Next Article in Journal
Generating High Resolution LAI Based on a Modified FSDAF Model
Previous Article in Journal
Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis
Open AccessArticle

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

1
Korea Ocean Satellite Center (KOSC), Korea Institute of Ocean Science and Technology (KIOST), 385, Haeyang-ro, Yeongdo-gu, Busan 49111, Korea
2
Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 149; https://doi.org/10.3390/rs12010149
Received: 1 November 2019 / Revised: 22 December 2019 / Accepted: 27 December 2019 / Published: 2 January 2020
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 (Rrc) and its spatial variability index. The algorithm suitably reflects the optical properties of fog by adopting lower Rrc 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. View Full-Text
Keywords: GOCI; marine fog; machine learning; decision tree GOCI; marine fog; machine learning; decision tree
Show Figures

Graphical abstract

MDPI and ACS Style

Kim, D.; Park, M.-S.; Park, Y.-J.; Kim, W. Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree. Remote Sens. 2020, 12, 149. https://doi.org/10.3390/rs12010149

AMA Style

Kim D, Park M-S, Park Y-J, Kim W. Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree. Remote Sensing. 2020; 12(1):149. https://doi.org/10.3390/rs12010149

Chicago/Turabian Style

Kim, Donghee; Park, Myung-Sook; Park, Young-Je; Kim, Wonkook. 2020. "Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree" Remote Sens. 12, no. 1: 149. https://doi.org/10.3390/rs12010149

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop