Estimating the Aboveground Dry Biomass of Grass by Assimilation of Retrieved LAI Into a Crop Growth Model

This study presents a method to assimilate leaf area index (LAI) retrieved from MODIS data using a physically based method into a soil-water-atmosphere-plant (SWAP) model to estimate the aboveground dry biomass of grass in the Ruoergai grassland, China. The assimilation method consists of reinitializing the model with optimal input parameters that allow a better temporal agreement between the LAI simulated by the SWAP model and the LAI retrieved from MODIS data. The minimization is performed by a four-dimensional variational data assimilation (4D-VAR) algorithm but which is challenged by the development of the adjoint model. The automatic differentiation (AD) technique is thus used to provide the adjoint model at the level of computer language codes. After the re-initialization, the simulated aboveground dry biomass value is compared with ground measurements taken in early August 2013. The results show that the biomass can be estimated with highly satisfactory accuracy level through the assimilation method with R-2 (the deterministic coefficient) = 0.73 and RMSE(root-mean-square error) = 617.94 kg ha(-1). The accuracy is further improved when the newly derived RMSELAI values are used as observation errors in the assimilation process, with R-2 = 0.76 and RMSE = 542.52 kg ha(-1). Both assimilation strategies yield a significant improvement in SWAP model accuracy with respect to no significant correlation obtained when the SWAP model is run alone with constant values of the input parameters employed for the whole area. The validity of the 4D-VAR method for biomass estimation is well demonstrated.