Abstract: Potential of new machine learning methods for understanding long-term interannual variability of carbon and energy fluxes and states from site to global scale (2016 AGU Fall Meeting)

Potential of new machine learning methods for understanding long-term interannual variability of carbon and energy fluxes and states from site to global scale

Markus Reichstein^1,2, Martin Jung³, Paul Bodesheim¹, Miguel Mahecha^2,4, Fabian Gans⁵, Erik Rodner^2,6, Gustau Camps-Valls⁷, Dario Papale⁸, Gianluca Tramontana⁹, Joachim Denzler^2,6 and Dennis D Baldocchi¹⁰, (1)Max Planck Institute for Biogeochemistry, Jena, Germany, (2)Michael-Stifel-Center Jena for Data-driven and Simulation Science, Jena, Germany, (3)Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, Jena, Germany, (4)Max Planck Institute for Biogeochemistry, Biogeochemical Integration, Jena, Germany, (5)German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany, (6)Friedrich-Schiller University Jena, Computer Vision Group, Jena, Germany, (7)Image Processing Laboratory, Universitat de València, Paterna, Spain, (8)National Research Council, Institute of Research on Terrestrial Ecosystems (CNR-IRET), Montelibretti, Italy, (9)Tuscia University, Viterbo, Italy, (10)University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, CA, United States
Scientific Team Name: FLUXCOM team

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