Biogeosciences
Biogeosciences
BG
Articles | Volume 21, issue 10
Articles | Volume 21, issue 10
https://doi.org/10.5194/bg-21-2447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-2447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 10
Research article
 | 
24 May 2024
Research article |  | 24 May 2024

Using automated machine learning for the upscaling of gross primary productivity

Max Gaber, Yanghui Kang, Guy Schurgers, and Trevor Keenan

Viewed

Total article views: 1,109 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
792 273 44 1,109 34 39
  • HTML: 792
  • PDF: 273
  • XML: 44
  • Total: 1,109
  • BibTeX: 34
  • EndNote: 39
Views and downloads (calculated since 31 Aug 2023)
Cumulative views and downloads (calculated since 31 Aug 2023)

Viewed (geographical distribution)

Total article views: 1,109 (including HTML, PDF, and XML) Thereof 1,184 with geography defined and -75 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jun 2024
Download
Short summary
Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a vital role in the carbon cycle. We can measure GPP locally, but producing larger and continuous estimates is challenging. Here, we present an approach to extrapolate GPP to a global scale using satellite imagery and automated machine learning. We benchmark different models and predictor variables and achieve an estimate that can capture 75 % of the variation in GPP.
Read more
Altmetrics
Final-revised paper
Preprint