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    Area of Science:

    • Atmospheric Science
    • Remote Sensing
    • Environmental Monitoring

    Context:

    • China faces significant haze pollution events with high concentrations of fine particulate matter (PM2.5).
    • Existing satellite aerosol products, like NASA's MODIS, often misidentify haze, hindering accurate air pollution characterization.
    • Haze optical properties are not adequately retrieved by current MODIS cloud detection and dark target algorithms.

    Purpose:

    • To develop and validate a novel threshold algorithm for distinguishing haze from other atmospheric and surface features using MODIS data.
    • To improve the detection and characterization of haze pollution events over China using satellite remote sensing.
    • To enhance the capability of satellite-based monitoring for severe air pollution episodes.

    Summary:

    • The study analyzes spectral characteristics of cloud, fog, haze, and land cover.
    • A new threshold algorithm utilizing MODIS multi-band reflectance and brightness temperature is developed to detect haze.
    • The algorithm successfully identified haze distribution over the North China Plain, showing good agreement with true-color images and AERONET data, detecting approximately 80% of haze days.

    Impact:

    • Enhances the utility of MODIS satellite data for monitoring severe air pollution events, particularly haze.
    • Provides a validated method for detecting haze distribution, crucial for understanding its impact on public health and the environment.
    • Identifies potential improvements and error analyses for future haze detection algorithm development.