objective data, experienced experts inevitably need to be used as a supplementary approach, leading to some subjective uncertainties in the estimations. Objective data (such as historical accident data) are considered to have more potential in providing unbiased risk information [16; 17; 18], such as being used as training data in a Bayesian network. Historical accident data and automatic identification system (AIS) data can be used instead of an experienced expert, which have further revealed potential links between different risk factors [19; 20]. However, apart from training Bayesian networks, objective data are rarely used in other navigational risk estimation research. In addition, these studies had a good analytical capability for specific ships and accidents, but were unable to reflect the spatial distribution and variation of risks.