Sujung Go
Papers
10
Total Citations
240
H-Index
8
About
Sujung Go is a specialist in atmospheric remote sensing and aerosol science, with a particular focus on developing satellite-based retrieval algorithms for monitoring air quality across Asia. Her research centers on retrieving aerosol optical properties from geostationary and polar-orbiting satellite instruments, leveraging platforms such as the Geostationary Environment Monitoring Spectrometer (GEMS), the Ozone Monitoring Instrument (OMI), GOCI, and AHI. Go's most influential contribution is her development of an optimal estimation-based aerosol retrieval algorithm for GEMS — the world's first geostationary satellite instrument designed specifically for atmospheric air quality monitoring — a paper that has garnered 72 citations. She has further advanced the field through synergistic data fusion techniques, combining hyperspectral UV-visible and broadband imager datasets to improve aerosol optical depth accuracy, earning an additional 49 citations. Her work integrating multi-satellite aerosol products during major field campaigns such as KORUS-AQ and EMeRGe demonstrates real-world applicability, while her more recent application of deep neural networks to aerosol data fusion highlights her embrace of cutting-edge computational methods. Across her career, Go's contributions have meaningfully advanced Asia-focused air quality science and satellite algorithm development, making her a key figure in next-generation environmental monitoring research.
Research Focus
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