articleBiogeosciencesJul 29, 2016GOLD OA

Predicting carbon dioxide and energy fluxes across global FLUXNET sites withregression algorithms

GTGianluca TramontanaMJMartin JungCRChristopher R. SchwalmKIKazuhito IchiiGCGustau Camps‐Valls

Università degli Studi della Tuscia · Max Planck Institute for Biogeochemistry · +12 more institutions

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Abstract

Abstract. Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely…

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Authors

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Topics & keywords

Topics
Keywords
  • FluxNet
  • Eddy covariance
  • Environmental science
  • Sensible heat
  • Primary production
  • Latent heat
  • Atmospheric sciences
  • Water cycle
UN Sustainable Development Goals
  • Climate action
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