Papers
5
Total Citations
211
H-Index
5
About
Chang-Keun Song is a leading figure in satellite-based aerosol remote sensing, with a research focus on developing and validating algorithms to retrieve aerosol optical properties from geostationary platforms. His most impactful contribution is the Yonsei Aerosol Retrieval (YAER) algorithm, which processes data from the Geostationary Ocean Color Imager (GOCI) to provide hourly aerosol optical depth (AOD) measurements over East Asia. This work, validated during the DRAGON-NE Asia 2012 campaign, has garnered over 128 citations, underscoring its significance in atmospheric science. Song’s research addresses critical challenges, such as masking bright surfaces and integrating data from multiple sensors like the Advanced Himawari Imager (AHI), to improve aerosol monitoring during major field campaigns including KORUS-AQ and EMeRGe. By enabling high-temporal-resolution observations, his algorithms support studies on air quality, climate forcing, and long-range pollutant transport. Song’s achievements have positioned him as a key contributor to geostationary aerosol science, with his work directly enhancing our ability to track atmospheric particles in near-real-time.
Research Focus
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