An AI-based algorithm for retrieving aerosol optical depth and single scattering albedo using All-Sky Imager observations

Accurate measurement of aerosol optical properties is critical for understanding their radiative and environmental impacts. Currently, the most accurate retrieval of aerosol properties comes from the multi-channel surface sun photometer, but with relatively high cost and deployment/maintenance requirements. Here we develop a novel AI-based method for retrieving daytime aerosol optical parameters, namely aerosol optical depth (AOD) and single scattering albedo (SSA) using images acquired by All-Sky Imagers (ASI). Surface-based AOD and SSA retrievals from surface sun photometers are used as the training targets. Algorithm training and retrievals were performed for two sites in East China and Central US respectively. Independent validation against ground-based measurements demonstrated high consistency between the ASI-retrieved and sun photometer measured AOD and SSA, with Pearson correlation coefficients ( r ) exceeding 0.86 for AOD across all wavelengths at both sites and Root Mean Square Errors (RMSE) below 0.25. For SSA, r values reached 0.74 at the Beijing_PKU site and 0.84 at the SGP site, with RMSE remaining below 0.09 across all spectral channels, demonstrating the feasibility of simultaneous AOD and SSA retrieval from relatively low-cost ASI. These results demonstrate the feasibility of simultaneous AOD and SSA retrieval from ASI imagery and suggest that low-cost sky cameras may become a useful complement to sun-photometer networks after broader validation across additional environments, seasons, instruments, and aerosol types.