Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 13
Articles | Volume 17, issue 13
https://doi.org/10.5194/amt-17-3949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 13
Research article
 | 
03 Jul 2024
Research article |  | 03 Jul 2024

Fast retrieval of XCO2 over east Asia based on Orbiting Carbon Observatory-2 (OCO-2) spectral measurements

Fengxin Xie, Tao Ren, Changying Zhao, Yuan Wen, Yilei Gu, Minqiang Zhou, Pucai Wang, Kei Shiomi, and Isamu Morino

Viewed

Total article views: 2,069 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,586 421 62 2,069 61 57
  • HTML: 1,586
  • PDF: 421
  • XML: 62
  • Total: 2,069
  • BibTeX: 61
  • EndNote: 57
Views and downloads (calculated since 20 Nov 2023)
Cumulative views and downloads (calculated since 20 Nov 2023)

Viewed (geographical distribution)

Total article views: 2,069 (including HTML, PDF, and XML) Thereof 2,059 with geography defined and 10 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 09 May 2025
Download
Short summary
This study demonstrates a new machine learning approach to efficiently and accurately estimate atmospheric carbon dioxide levels from satellite data. Rather than using traditional complex physics-based retrieval methods, neural network models are trained on simulated data to rapidly predict CO2 concentrations directly from satellite spectral measurements.
Read more
Share