Yushan Guo
Papers
1
Total Citations
32
H-Index
1
About
Yushan Guo is a leading researcher in satellite remote sensing and atmospheric aerosol science, with a primary focus on distinguishing natural and anthropogenic aerosols through advanced data-driven methodologies. Their most influential work, "A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches" (2022), has garnered 32 citations and represents a landmark contribution to the field. Guo pioneered a novel hybrid framework that synergizes physical retrieval principles with deep learning techniques, overcoming long-standing limitations in satellite-based fine-mode fraction (FMF) products over land. This innovation produced the first reliable, high-resolution global daily FMF dataset spanning two decades, enabling researchers to more accurately discriminate anthropogenic aerosols from natural sources—a critical capability for climate modeling, air quality assessment, and policy-making. By addressing the persistent unreliability of previous satellite FMF retrievals, Guo’s work has significantly advanced our understanding of aerosol composition and its environmental impacts. Their research exemplifies the power of integrating physical models with machine learning to solve complex Earth observation challenges, establishing Guo as a key figure in modern aerosol remote sensing and environmental data science.
Research Focus
Key Achievements
Top Papers
- 1