Yize Jiang
Papers
3
Total Citations
54
H-Index
3
About
Yize Jiang is an environmental data scientist whose research focuses on the intersection of satellite remote sensing, aerosol physics, and deep learning to unravel the global dynamics of atmospheric aerosols. Dr. Jiang’s primary contributions lie in developing innovative retrieval algorithms that distinguish fine-mode (anthropogenic) aerosols from coarse-mode (natural) aerosols using satellite observations. Their most cited work, “A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches” (32 citations), introduced the Phy-DL FMF dataset—a groundbreaking hybrid approach that synergizes physical models with deep learning to produce the first reliable, long-term global land aerosol fine-mode fraction record. This dataset has become a critical resource for discriminating human-caused pollution from natural dust and sea salt. Jiang further extended this capability with “Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020” (17 citations), which revealed decadal trends in aerosol composition. Their work on the “Spectral Deconvolution Algorithm for Global Fine-Mode Aerosol Retrieval in the 1990s” (5 citations) pushed the temporal frontier, enabling analysis of pre-2000 aerosol conditions using dual-angle satellite data. Collectively, Jiang’s research has significantly advanced our ability to monitor anthropogenic aerosol impacts on climate and air quality across decades.
Research Focus
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Top Papers
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