About

Xingfa Gu is a prominent atmospheric scientist whose research centers on aerosol remote sensing, optical property retrieval, and satellite-based environmental monitoring, with a particular focus on East and Central Asia. His work has made substantial contributions to our understanding of aerosol dynamics, helping to quantify how particulates from sources such as biomass burning and dust storms affect climate and air quality. Gu has played a pivotal role in validating and inter-comparing major aerosol datasets, including MODIS, MISR, GOCART, OMI, and VIIRS products against ground-truth AERONET measurements — work that has accumulated over 200 citations collectively and established foundational benchmarks for satellite aerosol science over China. His innovative algorithm development stands out notably, including a high-spatial-resolution retrieval method using China's GF-1 satellite and an N-Dimensional Cost Function approach leveraging Himawari-8 geostationary data for multitemporal aerosol optical depth retrieval. His landmark biomass burning study, drawing on eighteen years of AERONET photometer records across seven global sites, has become an important reference for characterizing aerosol aging across diverse vegetation types. Collectively, Gu's research has meaningfully advanced satellite aerosol monitoring capabilities, supporting both climate science and pollution assessment efforts across the Asia-Pacific region.

Research Focus

Key Achievements

11
H-Index
20
Papers
450
Total Citations
23
Avg Citations/Paper
🏆 Most Cited Paper
The inter-comparison of MODIS, MISR and GOCART aerosol products against AERONET data over China
82 citations · 2012
📈 Most Prolific Year: 2011 (3 Papers)
🤝 Key Collaborators: 78
🏛 Institutions: Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Institute of Remote Sensing and Digital Earth, China National Space Administration, Beijing Normal University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago