Hybrid AI-physics method developed for accurate aerosol remote sensing
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, s...
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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.
Originally published at Phys.org
