Sub-grid physical optical flow for remote sensing of sandstorm

Satellite observations are currently of major importance in geosciences. Remote sensing is a strong tool to study atmospheric and earth phenomena. In this work, we propose a new motion estimation approach with application to dust storm tracking from remote sensing images. Dynamic natural phenomena in the atmosphere are generally turbulent due to a high Reynolds number. Meteorological images are still poor in time and space resolution compared to the turbulence characteristics of the flow. To tackle this problem, we define a new formulation of the flow equation based on a filtered scalar transport equation. Using Large Eddy Simulation theory, we propose a sub-grid model which incorporates small scale effects as missing (ie non-observed) information of remote sensing images. For day light changes, a uniform brightness variation term is incorporated to the model. We validated our approach on synthetic Direct Numerical Simulation (DNS) of scalar propagation. Promising results are obtained on real MTSAT-1R visible images of a dust storm event over Australia.