Articles | Volume 13, issue 14
https://doi.org/10.5194/bg-13-4291-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-13-4291-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
CORRESPONDING AUTHOR
Department for Innovation in Biological, Agro-food and Forest
systems (DIBAF), University of Tuscia, Viterbo, 01100, Italy
Martin Jung
Max
Planck Institute for Biogeochemistry, Jena, 07745, Germany
Christopher R. Schwalm
Woods
Hole Research Center, Falmouth, MA 02540, USA
Kazuhito Ichii
Department of
Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth
Science and Technology, Yokohama, 236-0001, Japan
Center for
Global Environmental Research, National Institute for Environmental Studies,
Tsukuba, 305-8506, Japan
Gustau Camps-Valls
Image Processing Laboratory (IPL),
Universitat de València, Paterna (València), 46980, Spain
Botond Ráduly
Department for Innovation in Biological, Agro-food and Forest
systems (DIBAF), University of Tuscia, Viterbo, 01100, Italy
Department of Bioengineering, Sapientia Hungarian University of
Transylvania, Miercurea Ciuc, 530104, Romania
Markus Reichstein
Max
Planck Institute for Biogeochemistry, Jena, 07745, Germany
M. Altaf Arain
School of Geography
and Earth Sciences, McMaster University, Hamilton (Ontario), L8S4L8, Canada
Alessandro Cescatti
European Commission, Joint Research Centre, Directorate for
Sustainable Resources, Ispra, Italy
Gerard Kiely
Civil & Environmental
Engineering and Environmental Research Institute, University College, Cork,
T12 YN60, Ireland
Lutz Merbold
Department of Environmental Systems Science,
Institute of Agricultural Sciences, ETH Zurich, Zurich, 8092, Switzerland
Mazingira Centre, Livestock Systems and Environment, International
Livestock Research Institute (ILRI), 00100, Nairobi, Kenya
Penelope Serrano-Ortiz
Department of Ecology, University of Granada, Granada, 18071,
Spain
Sven Sickert
Computer Vision Group, Friedrich Schiller University Jena,
07743 Jena, Germany
Sebastian Wolf
Department of Environmental Systems Science,
Institute of Agricultural Sciences, ETH Zurich, Zurich, 8092, Switzerland
Dario Papale
Department for Innovation in Biological, Agro-food and Forest
systems (DIBAF), University of Tuscia, Viterbo, 01100, Italy
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Latest update: 06 Dec 2025
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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