※The title and the authors are changed. Please refer to the errata.
        
In the previous study, Arakida et al. developed a data assimilation system based on a particle filter approach with a dynamical global vegetation model known as the SEIB-DGVM (Spatially Explicit Individual-Based Dynamic Global Vegetation Model), and assimilated the satellite-based MODIS LAI (Leaf Area Index) observations successfully. We extend the previous study to a large domain in Siberia and estimate the state variables including carbon flux, water flux, heat flux, vegetation structure, and parameters related to the phenology of the deciduous needle leaved tree and grass. At the 64th annual meeting in 2017, we presented the results of LAI and carbon flux estimates. This year, we further present the results of the estimates of the model parameters and additional state variables such as above-ground biomass, tree LAI, and evapotranspiration. We also compare the estimates of above-ground biomass, tree LAI, and carbon flux with other previous studies. The results show that the DA system generally performs well at multiple locations, although systematic differences are found from other studies.