Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

Sahar Dehnavi,Klemen Zakšek,Vladimir Skripachev,Mohammad Javad Valadan Zoej,Gunther Seckmeyer,Yasser Maghsoudi

doi:10.3390/rs12030371

Abstract

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

Highlights

One of the most interesting features of Earth, as seen from space, is the ever-changing distribution of clouds
We investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0◦ E
In order to provide a single reference grid with a similar spatial resolution and projection, the Meteosat-8 (IODC) image was projected on the Meteosat-10 spatial grid

Summary

Introduction

One of the most interesting features of Earth, as seen from space, is the ever-changing distribution of clouds. They are as natural as anything we encounter in our daily lives. Clouds have widespread effects on the energy balance of the Earth and the atmosphere. They cause severe atmospheric changes in both the vertical and horizontal directions [2]. Depending on their characteristics and height in the atmosphere, clouds can influence the energy balance in different ways. What are clouds? Clouds are either optically thick entities that cover the surface, or semi-transparent if they are above homogeneous surface

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