## 3 Results
### Validation of Soil Moisture  
Both surface and root zone soil moisture model estimates are validated in this experiment against an independent dataset (i.e., ISMN). In Noah-MP, SSM provides water content estimation at the top 10 cm of the soil layer, whereas RZSM provides estimation in the top 100 cm of the soil layer. ISMN provides data at different depths from 2 cm to 100 cm. The data from 10 cm and 100 cm are compared with SSM and RZSM of model outputs, respectively.  
First off, the NIC of NCRMSE for SSM is computed at each ISMN station (Figure 7). The number of stations that present an improvement in NCRMSE due to DA is maximum for the joint assimilation case (39). The NIC of NCRMSE does not show any clear spatial dependence, although the SMAP DA seems to be particularly effective in the western US, which also translates into the joint DA results. Although GLASS LAI DA degrades in a couple of stations in the northwestern US, SMAP DA can improve the performance at those locations, which also shows an improvement after applying the joint DA.  
Figure 7: NIC of NCRMSE between different DAs and OL with respect to ISMN for SSM over CONUS from 2016 to 2018. Blue (red) color indicates improvement (degradation) after the application DA. The number of stations presenting an improvement/degradation thanks to DA are reported on each map.  
In terms of ACCs, the number of stations improved by the DA technique is higher than the number of stations showing a degradation (Figure 8). GLASS LAI DA increases the SSM ACC at more stations (37) than the one obtained though the joint DA (29). Although some stations only present a slight improvement thanks to the SMAP DA (such as the four located in New Mexico) or even a degradation due to the LAI DA (the northern station in New Mexico), in the joint assimilation run, their ACCs are better than the original OL ones (i.e., blue dots in the bottom panel).  
The same analyses are then performed for RZSM. Figure 9 shows the NCRMSE NIC between OL and DA, with respect to the ISMN stations. Among the 67 stations, SMAP DA shows an improvement at 41 locations, similarly to the joint assimilation (which improves the OL NCRMSE of RZSM at 38 stations). As for SSM, SMAP DA improves most stations in the western US, where GLASS LAI DA cannot fully improve the OL RZSM. Once  
Figure 8: Difference in ACC between different DAs and OL with respect to ISMN for SSM over CONUS from 2016 to 2018. Blue (red) color indicates improvement (degradation) after the application of DA. The number of stations presenting an improvement/degradation thanks to DA are reported on each map.  
again, the joint assimilation shows a combination of these results, by improving the model performance in almost all the western locations. This is particularly interesting, since, as highlighted above, RZSM is not directly assimilated within Noah-MP. Nevertheless, the model physics transfer the SMAP surface soil moisture information to lower layers, which impacts the estimation of root zone soil moisture, by improving the RZSM NCRMSE at more than 60% of the total stations.  
Figure 10 shows ACC differences between DA and OL for RZSM, where correlations are once again computed against the ISMN dataset. Here, the number of stations showing an improvement is the highest after the application of SMAP DA (30), but this number decreases with the application of the LAI and joint DA (25). Moreover, the number of stations at which the ACC improves thanks to DA is smaller than the number of stations where ACC degrades, and this result is consistent for all the DA approaches proposed here.  
Figure 9: Same as Figure 7, but for RZSM.