Xiao, Y.F.; Zhang, J., and Qin, P., 2019. An algorithm for daytime sea fog detection over the Greenland Sea based on MODIS and CALIOP data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 95-103. Coconut Creek (Florida), ISSN 0749-0208.
Sea fog is one of the dangerous weather disasters affecting scientific investigation and maritime transportation on the Arctic Ocean. In this paper, a detection algorithm for daytime sea fog over the Arctic Ocean is proposed taking the Greenland Sea as an example. With the assistance of satellite lidar data from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)/CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization), a large number of sample points of sea fog, low level clouds, mid-high level clouds, the sea surface, ice, and snow were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) images. The radiance (reflectance and emissivity) and texture (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) characteristics of each MODIS channel for sea fog and other features were analyzed. Thereafter, a new algorithm for sea fog detection is proposed. The step-by-step algorithm first masks the sea surface, sea ice, and snow using the reflectance of MODIS channels 2 and 7. Then, the sea fog is separated from low and medium-high level clouds using the homogeneity of channel 18. The validation compared with CALIOP data shows encouraging accuracy for sea fog detection with probabilities of detection (POD) ∼80 %, and probabilities of falsity (POF) ∼8.5 %.