Earth System Dynamics
Earth System Dynamics
ESD
Articles | Volume 14, issue 5
Articles | Volume 14, issue 5
https://doi.org/10.5194/esd-14-1039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-1039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 5
Research article
 | 
10 Oct 2023
Research article |  | 10 Oct 2023

Dynamic savanna burning emission factors based on satellite data using a machine learning approach

Roland Vernooij, Tom Eames, Jeremy Russell-Smith, Cameron Yates, Robin Beatty, Jay Evans, Andrew Edwards, Natasha Ribeiro, Martin Wooster, Tercia Strydom, Marcos Vinicius Giongo, Marco Assis Borges, Máximo Menezes Costa, Ana Carolina Sena Barradas, Dave van Wees, and Guido R. Van der Werf

Viewed

Total article views: 2,048 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,626 342 80 2,048 58 67 56
  • HTML: 1,626
  • PDF: 342
  • XML: 80
  • Total: 2,048
  • Supplement: 58
  • BibTeX: 67
  • EndNote: 56
Views and downloads (calculated since 06 Mar 2023)
Cumulative views and downloads (calculated since 06 Mar 2023)

Viewed (geographical distribution)

Total article views: 2,048 (including HTML, PDF, and XML) Thereof 2,041 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Dec 2024
Download
Short summary
Savannas account for over half of global landscape fire emissions. Although environmental and fuel conditions affect the ratio of species the fire emits, these dynamics have not been implemented in global models. We measured CO2, CO, CH4, and N2O emission factors (EFs), fuel parameters, and fire severity proxies during 129 individual fires. We identified EF patterns and trained models to estimate EFs of these species based on satellite observations, reducing the estimation error by 60–85 %.
Read more
Altmetrics
Final-revised paper
Preprint