Acta Optica Sinica, Volume. 44, Issue 18, 1801012(2024)

Fast Retrieval Method of Atmospheric CO2 Based on GF-5 Satellite Remote Sensing Data

Zhiqiang Sun1,2, Xianhua Wang2, Hanhan Ye2、*, Chao Li2,3, Yuan An2,3, Erchang Sun2,3, Shichao Wu2, and Hailiang Shi2
Author Affiliations
  • 1Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, Anhui , China
  • 2Key Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui , China
  • 3University of Science and Technology of China, Hefei 230026, Anhui , China
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    Figures & Tables(9)
    Calculation process of atmospheric XCO2 fast retrieval method
    Absorption cross section changes caused by temperature and pressure changes in the CO2-2 band at 2.050 μm. (a)(c) Comparison diagrams of pressure and temperature changes; (b)(d) temperature and pressure change curves corresponding to the peak and peak valley band sampling points
    Aerosol optical depth profiles and Gaussian fitting results of five aerosol types
    Comparison deviation diagrams of XCO2 results between GMI original and improved retrieval algorithms. (a) Error; (b) correlation
    Comparison of retrieval results between the original and improved algorithms of GMI and TCCON. (a) XCO2; (b) error
    • Table 1. GMI main instrument parameters

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      Table 1. GMI main instrument parameters

      ParameterTechnical indicator
      O2CO2-1CO2-2
      Spectral range /μm0.759-0.7691.568-1.5832.043-2.058
      Spectral resolution /cm-10.60.27
      Signal-to-noise ratio300250
      Radiometric calibrationAbsolute precision is 5% Relative precision is 2%
    • Table 2. Average interpolation error of absorption cross section table in three bands

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      Table 2. Average interpolation error of absorption cross section table in three bands

      Bandrange /cm-1Pressure interpolation error /%Temperature interpolation error /%Wavenumber interpolation error /%
      13000-131800.980.680.06
      6300-64000.510.890.02
      4800-49000.640.740.09
    • Table 3. Weights of subtypes and typical relative humidity for five aerosol types

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      Table 3. Weights of subtypes and typical relative humidity for five aerosol types

      Aerosol typeSubtypeDensity weight /%Relative humidity /%
      Black carbonHydrophilic, hydrophobic78.4, 21.653.9
      Sea saltBin 001, 002, 003, 004, 0050.5, 3.2, 19.1, 59.9, 17.388.3
      Organic carbonHydrophilic, hydrophobic82.2, 17.878.4
      Sulphate aerosolSO10070.2
      DustBin 001, 002, 003, 004, 00514.3, 41.5, 30.2, 11.3, 2.7
    • Table 4. Time comparison between the original retrieval algorithm and the improved algorithm of GMI

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      Table 4. Time comparison between the original retrieval algorithm and the improved algorithm of GMI

      ParameterOriginal algorithmImproved algorithm
      Average single forward model computation time32 min1 min 29 s
      Average single retrieval iteration computation time35 min 20 s1 min 56 s
      Total computation time107 min13 min 36 s
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    Zhiqiang Sun, Xianhua Wang, Hanhan Ye, Chao Li, Yuan An, Erchang Sun, Shichao Wu, Hailiang Shi. Fast Retrieval Method of Atmospheric CO2 Based on GF-5 Satellite Remote Sensing Data[J]. Acta Optica Sinica, 2024, 44(18): 1801012

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Dec. 27, 2023

    Accepted: Mar. 4, 2024

    Published Online: Sep. 11, 2024

    The Author Email: Ye Hanhan (yehanhan@aiofm.ac.cn)

    DOI:10.3788/AOS231995

    CSTR:32393.14.AOS231995

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