Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 11
Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 11
Research article
 | 
18 Nov 2021
Research article |  | 18 Nov 2021

Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Farhan Mustafa, Lingbing Bu, Qin Wang, Na Yao, Muhammad Shahzaman, Muhammad Bilal, Rana Waqar Aslam, and Rashid Iqbal

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Cited articles

Bao, Z., Zhang, X., Yue, T., Zhang, L., Wang, Z., Jiao, Y., Bai, W., and Meng, X.: Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing, Remote Sens.-Basel, 12, 3063, https://doi.org/10.3390/rs12183063, 2020. 
Bie, N., Lei, L., Zeng, Z., Cai, B., Yang, S., He, Z., Wu, C., and Nassar, R.: Regional uncertainty of GOSAT XCO2 retrievals in China: quantification and attribution, Atmos. Meas. Tech., 11, 1251–1272, https://doi.org/10.5194/amt-11-1251-2018, 2018. 
Boden, T. A., Andres, R. J., and Marland, G.: Global, regional, and national fossil-fuel CO2 emissions (1751–2014) (v. 2017), Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States), Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA, 2017. 
Bovensmann, H., Buchwitz, M., Burrows, J. P., Reuter, M., Krings, T., Gerilowski, K., Schneising, O., Heymann, J., Tretner, A., and Erzinger, J.: A remote sensing technique for global monitoring of power plant CO2 emissions from space and related applications, Atmos. Meas. Tech., 3, 781–811, https://doi.org/10.5194/amt-3-781-2010, 2010. 
Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B.: Copernicus Global Land Cover Layers—Collection 2, Remote Sens.-Basel, 12, 1044, https://doi.org/10.3390/rs12061044, 2020. 
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A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based net primary productivity (NPP) and XCO2 retrievals. XCO2 anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO2 anomalies, and ODIAC CO2 emissions from 2015 to 2018 were used as a training dataset; and, finally, CO2 emissions were predicted for 2019 based on the NPP and XCO2 anomalies calculated for the same year.
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