Xiaobin Yin
Papers
1
Total Citations
11
H-Index
1
About
Xiaobin Yin is a leading figure in satellite oceanography, specializing in the retrieval of key biogeochemical parameters from Chinese ocean color sensors. Her primary research focuses on developing advanced deep learning algorithms, particularly residual networks (ResNets), to improve the accuracy of sea surface chlorophyll-a concentration estimates from HY-1C satellite data. In her most cited work (2023, 11 citations), she pioneered a ResNet model that dramatically enhances the retrieval of Chl-a from the COCTS instrument, overcoming limitations of traditional empirical algorithms in complex global waters. By training on 52 images spanning September 2018 to 2019, her approach demonstrated superior performance in capturing subtle spatial variability in phytoplankton biomass. This contribution is critical for monitoring marine primary productivity, assessing ecosystem health, and supporting China's expanding Earth observation capabilities. Her work bridges the gap between state-of-the-art machine learning and operational ocean color remote sensing, providing a robust framework for future satellite missions. Yin's research not only advances algorithmic methodology but also strengthens the utility of Chinese satellite data for global oceanographic studies.
Research Focus
Key Achievements
Top Papers
- 1