BIOME-BGC-GA for Optimizing Tectona Grandis Linn. F.'s Ecophysiological Parameters Using Remotely Sensed Data

The biogeochemical model BIOME-BGC was combined with genetic algorithms (GAs) to determine the appropriate set of ecophysiological parameters for teak (Tectona grandis Linn F.). The optimized parameters consisted of transfer growth period and litterfall, as fractions of growing season, and leaf litter labile, leaf litter cellulose, and leaf litter lignin at a particular site. Data assimilation was performed using a GA, and ecophysiological parameters were obtained from the simulated leaf area index (LAI) data after calibration. It was demonstrated that there is a significant relationship between the simulated and the measured LAI with high R^2 at 0.78. Reliable data assimilation may facilitate obtaining reliable model parameters, thus improving LAI estimates. The results demonstrated the great potential of simulating LAI using GA-based BIOME-BGC at the site scale. This can be applied to determine the net primary production (NPP) or the gross primary production (GPP), which are important in the study of the carbon exchange cycle.