A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has developed a new method combining deep learning with physical radiative transfer modeling to improve the retrieval of atmospheric aerosol properties from complex satellite observations, supporting high-resolution, near-real-time monitoring of haze and dust events. The study was recently published in Journal of Remote Sensing.