Simultaneous CO2 and aerosol retrieval in a vegetation fire plume using aviris hyperspectral data

This work aims to evaluate the benefit of simultaneously estimating aerosol properties and gas abundances to characterize a plume. Methods estimating gaseous concentrations by hyperspectral imagery generally take into account the radiative impact of aerosols by using standard models. However, in particular when studying smoke plumes, particles can sensibly differ from standard aerosols and an approximate modeling of these particles may introduce a significant bias in the gaseous concentrations estimation. In this study, we combine two methods to simultaneously retrieve CO₂ concentration and aerosol optical properties in a smoke plume. The proposed framework is based on a two step algorithm. First, aerosol properties are retrieved using a Look-Up-Table (LUT) inversion approach. Then, CO₂ abundance within the plume is retrieved iteratively using a spline interpolation of the ground reflectance and a Levenberg-Marquardt algorithm to optimally fit the at-sensor radiance. We applied the proposed method to an AVIRIS image of the Quinault prescribed fire. We show that estimation of aerosol properties can improve CO₂ abundance retrieval inside a biomass burning plume and the robustness of the method to strong aerosol concentration.