Chul Han Song
Papers
4
Total Citations
342
H-Index
4
About
Chul Han Song is a leading figure in satellite-based aerosol remote sensing, with a career dedicated to advancing our understanding of atmospheric aerosols over East Asia. His primary research focuses on developing and refining algorithms to retrieve aerosol optical properties from geostationary satellites, most notably through his work with the Geostationary Ocean Color Imager (GOCI). Song’s major contribution is the creation of the Yonsei Aerosol Retrieval (YAER) algorithm, which has evolved through two major versions. The YAER algorithm enables the hourly monitoring of aerosol optical depth (AOD) and related properties, providing unprecedented temporal resolution for studying air pollution transport and dynamics. His work has had a profound impact, with his seminal 2018 paper on the YAER version 2 algorithm accumulating over 147 citations, and his foundational 2016 validation study garnering 128 citations. Beyond algorithm development, Song has also bridged satellite data with ground-level air quality, as demonstrated by his 2015 study estimating PM₁₀ concentrations over Seoul using AERONET and MODIS data. His research is instrumental for environmental monitoring and public health, making him a key contributor to the field of atmospheric science.
Research Focus
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Top Papers
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