Retrieval algorithm of atmospheric CO2 for urban underlying surface in Beijing

  • role: First author第一作者
  • Affiliation:

    Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

    University of Science and Technology of China, Hefei 230031, China

  • Email:wuhaoahnu@163.com
  • Introduction:吴浩,1991年生,男,博士研究生,研究方向为大气CO2反演。E-mail:wuhaoahnu@163.com
WU Hao12,  
  • role: Corresponding author通信作者
  • Affiliation:

    Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

  • Email:xhwang@aiofm.ac.cn
  • Introduction:王先华,1963年生,男,研究员,研究方向为大气CO2反演。E-mail:xhwang@aiofm.ac.cn
WANG Xianhua1*,  
  • Affiliation:

    Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

YE Hanhan1,  
  • Affiliation:

    Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

    University of Science and Technology of China, Hefei 230031, China

JIANG Yun12,  
  • Affiliation:

    Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

    University of Science and Technology of China, Hefei 230031, China

DUAN Fenghua12,  
  • Affiliation:

    Key Laboratory of Optical Calibration and Characterization of Chinese Academy of Science, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China

    University of Science and Technology of China, Hefei 230031, China

LYU Song12

ملخص

The greenhouse Gas Monitor Instrument (GMI) is a payload of GF-5 satellite. It is mainly used to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4) and carbon cycling studies. Surface reflection is an important factor for developing a high-precision column-averaged carbon dioxide dry air mole fraction (XCO2) retrieval algorithm for the GMI short-wave infrared data. Urban areas are an important source of CO2 emission, and an apparent directional reflection occurs in the urban underlying surface. However, the aerosol optical depth (AOD 550 nm) in Urban Beijing is relatively large, and combining atmospheric scattering and surface directional reflection makes the retrieval of XCO2 in Beijing urban area difficult. In this study, we investigate the MODIS Bidirectional Reflectance Distribution Function (BRDF) data in Beijing urban area in 2011–2016. The three parameters (fiso, fgeo, and fvol) of the BRDF model vary with season. The ratio of fiso to fgeo and of fiso to fvol slightly change. Statistics show that the average value of fgeo/fiso is 0.42±0.09 and fvol/fiso is 0.23±0.03. The simplification of the BRDF model allows us to invert the BRDF parameters simultaneously through one observation of the GMI. We present a new algorithm to improve the accuracy of XCO2 in Beijing urban areas through simultaneous retrieval of BRDF parameters. An a priori value of AOD is provided by using a directional polarimetric camera, which is onboard the GF-5 satellite. The MODIS MCD43A1 product is used to build an a priori surface BRDF database after filtering and resampling. The a priori CO2 profile is estimated on the basis of the analysis of CO2 profile distribution in Beijing area. The monthly a priori covariance matrices of CO2 at a grid cell (2°×3°) on the globe is precalculated as input parameters using the carbon tracker database. The measurement error covariance matrix is (y/SNR)2, and the signal-to-noise ratio (SNR) used is the result of a laboratory test. This SNR is a tentative value and must be tuned after acquiring in-orbit data. To determine the effect of aerosols and surface BRDF reflection on the retrieval of XCO2, we simulate GMI measurements in the spectral range of 6310–6380 cm−1. We use absolute radiance, ratio radiance, and BRDF methods to retrieve the simulation data separately. The error of the absolute radiance method exceeds 30% when the AOD is 0.05 considering the changes in surface albedo caused by surface direction reflection, and the error of the ratio radiance method is less than 1% in most observations. By contrast, the BRDF method has high accuracy, and its error is less than 0.2%. In the case of high AOD (0.4), the maximum error of XCO2 is 4.75% without the inversion of BRDF parameters. The evaluated precisions of the retrieved XCO2 are less than 0.5% in most cases. To validate the correctness of our algorithm for the measured data, we retrieve the observations of the AOD less than 0.4 from Greenhouse Gases Observing Satellite (GOSAT) L2 data in Beijing urban area in 2016. We find that the correlation is 0.85, and the average error is 1.25 ppm by comparing our retrievals with GOSAT NIES XCO2. The retrievals are stable, except the results in summer. The AOD of 64.28% observations in summer is larger than 0.3, and the rest is close to 0.3. The difference in albedo caused by the BRDF characteristics of the urban underlying surface significantly influences the accuracy of XCO2 retrievals, and the high-precision CO2 concentration information cannot be obtained when it is not corrected. The aerosol conditions in urban areas are frequently complex. The proposed algorithm for the simultaneous retrieval of surface BRDF parameters and CO2 content can accurately describe the surface direction reflection. The proposed algorithm improves the retrieval accuracy and data utilization of GMI data in the urban areas of Beijing.

