## 4 Discussion
### Model Representation of In-situ Carbon Flux Observations  
In a next step, we performed an independent analysis of the simulation results. In Figure 7, the Net Ecosystem Productivity (NEP) and Gross Primary Productivity (GPP) from site CA-Gro as simulated by the different assimilation experiments are compared with FLUXNET NEP and GPP data. For the whole 10 sites, results are shown in Figure 11. When comparing the eddy covariance measurements against our simulation results, we needed to keep in mind the difference in footprint size of the flux towers (typically a few hundred m\({}^{2}\)) to the spatial resolution of the products used in the assimilation (~600 km\({}^{2}\) for the SMOS soil moisture and ~1 km\({}^{2}\) for the JRC-TIP FAPAR). Therefore, we did not expect a perfect fit against the observations even with the posterior model. In fact, there were large differences of the various optimized NEP values. As shown in Figure 7, the assimilation of SMOS and FAPAR at multi-site achieved the best performance for NEP and GPP for site CA-Gro. For most of the sites, the optimized NEP and GPP captured the seasonality of observations very well, except for two sites, AU-DaS and site US-Wkg (Figures 11 and 12). As above, a major influence of land management processes not represented in the model seems a probable explanation for this deviation. Both sites are dominated by C4 grasses (C4 Gr) and evergreen shrubs (EvShr) and are characterizedby an arid/semi-arid climate. Both sites are strongly influenced by human activities, such as farming and grazing. For the other sites, modelled NEP and GPP matched the fluxes for the non-growing season fluxes much better than for the growing season. For example, at the site CN-Sw2 (Figure 10(c)), the peak value of modeled NEP was much higher than the observations. For this site, only measurements for the year, 2011, were available. This site experienced a severe drought in 2011, causing a significant decrease in observed NEP during the growing season. Only the prior and soil moisture assimilation experiment in the single-site case detected the changes in July (Figure 10(c)). A similar decrease in observed NEP was detected at the site, US-Wkg (Figure 10(j)) between June and July. The discrepancy between modeled and measured NEP indicates again that the model was not able to capture the reduction in NEP in dry seasons. Table 6 shows the relative changes of RMSE (%) to prior simulation for NEP (RMSE\({}_{\text{NEP}}\)) using the eddy covariance measurements at the sites and the simulated NEP from the various experiments. Generally, there was a reduction of the RMSE\({}_{\text{NEP}}\) values of up to 43% when compared to the prior simulation. However, especially when only one dataset was assimilated in a single-site experiment, the agreement with the NEP observations was occasionally degraded. In general, better results were achieved when soil moisture and FAPAR were simultaneously assimilated in the multi-site  
Figure 7: Simulated (**a**) net ecosystem productivity (NEP); (**b**) gross primary productivity (GPP) compared against NEP and GPP data derived from FLUXNET (black dots) at site CA-Gro. The blue line denotes the simulation with the prior parameters, and the red line denotes multi-site assimilation results with both soil moisture and FAPAR used for assimilation (’sm+fapar (multi)’). assimilation case. Similar results were found for gross primary productivity (GPP), as shown in Figure A2 and Table A3. Recently, assimilation experiments were conducted at a site-scale using latent heat and FAPAR observations by [18]. They noticed that the combination of two different datasets could lead to a higher uncertainty reduction in model parameters and in other model variables of interest than in single data set cases. In another study, single-site and multi-site assimilation experiments were compared with the CCDAS [51]. They also demonstrated a comparable model performance for multi-site assimilation to single-site assimilation.