Upscaling

Predicting Spatiotemporal Variability in Radial Tree Growth at the Continental Scale with Machine Learning
Abstract Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries …
Cite DOI
Effect of Spatial Sampling from European Flux Towers for Estimating Carbon and Water Fluxes with Artificial Neural Networks
Abstract Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy …
Cite DOI