## 3 Results
### Eddy Covariance and Optical Data  
Figure 3 shows the optical and flux data acquired during the flight campaign periods. _NDVI_ and _PRI_ data are shown in the first two rows (Figure 3a,b). Indices derived from field spectroscopy data  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Tower** & **Grass (\%)** & **Trees (\%)** & **Soil and Roads (\%)** & **Water and Shadows (\%)** \\ \hline
5 May 2011 & CTRL & 75.15 & 18.39 & 1.83 & 4.64 \\
4 October 2012 & CTRL & 68.42 & 18.47 & 1.56 & 11.55 \\
8 April 2014 & CTRL & 74.17 & 19.41 & 1.21 & 5.21 \\
8 April 2014 & N-ADD & 74.81 & 17.40 & 1.36 & 6.43 \\
8 April 2014 & NP-ADD & 71.28 & 21.55 & 1.08 & 6.09 \\
23 April 2015 & CTRL & 77.13 & 17.18 & 1.04 & 4.65 \\
23 April 2015 & N-ADD & 77.37 & 15.42 & 1.05 & 6.16 \\
23 April 2015 & NP-ADD & 75.10 & 17.45 & 0.62 & 6.83 \\
3 July 2015 & CTRL & 79.49 & 13.83 & 1.38 & 5.30 \\
3 July 2015 & N-ADD & 79.08 & 15.41 & 1.53 & 3.98 \\
3 July 2015 & NP-ADD & 75.03 & 17.46 & 0.97 & 6.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency of the different classes in the airborne imagery.  
Figure 2: Mean spectra of different covers within a 1 km\({}^{2}\) square centered on the Eddy Covariance (EC) towers: (**a**) Grass; (**b**) Trees; (**c**) Vegetation (grass + trees); and (**d**) All covers.  
acquired during several ground campaigns in 25 m \(\times\) 25 m plots are shown as example of the temporal dynamics in the ecosystem. In addition, the SVI derived from the mean spectra of \"trees\" and \"grass\" covers in the 1 km\({}^{2}\) square centered on each tower (Figure 2) are shown. \"Grass\" _NDVI_ derived from ground and airborne sensors are similar, and vary more than trees _NDVI_ along the year. \"Grass\" _NDVI_ derived from CASI and ground measurements were strongly related (\(r\) = 0.99, \(p\) < 0.05), whereas the same relationship was weaker for _PRI_ (\(r\) = 0.69, \(p\) < 0.05). CASI \"grass\" _PRI_ was related positively with _NDVI_ (\(r\) = 0.74, \(p\) < 0.05); which was confirmed by field spectroscopy data (\(r\) = 0.88, \(p\) < 0.05). On the contrary, the same indices presented a negative relationship (\(r\) = -0.69, \(p\) < 0.05) in CASI \"trees\". For all the \"vegetation\" pixels, both indices were positively correlated (\(r\) = 0.65, \(p\) < 0.05). The four formulations of _PRI_ presented different degrees of correlation with the original index. For \"grass\" (and \"vegetation\") pixels, all the indices were significantly correlated (\(p\) < 0.05); _PRI_ was positively correlated with _PRI\({}_{515}\)_ and _PRI\({}_{\rm norm}\)_, and negatively with _CPRI_. For \"trees\" pixels, _PRI_ was positively related with _PRI\({}_{\rm norm}\)_, and negatively with _PRI\({}_{515}\)_ and _CPRI_ (\(p\) < 0.05).  
Figure 3c-f shows the yearly trends of daily meteorological variables: daily cumulated _GPP_ (_GPP_d) (Figure 3c), daily mean \(T_{\rm air}\) (\(T_{\rm air,d,mean}\)) (Figure 3d), and _VPD_ (_VPD_\({}_{\rm d,mean}\)) (Figure 3e). _GPP_d peaks in spring, reaches minimum values during the summer and recovers during autumn after the onset of the rainfall. There is a large inter-annual variability in _GPP_; 2012 was the  
Figure 3: Optical (spectral vegetation indices), flux (daily accumulated _GPP_) and meteorological (daily mean air temperature (\(T_{\rm air}\)), vapor pressure deficit (_VPD_) and daily accumulated precipitation) data acquired in the site of Majadas del Tiétar in the years of the airborne flight campaigns.  
precipitation in winter and spring and \(GPP_{\text{d}}\) presented low values, except for a brief rise in spring. Autumn precipitation triggered a recovery of the vegetation activity more pronounced than in previous years. More precipitation was observed in spring in 2011, 2014 and 2015; this was followed by a quick drought in late spring/early summer and then by a later recovery after autumn rains. Some differences in \(GPP_{\text{d}}\) can be observed between the three towers in 2015, which are larger after nutrient addition [(37)].