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A Bayesian approach to spaceborn hyperspectral optical flow estimation on dust aerosols

The significant role dust aerosols play in the earth's climate system and microbial nutrition cycles have lead to increased efforts of employing remote sensing to monitor their genesis, transport and deposition. This contribution extends earlier approaches of using Bayesian hierarchical models to extract dust activity from multi-spectral MSG-SEVIRI measurements by focusing on the signal-to-noise ratio with respect to post hoc motion analysis via optical flow. While interpreting also the latter in a completely Bayesian fashion, we show that our novel dust indication scheme reduces background noise and thereby renders the optical flow more decisive in terms of detecting even faint dust plumes. As a side effect of the indicators stability in case of dust absence, we point out the potential usage of its temporal variance to characterize dust at an early stage of the genesis and thus close to the corresponding source region.