Efficient Multiangle Polarimetric Retrieval of Aerosols Using Data-Driven Deep Learning Method

Abstract

The multiangle polarimetric (MAP) measurement provides abundant information about aerosol microphysical properties, but its physical retrieval methods of aerosols usually rely on time-consuming optimal iterative calculations. This study introduces a robust and efficient MAP aerosol retrieval over eastern China based on a data-driven deep learning (DL) method. By directly training the function relationship between Polarization and Directionality of the Earth’s Reflectances (POLDER) measurements and matched aerosol products in typical Aerosol Robotic Network (AERONET) sites with the deep belief network (DBN) methods, aerosol optical depth (AOD), fine mode AOD (FAOD), coarse mode AOD (CAOD), and single scattering albedo (SSA) can be retrieved reliably. Ground validation shows very high accuracy for POLDER-3 DBN AOD ( ${R} = 0.917$ ) and FAOD ( ${R} = 0.942$ ) compared with AERONET results. Despite a decrease in retrieval accuracy, DBN CAOD and spectral SSA exhibit very consistent variations with ground inversions. In particular, POLDER-3 DBN retrievals over eastern China perform better than generalized retrieval of aerosol and surface properties (GRASP) products with optimized method. Our results demonstrate that DBN can well model the complex functional relationships between MAP measurements and aerosol optical/microphysical parameters. With the striking advantage in computational efficiency and modeling ability, the DL methods, such as DBN, have an enormous potential in operational aerosol retrieval of the emerging MAP satellite instruments.