IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10853-d902635.html
   My bibliography  Save this article

High-Coverage Reconstruction of XCO 2 Using Multisource Satellite Remote Sensing Data in Beijing–Tianjin–Hebei Region

Author

Listed:
  • Wei Wang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410017, China)

  • Junchen He

    (School of Geosciences and Info-Physics, Central South University, Changsha 410017, China)

  • Huihui Feng

    (School of Geosciences and Info-Physics, Central South University, Changsha 410017, China)

  • Zhili Jin

    (School of Geosciences and Info-Physics, Central South University, Changsha 410017, China)

Abstract

The extreme climate caused by global warming has had a great impact on the earth’s ecology. As the main greenhouse gas, atmospheric CO 2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO 2 concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO 2 and various factors affecting the spatial distribution of CO 2 , this study used multisource satellite-based data and a random forest model to reconstruct the daily CO 2 column concentration (XCO 2 ) with full spatial coverage in the Beijing–Tianjin–Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R 2 ) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R 2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R 2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO 2 concentrations from 2015 to 2019 in the Beijing–Tianjin–Hebei region was conducted using the established model. The study of the spatial distribution of XCO 2 concentration in the Beijing–Tianjin–Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO 2 map has the potential to monitor regional carbon emissions and evaluate emission reduction.

Suggested Citation

  • Wei Wang & Junchen He & Huihui Feng & Zhili Jin, 2022. "High-Coverage Reconstruction of XCO 2 Using Multisource Satellite Remote Sensing Data in Beijing–Tianjin–Hebei Region," IJERPH, MDPI, vol. 19(17), pages 1-20, August.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10853-:d:902635
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10853/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/17/10853/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raupach, M.R. & Rayner, P.J. & Paget, M., 2010. "Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions," Energy Policy, Elsevier, vol. 38(9), pages 4756-4764, September.
    2. Kevin Robert Gurney & Rachel M. Law & A. Scott Denning & Peter J. Rayner & David Baker & Philippe Bousquet & Lori Bruhwiler & Yu-Han Chen & Philippe Ciais & Songmiao Fan & Inez Y. Fung & Manuel Gloor , 2002. "Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models," Nature, Nature, vol. 415(6872), pages 626-630, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xueqi & Liu, Gengyuan & Coscieme, Luca & Giannetti, Biagio F. & Hao, Yan & Zhang, Yan & Brown, Mark T., 2019. "Study on the emergy-based thermodynamic geography of the Jing-Jin-Ji region: Combined multivariate statistical data with DMSP-OLS nighttime lights data," Ecological Modelling, Elsevier, vol. 397(C), pages 1-15.
    2. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    3. Gang Xu & Tianyi Zeng & Hong Jin & Cong Xu & Ziqi Zhang, 2023. "Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    4. Minkwang Cho & Hyun Mee Kim, 2022. "Effect of assimilating CO2 observations in the Korean Peninsula on the inverse modeling to estimate surface CO2 flux over Asia," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    5. Juan Jose Miranda & Oscar A. Ishizawa & Hongrui Zhang, 2020. "Understanding the Impact Dynamics of Windstorms on Short-Term Economic Activity from Night Lights in Central America," Economics of Disasters and Climate Change, Springer, vol. 4(3), pages 657-698, October.
    6. Nadiia Charkovska & Mariia Halushchak & Rostyslav Bun & Zbigniew Nahorski & Tomohiro Oda & Matthias Jonas & Petro Topylko, 2019. "A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 907-939, August.
    7. Rostyslav Bun & Zbigniew Nahorski & Joanna Horabik-Pyzel & Olha Danylo & Linda See & Nadiia Charkovska & Petro Topylko & Mariia Halushchak & Myroslava Lesiv & Mariia Valakh & Vitaliy Kinakh, 2019. "Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 853-880, August.
    8. Mengcheng Li & Haimeng Liu & Shangkun Yu & Jianshi Wang & Yi Miao & Chengxin Wang, 2022. "Estimating the Decoupling between Net Carbon Emissions and Construction Land and Its Driving Factors: Evidence from Shandong Province, China," IJERPH, MDPI, vol. 19(15), pages 1-26, July.
    9. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    10. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    11. Liang Feng & Paul I. Palmer & Sihong Zhu & Robert J. Parker & Yi Liu, 2022. "Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    12. Paulo Reis Mourao, 2019. "The effectiveness of Green voices in parliaments: Do Green Parties matter in the control of pollution?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(2), pages 985-1011, April.
    13. Naifu Yu & Yingkai Tang & Ying Ma, 2023. "Spatio-Temporal Evolution, Spillover Effects of Land Resource Use Efficiency in Urban Built-Up Area: A Further Analysis Based on Economic Agglomeration," Land, MDPI, vol. 12(3), pages 1-17, February.
    14. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    15. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    16. Ahfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2017. "The compact city in empirical research: A quantitative literature review," LSE Research Online Documents on Economics 83638, London School of Economics and Political Science, LSE Library.
    17. Hamidreza Zoraghein & Brian C. O'Neill, 2020. "A spatial population downscaling model for integrated human-environment analysis in the United States," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(54), pages 1563-1606.
    18. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    19. Michail Fragkias & José Lobo & Karen C Seto, 2017. "A comparison of nighttime lights data for urban energy research: Insights from scaling analysis in the US system of cities," Environment and Planning B, , vol. 44(6), pages 1077-1096, November.
    20. Elisabet Garrido & Consuelo González & Raquel Orcos, 2020. "ISO 14001 and CO2 emissions: An analysis of the contingent role of country features," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 698-710, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10853-:d:902635. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.