Jong‐Min Yeom

About

Jong‐Min Yeom is a leading figure in satellite remote sensing, specializing in the retrieval of critical land and atmospheric parameters from geostationary platforms. His research focuses on advancing algorithms for aerosol optical depth (AOD) and land surface albedo, leveraging both physical models and cutting-edge machine learning techniques. In a landmark 2021 study, Yeom developed a novel deep neural network approach to estimate hourly AOD from GOCI satellite data, overcoming the limitations of traditional physical models in separating aerosol and surface reflectance over land—a work that has garnered 35 citations for its methodological impact. He also played a key role in the development of the land surface albedo algorithm for South Korea’s next-generation Geo-KOMPSAT-2A (GK-2A) satellite, launched in 2018, which now provides high-resolution (0.5–2 km) observations from geostationary orbit. This 2020 contribution, with 18 citations, underscores his expertise in operationalizing satellite products for climate and environmental monitoring. Yeom’s work bridges the gap between advanced computation and practical Earth observation, making him a pivotal contributor to the evolving capabilities of geostationary remote sensing.

Research Focus

Key Achievements

Top Papers

1. 1
   Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models

   35 citations · 2021
2. 2
   Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

   18 citations · 2020

Key Collaborators

Contact & Links