## 5 Discussion  
In the navigation risk assessment framework proposed in this paper, we utilized a remote sensing sea fog identification method to obtain a large amount of accurate historical sea fog data in Bohai Sea and its surrounding waters. In the experiment, we found that the statistical results of sea fog are disturbed by low clouds, which could bring some abnormal statistic values (for instance, some areas with more than 200 days of sea fog in a year). In order to improve the classification accuracy, we used CALIPSO-FVM data to construct the sea fog sample set, and selected the features that contribute to the classification by spectral analysis. Compared with the NDSI threshold method in [11], the POD of proposed method decreased by 7.41%, but the ETS increased by 27.61%, which has a better distinguishing performance on sea fog and low clouds. It is very useful to have the historical frequency statistics of sea fog. The spatial distribution and seasonal differences of sea fog frequency in Bohai Sea and its surrounding waters are very obvious (Figure 10), the anomaly statistics disappear, and the results can be applied to the navigation risk assessment model. However, the proposed method still has some limitations. The small sample set size and unbalanced sample proportion are two factors that restrict the classification performance. The number of accurate sea fog samples obtained from CALIPSO-VFM data in Bohai Sea and Yellow Sea is limited, resulting in an unbalanced sample proportion (the sample ratio of sea fog to non-sea fog data is 1:3). It is necessary to expand the sample set size further. Observation data from weather ships or ground stations may be a viable option, which will be performed in a future study. In order to eliminate the subjectivity in the navigation risk assessment methods, we collected the ship accident data of the study area for many years, and used the objective weighting method to determine the weight of criteria, from the accident matching and sensitivity analysis results, it has achieved a good performance in Bohai Sea and its surrounding waters. There are still some limitations that could be ameliorated further. Firstly, the spatial resolution and data performance of datasets used in this paper are quite different (for instance, the resolution of wind data is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), but the resolution of the currents is 0.125\({}^{\circ}\)\(\times\) 0.125\({}^{\circ}\)). Some data need to be unified by interpolation, as it is expected that high-quality data could get the better risk-mapping results. Secondly, we significantly consider the navigation environment factors in the model, and some thresholds about ship factors follow the methods employed in previous research [21; 41]. In the analysis about the ship, even the human factors could be added in the model and the applicability of the model will get a better performance. In addition, with the development of geostationary orbit satellite and satellite constellation observation technology, ocean state observation data like wind, sea surface temperature, sea fog, etc. can be obtained in near real time. The historical or monitoring data of sea fog derived from Himawari-8 data have been integrated into a risk model of the proposed method; however, real-time sea fog observation data will be expected to be applied in risk assessment when the temporal resolution is further improved by satellite constellation. Using the real-time sea state observation data to evaluate and forecast navigation risks will greatly enhance the risk warning capability of the model, but it is a great challenge to develop a navigation risk forecast system. The dynamic Bayesian network could be a solution: for example, [PERSON] [47] used dynamic Bayesian network to predict the risk of current and future time. Although this study only considers some risk factors of the ship itself and does not make use of the observed data of the space marine environment, it may be a feasible method for the prediction of navigation risk.