1 November 2012 Estimation of aerosol type from airborne hyperspectral data: a new technique designed for industrial plume characterization
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The determination of the aerosol type in a plume from remotely sensed data without any a priori knowledge is a challenging task. If several methods have already been developed to characterize the aerosols from multi or hyperspectral data, they are not suited for industrial particles, which have specific physical and optical properties, changing quickly and in a complex way with the distance from the source emission. From radiative transfer equations, we have developed an algorithm, based on a Look-Up Table approach, enabling the determination of the type of this kind of particles from hyperspectral data. It consists in the selection of pixels pairs, located at the transitions between two kinds of grounds (or between an illuminated and a shadow area), then in the comparison between normalized estimated Aerosol Optical Thicknesses (AOTs) and pre-calculated AOTs. The application of this algorithm to simulated data leads to encouraging results: the selection of only six pixels pairs allows the algorithm to differentiate aerosols emitted by a metallurgical plant from biomass burning particles, urban aerosols and particles from an oil depot explosion, regardless the size and the aerosol concentration. The algorithm performances are better for a relatively high AOT but the single scattering approximation does not enable the characterization of thick plumes (AOT above 2.0). However, the choice of transitions (type of grounds) does not seem to significantly affect the results.
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A. Deschamps, A. Deschamps, R. Marion, R. Marion, P.-Y. Foucher, P.-Y. Foucher, X. Briottet, X. Briottet, } "Estimation of aerosol type from airborne hyperspectral data: a new technique designed for industrial plume characterization", Proc. SPIE 8534, Remote Sensing of Clouds and the Atmosphere XVII; and Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing VIII, 85340I (1 November 2012); doi: 10.1117/12.970616; https://doi.org/10.1117/12.970616

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