• Spectroscopy and Spectral Analysis
  • Vol. 40, Issue 11, 3321 (2020)
Shu-tao WANG*, Gui-chuan WANG, Kun-kun FAN, Xing WU, and Yu-tian WANG
Author Affiliations
  • Institute of Electrical Engineering, Measurement Technology and Instrumentation Key of Hebei Province, Yanshan University, Qinhuangdao 066004, China
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    DOI: 10.3964/j.issn.1000-0593(2020)11-3321-07 Cite this Article
    Shu-tao WANG, Gui-chuan WANG, Kun-kun FAN, Xing WU, Yu-tian WANG. Inversion of Aerosol Optical Depth in the Beijing-Tianjin-Hebei Region Based on PSO Clustering Algorithm[J]. Spectroscopy and Spectral Analysis, 2020, 40(11): 3321 Copy Citation Text show less
    Algorithm flow diagram
    Fig. 1. Algorithm flow diagram
    Fit curve of red-blue band equivalent surface reflectivity
    Fig. 2. Fit curve of red-blue band equivalent surface reflectivity
    Effect of four aerosol types on apparent reflectivity
    Fig. 3. Effect of four aerosol types on apparent reflectivity
    Relationship between energy E and cluster number m
    Fig. 4. Relationship between energy E and cluster number m
    Volumetric spectral distribution of five aerosol types
    Fig. 5. Volumetric spectral distribution of five aerosol types
    (a) The birefringence index real part (a) and birefringence index imaginary part (b) of five types of aerosol
    Fig. 6. (a) The birefringence index real part (a) and birefringence index imaginary part (b) of five types of aerosol
    Inversion results and consistency of the aerosol optical depth of GF-4 satellite(a): Original image; (b): C1 aerosol; (c): C4 aerosol; (d): Continental aerosol;(e): MODIS aerosol products; (f) Analysis of C1 type aerosol inversion results;(g): Analysis of C4 type aerosol inversion result; (h): Analysis of continental aerosol inversion results
    Fig. 7. Inversion results and consistency of the aerosol optical depth of GF-4 satellite
    (a): Original image; (b): C1 aerosol; (c): C4 aerosol; (d): Continental aerosol;(e): MODIS aerosol products; (f) Analysis of C1 type aerosol inversion results;(g): Analysis of C4 type aerosol inversion result; (h): Analysis of continental aerosol inversion results
    谱段谱段范围
    /μm
    空间分
    辨率/m
    幅宽
    /km
    重访
    时间/s
    可见光
    近红外
    (VNIR)
    10.45~0.9040020
    20.45~0.52
    30.52~0.6050
    40.63~0.69
    50.76~0.90
    中波红外
    (MWIR)
    63.50~4.10400
    Table 1. Parameters of PMI sensor for GF-4 satellite
    Number of
    categories/m
    Maximum/ Minimum Inertiaweight
    (ωmax/ωmin)
    Learning factors
    (c1/c2)
    Number of
    trainings (T)
    80.9/0.41.2/1.2300
    Table 2. Parameters of PSO clustering algorithm
    TimeAODAbsolute Error
    AERONETC1C4ContinentalC1C4Continental
    0825-0.616 40.910 00.710 0---
    09040.120 00.411 00.390 00.386 00.291 00.270 00.266 0
    0909-0.230 00.360 00.330 0---
    09230.080 00.210 00.150 00.169 00.130 00.070 00.089 0
    10010.115 20.254 00.212 00.239 00.138 80.096 80.123 8
    10030.074 10.080 00.172 00.162 00.005 90.097 90.087 9
    10040.625 10.460 00.830 00.510 00.065 10.204 90.115 1
    Table 3. Comparison of inversion results of different aerosol types with AERONET ground-based data
    Shu-tao WANG, Gui-chuan WANG, Kun-kun FAN, Xing WU, Yu-tian WANG. Inversion of Aerosol Optical Depth in the Beijing-Tianjin-Hebei Region Based on PSO Clustering Algorithm[J]. Spectroscopy and Spectral Analysis, 2020, 40(11): 3321
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