مفهوم

GF-5;GMI;CO2 retrieval;BRDF;aerosol

References

  1. 1.
    Aben I, Hasekamp O and Hartmann W. 2007. Uncertainties in the space-based measurements of CO2 columns due to scattering in the Earth’s atmosphere. Journal of Quantitative Spectroscopy and Radiative Transfer, 104(3): 450–459
  2. 2.
    Dhakal S. 2009. Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy, 37(11): 4208–4219
  3. 3.
    Friedl M A, McIver D K, Hodges J C F, Zhang X Y, Muchoney D, Strahler A H, Woodcock C E, Gopal S, Schneider A, Cooper A, Baccini A, Gao F and Schaaf C. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1/2): 287–302
  4. 4.
    Hammerling D M, Michalak A M and Kawa S R. 2012. Mapping of CO2 at high spatiotemporal resolution using satellite observations: global distributions from OCO‐2. Journal of Geophysical Research: Atmospheres, 117(D6): D06306
  5. 5.
    Liu Y, Yang D X and Cai Z N. 2013. A retrieval algorithm for TanSat XCO2 observation: retrieval experiments using GOSAT data. Chinese Science Bulletin, 58(13): 1520–1523
  6. 6.
    Myhrvold N P and Caldeira K. 2012. Greenhouse gases, climate change and the transition from coal to low-carbon electricity. Environmental Research Letters, 7(1): 014019
  7. 7.
    O’Dell C W, Connor B, Bösch H, O’Brien D, Frankenberg C, Castano R, Christi M, Eldering D, Fisher B, Gunson M, McDuffie J, Miller C E, Natraj V, Oyafuso F, Polonsky I, Smyth M, Taylor T, Toon G C, Wennberg P O and Wunch D. 2012. The ACOS CO2 retrieval algorithm-Part 1: description and validation against synthetic observations. Atmospheric Measurement Techniques, 5(1): 99–121
  8. 8.
    Oishi Y, Ishida H, Nakajima T Y, Nakamura R and Matsunaga T. 2017. The impact of different support vectors on GOSAT-2 CAI-2 L2 cloud discrimination. Remote Sensing, 9(12): 1236
  9. 9.
    Schmidt M, Udelhoven T, Röder A and Gill T K. 2012. Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna. Journal of Applied Remote Sensing, 6(1): 063512
  10. 10.
    Shuai Y M, Schaaf C B, Strahler A H, Liu J C and Jiao Z T. 2008. Quality assessment of BRDF/albedo retrievals in MODIS operational system. Geophysical Research Letters, 35(5): L05407
  11. 11.
    Wang K C, Wang J K, Wang P C and Chen H B. 2008. The accuracy of MODIS albedo over Beijing Urban area and its algorithm improvement. Chinese Journal of Atmospheric Sciences, 32(1): 67–74
  12. 12.
    Wu H, Wang X H, Ye H H, Jiang Y and Duan F H. 2018. Error analysis of the greenhouse-gases monitor instrument short wave infrared XCO2 retrieval algorithm. Journal of Applied Remote Sensing, 12(1): 016015
  13. 13.
    Ye H H, Wang X H, Wu J, Fang Y H, Ma J J, Jiang X H and Wei Q Y. 2013. Study of the effect of surface reflectance on atmospheric CO2 retrieval and ratio spectrometry. Spectroscopy and Spectral Analysis, 33(8): 2182–2187
  14. 14.
    Yokota T, Yoshida Y, Eguchi N, Ota Y, Tanaka T, Watanabe H and Maksyutov S. 2009. Global concentrations of CO2 and CH4 retrieved from GOSAT: first preliminary results. Sola, 5: 160–163
  15. 15.
    Zhang H, Jiao Z T, Dong Y D, Li J Y and Li X W. 2015. Albedo retrieved from BRDF archetype and surface directional reflectance. Journal of Remote Sensing, 19(3): 355–367
  16. 16.
    Zhang H, Yu C, Su L, Li L J, Fan M, Wang Y P and Chen L F. 2017. Emission control effects observed from space during the military parade 2015 in Beijing. Journal of Remote Sensing, 21(4): 622–632

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