Articles | Volume 13, issue 14
Biogeosciences, 13, 4291–4313, 2016
https://doi.org/10.5194/bg-13-4291-2016
Biogeosciences, 13, 4291–4313, 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 et al.

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Cited articles

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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).
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