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A broad range of different of Bayesian cloud detection schemes is applied tomeasurements from the Medium Resolution Imaging Spectrometer (MERIS), theAdvanced Along-Track Scanning Radiometer (AATSR), and their combination. Thecloud detection schemes were designed to be numerically efficient and suitedfor the processing of large numbers of data. Results from the classical andnaive approach to Bayesian cloud masking are discussed for MERIS and AATSR aswell as for their combination. A sensitivity study on the resolution ofmultidimensional histograms, which were post-processed by Gaussian smoothing,shows how theoretically insufficient numbers of truth data can be used to setup accurate classical Bayesian cloud masks. Sets of exploited features fromsingle and derived channels are numerically optimized and results for naiveand classical Bayesian cloud masks are presented. The application of theBayesian approach is discussed in terms of reproducing existing algorithms,enhancing existing algorithms, increasing the robustness of existingalgorithms, and on setting up new classification schemes based on manuallyclassified scenes

Bayesian cloud detection for MERIS, AATSR, and their combination