Aerosol single scattering albedo (SSA) measures the ratio of scattering to extinction, which is critical in determining aerosol radiative effect. However, spaceborne SSA retrieval is restricted by comprehensive monitoring requirements of both direct solar radiance and scattered sky radiance. Most existing passive satellite sensors such as MODIS and VIIRS only provide the measurements of reflected solar radiation at the top of the atmosphere (TOA), which are sensitive to both aerosol optical depth (AOD) and SSA. Current AOD products are commonly derived from satellites on the basis of assumed SSA. On the other hand, it would be possible to retrieve SSA using satellite measurements with known AOD. In this study, a machine learning approach is developed for the retrieval of SSA using joint visibility and satellite measurements. With meteorological and ancillary information, surface visibility can be converted to column AOD. Therefore, combining these variables representing AOD with MODIS measured TOA apparent reflectance, we retrieve SSA at over 2000 stations worldwide. The results show a great consistency with AERONET retrieved SSA. We also applied our method to surface PM2.5 measurements and obtained satisfactory results. Our work generates a global aerosol SSA dataset with extensive coverage over land, which can be used for the estimation of aerosol radiative forcing and the validation of climate models.

卫星反演,气溶胶光学特性,能见度观测