Nadine Gobron

TL;DR: An estimate of global land evapotranspiration from 1982 to 2008 is provided using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm, which suggests that increasing soil-moisture limitations on evapOTranspiration largely explain the recent decline of the global land-evapotranpiration trend.

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: Investigation of relationships between phenology and productivity in temperate and boreal forests finds the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests, which has implications for how climate change may drive shifts in competition within mixed-species stands.

TL;DR: In this paper, a spectral index is used to retrieve vegetation water content from remotely sensed data in the solar spectrum domain. But the spectral index was not optimized for the SPOT-VEGETATION sensor.

TL;DR: In this article, a review of the state of the art of the understanding of the relation between soil moisture drought and the interactions with the carbon cycle of the terrestrial ecosystems is presented.