Lutz Merbold

TL;DR: In this paper, the authors upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE), to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use.

TL;DR: The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe, and is detailed in this paper.

TL;DR: In this article, the authors 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).

TL;DR: In this article, the authors explored the relationship between energy balance closure and landscape heterogeneity using MODIS products and GLOBEstat elevation data and found that landscape-level heterogeneity in vegetation and topography cannot be ignored as a contributor to incomplete energy balance closures at the surface-atmosphere exchange measurements.

TL;DR: It is found that plant phenological and physiological properties can be integrated in a robust index—the product of the length of CO2 uptake period and the seasonal maximal photosynthesis—to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.