Chen Zuo

About

Chen Zuo is a leading researcher in satellite-based aerosol remote sensing, with a focus on distinguishing natural from anthropogenic aerosols through fine-mode aerosol optical depth (fAOD) and fine-mode fraction (FMF) retrievals. His most impactful contribution is the development of a global land daily FMF dataset (2001–2020) by synergizing physical models with deep learning approaches, published in 2022 and already cited 32 times—a testament to its rapid adoption by the atmospheric science community. This work directly addresses the long-standing challenge of unreliable satellite-based FMF products over land, enabling more accurate discrimination of human-caused aerosol pollution. Zuo further advanced the field by unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced monthly inversion data (2024, 17 citations), and by pioneering a spectral deconvolution algorithm to retrieve global fAOD from dual-angle satellite data for the 1990s (2023). His research fills critical gaps in historical aerosol records, providing essential data for climate modeling and environmental policy. Through these innovations, Zuo has become a key figure in leveraging machine learning and satellite technology to improve our understanding of aerosol impacts on climate and air quality.

Research Focus

Key Achievements

Top Papers

1. 1
   A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

   32 citations · 2022
2. 2
   Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data

   17 citations · 2024
3. 3
   Spectral Deconvolution Algorithm for Global Fine-Mode Aerosol Retrieval in the 1990s Using Dual-Angle Satellite Aerosol Data

   5 citations · 2023

Key Collaborators

Contact & Links