Yongzhen Fan

Papers

Total Citations

153

H-Index

About

Yongzhen Fan is a leading researcher in ocean optics and satellite remote sensing, with a primary focus on advancing atmospheric correction methods for coastal and inland waters. His most notable contribution is the development of a multilayer neural network approach for atmospheric correction over optically complex coastal waters, detailed in his highly cited 2017 paper (153 citations). This work addresses a critical challenge in remote sensing—accurately retrieving water-leaving radiances in turbid, shallow, and highly absorbing environments where traditional algorithms fail. By leveraging machine learning, Fan’s method significantly improves the retrieval of ocean color data, enabling more reliable monitoring of water quality, phytoplankton dynamics, and coastal ecosystem health. His research bridges the gap between atmospheric physics and data-driven modeling, offering practical solutions for satellite missions like MODIS and VIIRS. With an impact extending to climate studies and environmental management, Fan’s work has become a cornerstone for researchers tackling the complexities of coastal ocean color remote sensing. His innovative integration of neural networks into atmospheric correction continues to inspire new approaches in the field.