Abstract
Abstract. A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.
Highlights
Cloud masking of Earth observation measurements is an important and often crucial part of various remote sensing retrievals
This paper emphasizes the application of Bayesian methods for the cloud masking of the complete 9.5 year time series of the Medium Resolution Imaging Spectrometer (MERIS) (Rast et al, 1999) and the Advanced Along-Track Scanning Radiometer (AATSR) (Llewellyn-Jones et al, 2001) on-board the Environmental Satellite (ENVISAT) and is part of the European Space Agency (ESA) Cloud CCI (Climate Change Initiative) project (Hollmann et al, 2013)
The application of the classical and naive Bayesian cloud masking technique to MERIS, AATSR, and their Synergy was discussed in detail
Summary
Cloud masking of Earth observation measurements is an important and often crucial part of various remote sensing retrievals This includes, but is not limited to, the retrieval of cloud and aerosol microphysical parameters, the estimation of cloud cover, ocean color retrievals, and in general, algorithms which include atmospheric correction schemes. A. Hollstein et al.: Bayesian cloud detection for MERIS, AATSR, and their combination gions and over snow- and ice-covered areas, and the distinction between clouds and optically thick aerosol plumes such as dust storms. Hollstein et al.: Bayesian cloud detection for MERIS, AATSR, and their combination gions and over snow- and ice-covered areas, and the distinction between clouds and optically thick aerosol plumes such as dust storms These points are discussed in more detail in Sect. The results presented here are computational highly efficient and are very well suited for the processing of large numbers of data, which makes these results very well suited for future application to the Ocean Land Colour Instrument (OLCI) (Nieke, 2008) and the Sea and Land Surface Temperature Radiometer (SLSTR) (Coppo et al, 2010) on-board the Sentinel-3 satellite (Miguel et al, 2007) and its operational follow-ups
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