Papers
2
Total Citations
14
H-Index
2
About
Lina Xu is a leading researcher in atmospheric remote sensing, specializing in the retrieval of aerosol microphysical properties using advanced machine learning techniques. Her work bridges the gap between traditional physics-based algorithms and modern data-driven methods, dramatically improving both the accuracy and efficiency of aerosol retrievals from satellite instruments. In her highly cited 2024 paper, Xu introduced a physics-informed deep learning approach for the Multi-angle Imaging SpectroRadiometer (MISR), overcoming the limitations of conventional lookup table methods to better characterize aerosol types and microphysics. Building on this, her 2025 study developed a robust, efficient multiangle polarimetric retrieval method that replaces time-consuming iterative calculations with a neural network, achieving comparable accuracy at a fraction of the computational cost. With over 14 citations across her most influential works, Xu’s contributions are shaping the next generation of satellite-based aerosol monitoring, enabling faster, more reliable data for climate modeling and air quality studies. Her innovative fusion of physical principles with deep learning marks her as a rising leader in the field.
Research Focus
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