Jilin Men
Papers
2
Total Citations
16
H-Index
2
About
Dr. Jilin Men is a leading figure in satellite oceanography, specializing in the development of advanced atmospheric correction (AC) algorithms for coastal and turbid waters. His primary research focuses on leveraging deep learning and neural networks to overcome the persistent challenge of accurately retrieving bio-optical parameters from geostationary ocean color imagers (GOCI). Dr. Men’s most impactful work, "Atmospheric correction under cloud edge effects for Geostationary Ocean Color Imager through deep learning" (2023, 12 citations), pioneers a novel solution for correcting data near cloud boundaries—a notoriously difficult problem that degrades satellite imagery. This builds on his foundational contribution, "A Neural Network-Based Atmospheric Correction Algorithm for GOCI Imagery Over Coastal Waters" (2023, 4 citations), which directly addresses the complex optical properties of coastal zones where standard AC methods fail. Since GOCI has provided eight daily observations since 2010, Dr. Men’s algorithms are critical for enabling high-frequency, accurate monitoring of coastal dynamics. His work represents a significant step forward in quantitative remote sensing, empowering researchers to study phytoplankton blooms, sediment transport, and water quality with unprecedented precision.
Research Focus
Key Achievements
Top Papers
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