Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models

Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines enters ecosystem. The remote sensing-based light use efficiency (LUE) model one of tools currently used to estimate GPP at regional scale. Many models have been developed last several decades, and these well evaluated some sites. However, an accurate estimation remains challenging work using LUE because uncertainties caused by parameters, forcing, vegetation spatial heterogeneity. In this study, five widely models, Glo-PEM, VPM, EC-LUE, MODIS algorithm, C-fix, were selected simulate Heihe River Basin forced situ measurements. A multiple-model averaging method, Bayesian (BMA), was combine obtain more reliable estimation. BMA trained flux data from eddy covariance towers located dominant types area. Generally, method performed better than any single model. From case area, it indicated efficient multiple can improve simulation accuracy.