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

Author:

Wang Yu1ORCID,Qiu Zhongfeng1ORCID,Zhao Dongzhi1,Ali Md. Arfan23ORCID,Hu Chenyue1,Zhang Yuanzhi1,Liao Kuo4

Affiliation:

1. School of Marine Sciences (SMS), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China

2. Collaborative Innovation Center for West Ecological Safety (CIWES), Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China

3. Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia

4. Fujian Institute of Meteorological Sciences, Fuzhou 350008, China

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.

Funder

National Natural Science Foundation of China

pilot program of Fengyun satellite applications

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference29 articles.

1. Fundamental Research on Spring Season Daytime Sea Fog Detection Using MODIS in the Yellow Sea;Jeon;Korean J. Remote Sens.,2016

2. Jeon, H., Kim, S., Edwin, J., and Yang, C.-S. (2020). Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models. Electronics, 9.

3. Seasonal Variations of Yellow Sea Fog: Observations and Mechanisms;Zhang;J. Clim.,2009

4. Bendix, J., Cermak, J., and Thies, B. (2004). New Perspectives in Remote Sensing of Fog and Low Stratus—TERRA/AQUA-MODIS and MSG, Available online: https://www.researchgate.net/publication/228860444_New_perspectives_in_remote_sensing_of_fog_and_low_stratus-TERRAAQUA-MODIS_and_MSG.

5. A Comprehensive Dynamic Threshold Algorithm for Daytime Sea Fog Retrieval over the Chinese Adjacent Seas;Zhang;Pure Appl. Geophys.,2013

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