Cloud-screening algorithm for ENVISAT/MERIS multispectral images

Journal article

This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-lb data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and watervapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-probability output is then combined with a spectral unmixing procedure to provide a cloud-abundance product instead of binary flags. The method is conceived to be robust and applicable to a broad range of actual situations with high variability of cloud types, presence of ground covers with bright and white spectra, and changing illumination conditions or observation geometry. The presented method has been shown to outperform the MERIS level-2 cloud flag in critical cloud-screening situations, such as over ice/snow covers and around cloud borders. The proposed modular methodology constitutes a general framework that can be applied to multispectral images acquired by spaceborne sensors working in the visible and near-infrared spectral range with proper spectral information to characterize atmospheric-oxygen and water-vapor absorptions. © 2007 IEEE.

Authors: Gomez-Chova, L; Camps-Valls, G; Calpe-Maravilla, J; Guanter, L; Moreno, J

• Publication status: Published
• Journal: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• Volume: 45
• Issue: 12
• Pages: 4105-4118
• Publication date: 2007-12-01
• DOI: 10.1109/TGRS.2007.905312
• EISSN: 1558-0644
• ISSN: 0196-2892
• Language: English
• Keywords: multispectral images; cloud screening; spectrometer (MERIS); unsupervised classification; medium resolution imaging; spectral unmixing