Retrieval of global aerosol and surface properties from the Gaofen-5 Directional Polarimetric Camera measurements

Abstract

Multi-angle polarimetry has been recognized as the most effective configuration to retrieve aerosol parameters from space. In this study, we developed a numerical inversion algorithm that simultaneously retrieves aerosol optical depth (AOD), single scattering albedo (SSA), and land surface albedo (expressed as the Directional Hemispherical Reflectance, DHR) from multi-angle polarimetric observations of China's Directional Polarimetric Camera (DPC) onboard the Gaofen-5 satellite. As one of the few multi-angle polarimetric sensors in operation, DPC provides multi-spectral polarized measurements at up to 12 viewing angles, offering unique advantages for retrieving multiple aerosol parameters. With sensitivity experiments using the VLIDORT radiative transfer model, we first clarified that SSA retrieval with an uncertainty of 0.03 requires degree of linear polarization (DOLP) observation uncertainties below 0.01 with carefully designed viewing geometries. Subsequently, an optimization-based algorithm was implemented to minimize discrepancies between simulated and observed multi-angle scalar reflectance and DOLP. The algorithm performs well on the simulated dataset, with correlation coefficients of SSA (when AOD > 0.4) reaching 0.65. When laboratory calibration uncertainties representative of the DPC are introduced, retrieval performance degrades accordingly. Retrieval using DPC measurements and validation against AERONET observation also demonstrated robust performance. Retrieved AOD achieved a correlation coefficient of 0.75 with AERONET, comparable to operational satellite products such as those from MODIS. The correlation coefficient of SSA under high aerosol loading (AOD > 0.4) is approximately 0.3, matching the precision of Polarization and Directionality of the Earth’s Reflectances instrument (POLDER) SSA products, the previous best satellite-based SSA products. Regional and global results captured spatiotemporal aerosol variability of typical pollution events, including biomass burning plumes and dust transport tracks. The DHR results also align closely with MODIS-derived DHR (bias = 0.001). This work not only advances DPC's capability for comprehensive aerosol characterization globally, but also provides a physically interpretable framework for global aerosol and surface monitoring.