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Biogeosciences An interactive open-access journal of the European Geosciences Union
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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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Articles | Volume 13, issue 14
Articles | Volume 13, issue 14
Biogeosciences, 13, 4291–4313, 2016
https://doi.org/10.5194/bg-13-4291-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 13, 4291–4313, 2016
https://doi.org/10.5194/bg-13-4291-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 13, issue 14

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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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (Editor review) (31 May 2016) by Georg Wohlfahrt
AR by Gianluca Tramontana on behalf of the Authors (24 Jun 2016)  Author's response    Manuscript
ED: Publish as is (29 Jun 2016) by Georg Wohlfahrt
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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).
Read more
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Final-revised paper
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Biogeosciences

An interactive open-access journal of the European Geosciences Union

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