Cloud-Screening Algorithm for ENVISAT/MERIS Multispectral Images

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-1b dat...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2007-12, Vol.45 (12), p.4105-4118
Main Authors: Gomez-Chova, L., Camps-Valls, G., Calpe-Maravilla, J., Guanter, L., Moreno, J.
Format: Article
Language:English
Subjects:
Absorption
Applied geophysics
Brightness
Classification algorithms
Cloud screening
Clouds
Data mining
Earth sciences
Earth, ocean, space
Exact sciences and technology
Instruments
Internal geophysics
Medium Resolution Imaging Spectrometer (MERIS)
MERIS
Meteorology
multispectral images
Multispectral imaging
Robustness
Satellites
spectral unmixing
unsupervised classification
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Summary: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-1b 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 water-vapor 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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2007.905312