Toni Viskari

TL;DR: It is argued that stronger and more innovative connections to data are required to address gaps in understanding, and that constrained predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter-disciplinary communication.

TL;DR: In this paper, a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM was proposed, which only uses reflectance and does not require transmittance observations.

TL;DR: A critical look at the information infrastructure that connects ecosystem modeling and measurement efforts is taken, and a roadmap to community cyberinfrastructure development is proposed that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery.

TL;DR: The results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations, and overall the prediction follows observed NEE better than the model alone.

TL;DR: In this article, the Ecosystem Demography model (ED2) is combined with a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state, and the model is calibrated against estimates of hemispherical reflectance from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States.