Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
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.
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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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC1: 'Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms', Anonymous Referee #2, 28 Mar 2016 Printer-friendly Version 
AC1: 'Answers by authors to comments by Referee #2', Gianluca Tramontana, 31 May 2016 Printer-friendly Version 
 
RC2: 'Review of Tramontana et al.', Anonymous Referee #3, 01 Apr 2016 Printer-friendly Version 
AC2: 'Answers by authors to comments by Referee #3', Gianluca Tramontana, 31 May 2016 Printer-friendly Version 
 
RC3: 'Review Report of Referee #3', Anonymous Referee #4, 11 Apr 2016 Printer-friendly Version Supplement 
AC3: 'Answers by authors to comments by Referee #4', Gianluca Tramontana, 31 May 2016 Printer-friendly Version 
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  
CC BY 4.0
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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).
We have evaluated 11 machine learning (ML) methods and two complementary drivers' setup to...
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