Chang-Suck Lee

National Institute of Environmental Research, Honam University

Papers

Total Citations

H-Index

About

Chang-Suck Lee is a leading figure in satellite remote sensing and environmental monitoring, with a focus on advancing atmospheric and surface parameter retrieval from geostationary platforms. His work bridges the gap between traditional physical models and modern machine learning, particularly in the estimation of aerosol optical depth (AOD). In his highly cited 2021 study, Lee pioneered a deep neural network approach to estimate hourly AOD from GOCI satellite data, overcoming the limitations of physical models in separating aerosol and surface reflectance over land. This work, garnering 35 citations, has significant implications for air quality monitoring and climate studies. Lee also contributed to the development of the land surface albedo algorithm for Korea’s next-generation GK-2A/AMI instrument, a key achievement for the Geo-KOMPSAT-2A satellite launched in 2018, which provides high-resolution geostationary data. Earlier in his career, Lee explored low-cost sensor systems for robotic map building, demonstrating versatility in his research. With a total of over 58 citations across his top papers, Lee’s work is essential for researchers in satellite meteorology, atmospheric science, and machine learning applications in Earth observation.