# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 3 Results
### Impact of the Assimilation on SSM and LAI  
In the assimilation process, the available observations were merged with the model simulations, weighted with their errors, as defined in [12; 14], to compute an analysis taking into account all this information. It was expected that the analysis would end closer to the observations. Figure 2 shows the monthly average open-loop simulations, analysis and observations of SSM and LAI over the Euro-Mediterranean area. The SSM analysis was very close to the open-loop since the SSM observations were rescaled before being assimilated. Open-loop and analysis scores for the whole time period and over each pixel were similar, as described in Table 1. These scores were computed using pooled pixel data during one month, so they included both spatial and temporal components. Figure 2: Monthly time series of the observations (green), open-loop (blue) and analysis (red) simulations from 2007–2015 averaged over the Euro-Mediterranean area for: (**a**) Surface Soil Moisture (SSM) and (**b**) Leaf Area Index (LAI). The largest impacts of the assimilation process can be seen on the LAI variable. Figure 2 shows the LAI simulations and observations at the monthly scale. In summer and winter times, the open-loop simulations tended to underestimate LAI (compared to LAI observations), and the analysis simulations were closer to the observations after the assimilation. The statistics from Table 1 indicate a lower RMSD (\(-\)28%) and a better correlation R (+14%). Figure 3 shows seasonal scores for each month of all years from 2007-2015 (RMSD and R). They were computed using all the pixels and dates available for each considered month. These statistics show an improvement for both SSM and LAI analyzed variables for all the months of the year. For SSM, the RMSD was always reduced, with analysis values ranging from about 0.042 m\({}^{3}\cdot\)m\({}^{-3}\) in March and September to 0.054 m\({}^{3}\cdot\)m\({}^{-3}\) in May and December. Correlation was slightly improved in winter and spring and more markedly improved in summer and during the autumn, with analysis values ranging from 0.77-0.94. Because of the seasonal rescaling of SSM, the seasonality in the statistics cannot be improved. Regarding LAI, both RMSD and R scores were improved during the year with analysis RMSD and R values ranging from 0.3-0.9 m\({}^{2}\cdot\)m\({}^{-2}\) and from 0.77-0.91, respectively. The analyzed LAI was closer to the observations than the open-loop simulation throughout the year, in particular during the senescence phase where RMSD dropped from around 0.9 m\({}^{2}\cdot\)m\({}^{-2}\) (open-loop) to around 0.4-0.6 m\({}^{2}\cdot\)m\({}^{-2}\) (analysis) in September and October. A seasonality in the scores can clearly be seen with a larger open-loop SSM RMSD in summer and winter associated with a lower correlation in summer and larger open-loop LAI RMSD values in summer associated with a slightly better correlation in summer. After assimilation, the seasonality in the LAI correlation was smoothed out with R values between 0.78 and 0.90. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Variable** & **Exp.** & **bias** & **R** & **RMSD** & **SDD** & **No. of obs.** \\ \hline SSM & open-loop & 0.002 & 0.850 & 0.048 & 0.048 & \\ (m\({}^{3}\cdot\)m\({}^{-3}\)) & analysis & 0.004 & 0.860 & 0.046 & 0.046 & \\ \hline LAI & open-loop & \(-\)0.114 & 0.778 & 0.827 & 0.819 & \\ (m\({}^{2}\cdot\)m\({}^{-2}\)) & analysis & \(-\)0.084 & 0.884 & 0.594 & 0.588 & 1,558,568 \\ \hline E & open-loop & 0.015 & 0.883 & 0.533 & 0.533 & \\ (kg.m\({}^{-2}\cdot\)day\({}^{-1}\)) & analysis & 0.057 & 0.894 & 0.525 & 0.522 & 688,608 \\ \hline GPP & open-loop & \(-\)0.630 & 0.895 & 1.378 & 1.225 & \\ (kg.m\({}^{-2}\cdot\)day\({}^{-1}\)) & analysis & \(-\)0.562 & 0.916 & 1.233 & 1.098 & 384,480 \\ \hline SIF (mW\(\cdot\)m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)nm\({}^{-1}\)) & open-loop & - & 0.791 & - & - & 475,008 \\ compared to GPP & analysis & - & 0.813 & - & - & \\ \hline \end{tabular}
\end{table}