About

Linlu Mei is a remote sensing scientist whose research centers on satellite-based aerosol retrieval, atmospheric optics, and environmental monitoring across diverse and challenging surface conditions. With a career spanning over a decade, Mei has made significant contributions to the development of algorithms that extract aerosol optical depth (AOD) and aerosol optical thickness (AOT) from a wide range of satellite platforms, including MERIS, MSG/SEVIRI, MODIS, AATSR, and geostationary satellites such as GOES-16 and Himawari-8. Among his most recognized achievements is pioneering retrieval techniques over notoriously difficult surfaces, particularly Arctic snow- and ice-covered regions, where conventional passive remote sensing methods struggle due to high surface reflectivity. His 2020 study on AOT retrieval in Arctic snow regions (60 citations) and earlier work on AATSR snow retrievals reflect sustained dedication to high-latitude atmospheric science. Mei has also contributed to global aerosol monitoring through multi-satellite fusion datasets providing hourly, near-global coverage. His work documenting agricultural biomass burning events in China demonstrates real-world environmental applications of these techniques. With a cumulative citation count exceeding 440, Mei's methodological innovations have meaningfully advanced the scientific community's capacity to monitor aerosols globally under previously intractable conditions.

Research Focus

Key Achievements

15
H-Index
29
Papers
647
Total Citations
22
Avg Citations/Paper
🏆 Most Cited Paper
Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results
92 citations · 2016
📈 Most Prolific Year: 2011 (7 Papers)
🤝 Key Collaborators: 104
🏛 Institutions: University of Bremen, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing Normal University, University of Chinese Academy of Sciences

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago