Energy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural Network

Abstract

A multi-ecosystems carbon flux simulation from energy fluxes is presented. A new statistical learning technique based on Artificial Neural Network (ANN) back propagation algorithm and multi-layer perceptron architecture was used in the CO₂ simulation. Four input layers (net radiation, soil heat flux, sensible and latent heat flux) were used for training (calibration) and testing (verification) of model outputs. The 15-days half-hourly (grassland) and hourly (forest and cropland) micrometeorological data from eddy covariance observations of AmeriFlux towers were divided into training (5-days) and testing (10-days) sets. Results show that the ANN-based technique predicts CO₂ flux with testing R² values of 0.86, 0.75 and 0.94 for forest, grassland and cropland ecosystems, respectively. The technique is reliable and efficient to estimate regional or global CO₂ fluxes from point measurements and understand the spatiotemporal budget of the CO₂ fluxes.