Sea Fog Top Height Retrieval over the Yellow Sea andBohai Sea Using Island Elevation

Sea fog has significant impacts on both human activities and the natural environment. Sea fog top height (SFTH) reflects the impact of sea fog on vertical space and is a crucial parameter for both Numerical Weather Prediction models and the estimation of sea fog dissipation. The existing SFTH retrieval methods based on spaceborne passive radiometers are prone to significant errors. We focus on the Yellow and Bohai Seas region and introduce a high-precision SFTH retrieval algorithm based on the peak elevation (PE) of islands in the area. In remote sensing images, islands of different PE within the sea fog regions exhibit two distinct appearances either visible or obscured by the fog. Thus, islands are automatically classified into two categories by support vector machine. The relationship between SFTH and the corresponding sea fog reflectance (SFR) in remote sensing images is established by defining a linear decision boundary in the SFR-PE space through logistic regression or support vector machine to effectively separate the two island categories. Validation experiments on twelve sea fog cases show that the proposed method exhibits higher accuracy compared to the MODIS cloud top height product and demonstrates good agreement with CALIPSO data.