Papers
8
Total Citations
239
H-Index
6
About
Xingfeng Chen is a leading figure in satellite-based aerosol remote sensing, whose work has fundamentally advanced our ability to monitor atmospheric particles from space. His primary research focuses on developing and validating algorithms for retrieving aerosol optical depth (AOD) and aerosol types using geostationary satellites, a domain where he has made transformative contributions. Chen pioneered the application of artificial neural networks for joint retrieval of aerosol fine mode fraction and optical depth from MODIS data over China, a study that has garnered over 100 citations and become a benchmark in the field. He has been instrumental in adapting the Dark Target method for use with Himawari-8/AHI, enabling high-temporal-resolution aerosol monitoring across Asia, and has extended this capability to China’s Gaofen-4 and Fengyun-4A satellites, achieving unprecedented spatiotemporal resolution. His work on Arctic aerosol properties using AERONET data has provided critical insights into high-latitude atmospheric composition. Chen’s research bridges cutting-edge machine learning with operational satellite systems, producing tools that are widely used for air quality and climate studies. With multiple highly cited papers and a consistent focus on advancing geostationary aerosol retrieval, he stands as a key innovator in environmental remote sensing.
Research Focus
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Top Papers
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- 8Lidar SLAM algorithm based on bag of words loop detection1 citations · 2024