Papers
14
Total Citations
461
H-Index
12
About
Xing Yan is a leading figure in satellite-based aerosol remote sensing, whose work has fundamentally advanced our understanding of atmospheric aerosols and their impacts on air quality and climate. Her research focuses on developing and refining algorithms to retrieve aerosol properties—particularly fine-mode fraction (FMF) and aerosol optical depth (AOD)—from satellite data, with a strong emphasis on improving accuracy over complex land surfaces. Yan’s major contributions include the creation of a globally robust, high-resolution aerosol retrieval algorithm for MODIS images, which has been critical for applications in regions like Eastern China. She pioneered a hybrid physical and deep learning approach to produce a global land daily FMF dataset (2001–2020), a breakthrough for distinguishing natural from anthropogenic aerosols. Her work on satellite-based PM2.5 estimation using fine-mode AOD has directly informed air quality monitoring. With over 400 citations across her most-cited papers, Yan’s impact is evident. Notably, her studies on aerosol properties during Australian fire events and her improved global anthropogenic aerosol product have provided essential data for climate and health research. Her innovative algorithms and datasets are now foundational tools for the atmospheric science community.
Research Focus
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