Artificial neural network application for multi-ecosystem carbon flux simulation

The need for carbon dioxide (CO2) flux estimations covering larger areas and the limitations of the point eddy covariance technique to address this requirement necessitates the modeling of CO2 flux from other micrometeorological variables. The non-linearity of the relationship between CO2 flux and other micrometeorological flux parameters such as energy fluxes limits the applicability of carbon flux models to accurately estimate the flux dynamics. Black box models such as the artificial neural network (ANN) provide a mathematically flexible structure to identify the complex non-linear relationship between inputs and outputs without attempting to explain the nature of the phenomena. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a CO2 flux simulation study on three different ecosystems (forest, grassland and wheat). Energy fluxes (net radiation, latent heat, sensible heat and soil heat flux) and temperature (air and soil) were used to train the ANN and predict the flux of CO2. Diurnal hourly fluxes data from 15 days of observations were divided into training and testing. Results of the CO2 flux simulation show that the technique can successfully predict the observed values with R2 values between 0.75 and 0.94. Predictions from the forest and wheat field show higher promise than the grassland site. The technique is reliable, efficient and highly significant to estimate regional or global CO2 fluxes from point measurements and understand the spatiotemporal budget of the CO2 fluxes.