Next Article in Journal
Topography and Three-Dimensional Structure Can Estimate Tree Diversity along a Tropical Elevational Gradient in Costa Rica
Next Article in Special Issue
Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data
Previous Article in Journal
Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region
Previous Article in Special Issue
Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(4), 628;

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

Faculty of Geography, Philipps-University of Marburg, 35032 Marburg, Germany
Author to whom correspondence should be addressed.
Received: 9 February 2018 / Revised: 26 March 2018 / Accepted: 16 April 2018 / Published: 18 April 2018
(This article belongs to the Special Issue Remote Sensing of Low-Level Liquid Water Clouds and Fog)
Full-Text   |   PDF [19866 KB, uploaded 3 May 2018]   |  


Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter. View Full-Text
Keywords: fog detection; climatology; remote sensing; Meteosat Second Generation; machine learning fog detection; climatology; remote sensing; Meteosat Second Generation; machine learning

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

  • Externally hosted supplementary file 1
    Doi: 10.5678/LCRS/DAT.311
    Description: Fog frequencies in Europe (monthly means)

Share & Cite This Article

MDPI and ACS Style

Egli, S.; Thies, B.; Bendix, J. A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data. Remote Sens. 2018, 10, 628.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top