upscaling

Predicting Spatiotemporal Variability in Radial Tree Growth at the Continental Scale with Machine Learning

Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where …

Effect of Spatial Sampling from European Flux Towers for Estimating Carbon and Water Fluxes with Artificial Neural Networks

Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for …