A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the areas using satellite observations. Many studies of recognition algorithms are based on observations from space-borne imagers, such as Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Radiometer Suite (VIIRS) and Advanced Himawari Imager (AHI). pixels frequently misidentified clouds official cloud detection products, these mainly focus recovering them clouds. There just few that provide more precise distinction between clear pixels. The Medium Spectrometer II (MERSI-II), imager aboard FY-3D satellite, has similar bands those MODIS, hence, it appears equivalent application potential. This study proposes novel MERSI mask (MHAM) algorithm directly categorize addition cloudy ones. is fact exhibit opposing visible channel reflectance infrared brightness temperature characteristics, relative brighter, well this, there positive difference their apparent values, at 0.865 μm 1.64 μm, respectively, over bright surfaces. Compared with Aqua/MODIS MERSI-II two datasets treat dense aerosol loadings certain clouds, possible pixels, they distinguished light or moderate certainly while capable demonstrating region’s boundary manner substantially consistent true color image. Using PM2.5 (particle matter diameter less than 2.5 μm) data monitored by national air quality monitoring stations test source, results indicated when ground-based ≥ 35 μg/cm3 considered be days, samples rate higher 85% accounted for 72.22% total samples. When 50 83.33% had an identification was 85%. A cross-comparison research methods showed method proposed this better sensitivity surface areas. will subsequent quantitative inversion further exert benefits instrument FY3D satellite.