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

Viewed

Total article views: 5,161 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,910 1,152 99 5,161 124 152
  • HTML: 3,910
  • PDF: 1,152
  • XML: 99
  • Total: 5,161
  • BibTeX: 124
  • EndNote: 152
Views and downloads (calculated since 11 Aug 2021)
Cumulative views and downloads (calculated since 11 Aug 2021)

Viewed (geographical distribution)

Total article views: 5,161 (including HTML, PDF, and XML) Thereof 5,027 with geography defined and 134 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Apr 2026
Download
Short summary
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.
Read more
Share