Automatic Detection of Daytime Sea Fog Based on Supervised Classification Techniques for FY-3D Satellite

Abstract

Polar-orbiting satellites have been widely used for detecting sea fog because of their wide coverage and high spatial and spectral resolution. FengYun-3D (FY-3D) is a Chinese satellite that provides global sea fog observation. From January 2021 to October 2022, the backscatter and virtual file manager products from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) were used to label samples of different atmospheric conditions in FY-3D images, including clear sky, sea fog, low stratus, fog below low stratus, mid–high-level clouds, and fog below the mid–high-level clouds. A 13-dimensional feature matrix was constructed after extracting and analyzing the spectral and texture features of these samples. In order to detect daytime sea fog using a 13-dimensional feature matrix and CALIPSO sample labels, four supervised classification models were developed, including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Network. The accuracy of each model was evaluated and compared using a 10-fold cross-validation procedure. The study found that the SVM, KNN, and Neural Network performed equally well in identifying low stratus, with 85% to 86% probability of detection (POD). As well as identifying the basic components of sea fog, the SVM model demonstrated the highest POD (93.8%), while the KNN had the lowest POD (92.4%). The study concludes that the SVM, KNN, and Neural Network can effectively distinguish sea fog from low stratus. The models, however, were less effective at detecting sub-cloud fog, with only 11.6% POD for fog below low stratus, and 57.4% POD for fog below mid–high-level clouds. In light of this, future research should focus on improving sub-cloud fog detection by considering cloud layers.

A Comparative Investigation of Light Scattering and Digital Holographic Imaging to Measure Liquid Phase Cloud Droplets

“…Ground-based and spaceborne remote sensing techniques have been developed considerably over the past few decades. In remote sensing methods, power spectrum data measured by satellites and radars is used to invert to obtain cloud microphysics parameters [21][22][23][24][25][26][27][28][29][30]. Remote sensing methods have the advantage of detecting high-altitude clouds from a long distance [31,32].…”