## 4 Discussion
### Soil Moisture and FAPAR Controls on Soil Hydrology  
Surface soil moisture and FAPAR data control the soil moisture process in different ways. FAPAR controls the photosynthetic processes and phenology as represented by the model. Both influence plant water uptake indirectly under water-limited conditions. In other assimilation studies, [15] and [18], the authors also noticed that the inclusion of FAPAR in the assimilation resulted in significant adjustments in the maximum plant-available soil moisture. The impact of FAPAR on the model's soil hydrology is not fully understood and obviously rather indirect compared to the assimilation of soil moisture data. Besides of FAPAR's role in controlling the model's phenology, the assimilation of FAPAR also constrains soil hydrology related parameters [15]. Vice versa, the assimilation of soil moisture observations constrains also the model's phenology and photosynthesis by influencing the water uptake and surface energy balance, as was demonstrated by [19]. In fact, assimilation of surface soil moisture corrects the process parameters in the model's hydrological scheme. Because surface and root zone soil moisture share common parameters, such as soil porosity (which depends on the soil texture in a given gridcell), to calculate soil water properties,  
Figure 6: Posterior (i.e., optimized) values for soil texture dependent parameters related to soil water processes, (**a**) \(s_{b}\) for different soil textures; (**b**) \(s_{n}\) for different soil textures. Normalization and symbols as in Figure 4.  
the assimilation of surface soil moisture also constrains (through these parameters) plant-available soil moisture. When assimilating soil moisture only ('sm'), single-site optimizations achieved better soil moisture results for most sites than multi-site assimilation (Table 3). For the FR-Pue site, the single-site optimization resulted in a larger \(J_{sm}\) than the single-site'sm+fapar' optimization did. This was probably due to the relatively small number of SMOS soil moisture data points at FR-Pue (31) for the period from 2010 to 2015, which moreover showed nearly no temporal variability. An optimized parameter set for this site using these few and relatively constant observations was obviously difficult to compute in a single-site approach. Therefore, in the'sm+fapar' case, the FAPAR observations provided a stronger constraint through common parameters on the soil moisture simulation than the actual observations at FR-Pue. We also noticed that the smaller \(J_{sm}\) at'sm+fapar' for single-site optimization is compensated with a larger \(J_{p}\). This indicates that due to limited soil moisture observations, model parameters were over-adjusted to achieve a better performance. Better results for soil moisture were obtained when soil moisture and FAPAR were simultaneously assimilated ('sm+fapar'), with slight differences between the single-site and multi-site cases. This was due to specific site characteristics, which were not easily detectable in the multi-site assimilation and the processes embedded in the model. For example, the sites, AU-DaS, CN-Sw2, FR-Pue, and US-Kfs, are dominated by either grassland or cropland (or both). Water transport processes, however, were difficult to simulate at these sites by our optimization process since they are strongly affected by human management processes, such as irrigation, harvest, or grazing, which are not represented in the model. Another complicated case for our assimilation approach can be seen at the site, CA-Gro, which is located in North America and dominated by a cold climate. The soils at this site are subject to freezing/thawing conditions and snow cover for some periods of time. The associated cold region's soil hydrology is complicated and not fully represented in the model. When soil moisture and FAPAR were simultaneously assimilated ('sm+fapar'), model parameters related to soil hydrology processes were constrained more than the ones related to phenology processes.