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Methods coming from statistics and pattern recognition to estimate the cloud mask from radiance measured by visible and infrared sensors onboard satellites are gaining greater and greater consideration for their ability to properly exploit the increasing number of channels available with current and next generation sensors. Endowed with physical arguments, they give rise to robust methods for accurately estimating the cloud mask. Application of such classification methods to MODIS data is discussed in this paper. Three different types of MODIS datasets are considered: synthetic (radiance is simulated by proper radiative transfer models); annotated (real MODIS data labeled by a meteorologist as clear or cloudy) and real MODIS data whose truth is obtained from the official MODIS cloud mask product. A full assessment of the MODIS spectral bands is performed, aimed either at understanding the role of the spectral bands in detecting clouds and at achieving top performance with just very few properly chosen spectral channels. Local methods which use spatial correlation of images to improve classification reducing the pseudo-nuisance of nonlocal methods, have also been tested on real data

Cloud Detection of MODIS Multispectral Images