Papers
9
Total Citations
379
H-Index
8
About
Kaitao Li is a leading environmental data scientist whose research focuses on atmospheric aerosol characterization, satellite remote sensing, and big-data-driven air quality analytics. His most influential contribution is the development of LGHAP, the Long-term Gap-free High-resolution Air Pollutant concentration dataset, created through a novel tensor-flow-based multimodal data fusion framework. This work, cited over 140 times, provides a critical resource for environmental management and Earth system science by generating seamless, high-resolution air pollution records. Li has also made pioneering advances in aerosol remote sensing, including methods to calculate Stokes parameters and polarization angles from CIMEL sky radiometers, and algorithms to retrieve aerosol fine-mode fraction from both satellite (PARASOL, POLDER-3) and ground-based measurements. His research extends to characterizing aerosol optical, microphysical, and radiative properties across diverse regions—from high-aerosol-load Arctic events to typical sites in China’s SONET network. With cumulative citations exceeding 375 across his top papers, Li’s work bridges cutting-edge computational techniques with fundamental atmospheric science, enabling more accurate pollution monitoring and climate impact assessments. His achievements include developing neural-network-based aerosol optical depth retrievals for China’s HJ-2 satellite series and advancing calibration methods for sunphotometers.
Research Focus
Key Achievements
Top Papers
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