Improvement of Biome-BGC model by incorporation and data assimilation

A strategy of data assimilation using the refined remote sensing product for the process-based model (Biome-BGC) in order to improve the simulated carbon fluxes was proposed. Firstly, we applied the optimized the remote-sensing-based MODIS MOD_17 GPP (MOD_17) model to calibrate the process-based Biome-BGC model. This incorporation strategy for the parameterization of Biome-BGC has been proved to be more reliable in carbon fluxes' simulations. The calibrated Biome-BGC model agreed better with the Eddy covariance (EC) measurements (R
²
=0.87, RMSE=1.583 gC/m
²
/d) than the original model (R
²
=0.72, RMSE=2.419 gC/m
²
/d). Afterwards, two years (8-day during 2003 and 2004) Global LAnd Surface Satellite (GLASS) LAI products were applied to test the data assimilation procedure for the calibrated Biome-BGC using Ensemble Kalman Filter (EnKF). The results indicated that simulated LAI through assimilation agreed better with GLASS LAI, and the carbon fluxes are hoped to improve by further adaption of the filter.