Fast algorithm for retrieval of the atmospheric fine particulate matter maps from the multispectral satellite images
We describe a new algorithm for retrieving the atmospheric fine particulate matter total column (particles less than 1.0 and 2.5 μm) from multi-spectral satellite images in visible and IR regions of the electromagnetic spectrum. The algorithm is based on the regression relations between the top of atmosphere (TOA) reflectance, microphysical parameters of aerosol, and geometrical parameters of satellite scene. The regression equations are derived from the TOA reflectance calculations in the spectral channels of the satellite instrument for the ensemble of random generated parameters of the atmospheric radiative transfer model and the geometrical parameters of the satellite scene. Subsequently, this allows real-time mapping the fine particulate matter pollutions directly from the satellite images without solving ill-posed inverse problems of the solar radiation transfer in the atmosphere and aerosol light scattering. The proposed algorithm is implemented and tested for MERIS (Medium Resolution Imaging Spectrometer) satellite instrument. The comparison of the MERIS-retrieved total fine particulate matter content in the atmosphere with AERONET (Aerosol Robotic Network) data shows the standard deviation ~ 0.5 μg/cm2. The application of the developed algorithm to real-time monitoring of the regional and transboundary transport of the atmospheric particulate matter pollutants during the wildfires is demonstrated.