Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree

Abstract

Geostationary Ocean Color Imager (GOCI) observations are applied to marine fog (MF) detection in combination with Himawari-8 data based on the decision tree (DT) approach. Training and validation of the DT algorithm were conducted using match-ups between satellite observations and in situ visibility data for three Korean islands. Training using different sets of two satellite variables for fog and nonfog in 2016 finally results in an optimal algorithm that primarily uses the GOCI 412-nm Rayleigh-corrected reflectance (R-rc) and its spatial variability index. The algorithm suitably reflects the optical properties of fog by adopting lower R-rc and spatial variability levels, which results in a clear distinction from clouds. Then, cloud removal and fog edge detection in combination with Himawari-8 data enhance the performance of the algorithm, increasing the hit rate (HR) of 0.66 to 1.00 and slightly decreasing the false alarm rate (FAR) of 0.33 to 0.31 for the cloudless samples among the 2017 validation cases. Further evaluation of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data reveals the reliability of the GOCI MF algorithm under optically complex atmospheric conditions for classifying marine fog. Currently, the high-resolution (500 m) GOCI MF product is provided to decision-makers in governments and the public sector, which is beneficial to marine traffic management.