## 5 Conclusions  
We performed data assimilation experiments at multiple and single sites with two sources of satellite datasets, namely SMOS L3 soil moisture and JRC-TIP FAPAR. These data sets were assimilated in the global terrestrial biosphere model, BETHY, over a time period from 2009 to 2015. The sites for the multiple and single site approach were 10 stations from the FLUXNET network. Model output was also analyzed and compared to independent NEP, GPP, and in-situ soil moisture observations at different sites. This is the first report on experiments that SMOS L3 soil moisture data were used in combination with remotely sensed FAPAR data within site-scale assimilation experiments in order to constrain model parameters. In all cases, the cost function was reduced significantly, whether a single data set was assimilated or both simultaneously. However, the simultaneous assimilation of both data sets reduced the cost functions for the 10 considered FLUXNET sites to a larger degree (between 16.37% to 69.28%) than the experiments assimilating only one dataset. Parameters related to phenology, soil water, and energy balance processes showed the largest relative changes. Generally, assimilation of FAPAR observations mainly changed parameters related to phenological processes in the BETHY model, and thus mainly improved simulated FAPAR. SMOS soil moisture, to the contrary, constrained efficiently the soil water and energy balance as computed by the model as indicated by larger changes in the related parameters, such as the emissivity of the atmosphere (_emis_0). In general, the combination of SMOS L3 soil moisture and FAPAR in a multi-site assimilation performed best in fitting both simulated soil moisture and FAPAR. Considering the range of optimal parameters computed in the different experiments, we conclude that a multi-site assimilation with various sources of well-processed observational data streams achieved the best overall results. Soil moisture has shown to be important in controlling ecosystem production. Adding more detailed processes related to soil water and heat transfer in soil profiles to the model might therefore be key in improving the model response to extremes, such as drought, flood, or frost. Posterior parameters were also analyzed by comparing the results from the different assimilation experiments. For single sites and single data set assimilation, some global and PFT dependent parameters showed a large range of posterior values. This was probably due to problems with sparse observational data, specific processes associated to a cold/arid climate, and more complicated phenology processes in certain PFT (e.g., crops and grasses) as imposed by management practices. In the assimilation experiments, the emissivity of the atmosphere (_emis_0) covered a wide range of posterior values, in particular considering that _emis_0 is a global parameter. Additional independent data on the radiative balance at the surface, such as outgoing longwave radiation, would help to constrain this parameter. Comparison of simulated NEP with FLUXNET NEP observations as well as simulated soil moisture with FLUXNET in-situ soil moisture observations gave further evidence for the robustness of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**KUM**} & \multicolumn{3}{c}{**SPO**} \\ \cline{2-7}
**Experiments** & \multicolumn{1}{c}{**CO\({}_{2}\) Concentrations**} & \multicolumn{1}{c}{**Annual CO\({}_{2}\) Growth Rates**} & \multicolumn{1}{c}{**CO\({}_{2}\) Concentrations**} & \multicolumn{1}{c}{**Annual CO\({}_{2}\) Growth Rates**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{ME_{GR}}\) (ppm/yr) & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) (ppm) & \(\mathbf{ME_{GR}}\) (ppm/yr) \\ \hline prior & 0.83 & 8.75 & \(-2.17\) & 0.99 & 5.60 & \(-1.68\) \\ sm+fapar (multi) & 0.78 & 7.84 & \(-2.05\) & 0.98 & 4.73 & \(-1.49\) \\ \hline \hline \end{tabular}
\end{table}