Articles | Volume 13, issue 14
https://doi.org/10.5194/bg-13-4291-2016
https://doi.org/10.5194/bg-13-4291-2016
Research article
 | 
29 Jul 2016
Research article |  | 29 Jul 2016

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana, Martin Jung, Christopher R. Schwalm, Kazuhito Ichii, Gustau Camps-Valls, Botond Ráduly, Markus Reichstein, M. Altaf Arain, Alessandro Cescatti, Gerard Kiely, Lutz Merbold, Penelope Serrano-Ortiz, Sven Sickert, Sebastian Wolf, and Dario Papale

Viewed

Total article views: 12,899 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
7,480 5,083 336 12,899 1,259 242 323
  • HTML: 7,480
  • PDF: 5,083
  • XML: 336
  • Total: 12,899
  • Supplement: 1,259
  • BibTeX: 242
  • EndNote: 323
Views and downloads (calculated since 07 Mar 2016)
Cumulative views and downloads (calculated since 07 Mar 2016)

Cited

Saved (preprint)

Latest update: 22 May 2024
Download
Short summary
We have evaluated 11 machine learning (ML) methods and two complementary drivers' setup to estimate the carbon dioxide (CO2) and energy exchanges between land ecosystems and atmosphere. Obtained results have shown high consistency among ML and high capability to estimate the spatial and seasonal variability of the target fluxes. The results were good for all the ecosystems, with limitations to the ones in the extreme environments (cold, hot) or less represented in the training data (tropics).
Altmetrics
Final-revised paper
Preprint