## 3 Results and Discussion
### Validation
#### 3.2.3 Verification of LAI and SWI  
The consistency over time is essential if long-term datasets are assimilated [1]; therefore, anomaly correlation of satellite observation with simulated data was performed for the period 2008-2015. Standard statistics such as correlation, BIAS and RMSE are calculated against the satellite information for both simulations. The monthly area-average LAI and SWI scores are illustrated in Figure 10. Year-to-year correlation ranged within very wide intervals, especially for open-loop simulation. The open-loop experiment produced a low correlation for LAI in every springtime repeatedly, especially in May, and these weak correlations disappeared in the summer or autumn period. In the assimilation, these low correlations did not appear, the extended Kalman filter was relatively stable and worked properly between 0.4 and 0.82 (Figure 10, top). Simulated LAI was controlled by soil moisture, which had high BIAS and RMSE at the beginning of the vegetation period (Figure 10, bottom). Similar to [7], a low correlation was produced by opposite anomalies occurring for some periods where the LAI anomaly in the model was driven by soil moisture over the domain. Besides, the start of the growing season generally occurred later in the model than in the observations. The strong seasonal dependency of the RMSE was notable with 1 m\({}^{2}\) m\({}^{-2}\) values from May to October. The correlation was also higher in these months. High RMSE and low correlation were achieved in 2014 by open-loop. LAI was underestimated by the simulations (negative bias), especially for the summer months, except for 2010 and 2014, when the amount of biomass was overestimated throughout the year (Figure 10, bottom). Under unusually wet conditions, the biomass production due to photosynthesis was overestimated by ISBA-A-gs.  
Figure 9: AnoLAI and AnoWG2/AnoSWI-10 time series for 2008–2015 at Hegyhátsál (OL: blue, ASS: orange, GEOV1 LAI and SWI-10: green, in-situ: yellow).  
The soil moisture correlation, BIAS, and RMSE of simulated SWI against ASCAT SWI are illustrated in Figure 11, respectively. SWI values were calculated from the simulations using WG2 (root-zone water content): SWI = (WG2 \(-\) WG2\({}_{\text{min}}\))/(WG2\({}_{\text{max}}\)\(-\) WG2\({}_{\text{min}}\)), where WG2\({}_{\text{min}}\) and WG2\({}_{\text{max}}\) were obtained from the model results for the period 2008-2015 ASCAT SWI-10 satellite data were used to calculate SWI scores. The weak SWI statistics can be explained by the weak vertical coupling of the model [1]. The assimilation run showed a higher correlation over the entire period compared to the open-loop (Figure 11 top). The improvement of assimilating SWI in the root-zone soil moisture was about 14%, as found in [1]. The RMSE was slightly reduced by the assimilation run, especially in spring periods.  
Figure 11: Seasonal correlation (**top**); BIAS (bottom, dashed line) and RMSE (**bottom**, full line)) for SWI for open-loop (blue) and assimilation (orange) runs.  
Figure 10: Seasonal correlation (**top**); BIAS (**bottom**, dashed line) and RMSE (**bottom** full line)) for LAI for open-loop (blue) and assimilation (orange) runs.