## Methods
### Airborne and Ground Hyperspectral Data  
Airborne hyperspectral images were acquired using the Compact Airborne Spectrographic Imager CASI-1500i (Itres Research Ltd., Calgary, AB, Canada), operated by the Instituto Nacional de Tecnica Aeroespacial (INTA). Data were taken during campaigns between May 2011 and July 2015 supported by the projects BIOSPEC [38], FLUXPEC [39], CEOS-Spain [40], and the EUFAR TA DEHESyrE [41]. Eleven overpasses were selected for this work centered on the eddy flux towers (Table 1). The CASI sensor featured 144 spectral bands between 380 and 1050 nm, with a full width at half maximum (FWHM) of -5.5 nm. CASI field of view (FOV) is 40\({}^{\circ}\), providing approximately 0.90 m \(\times\) 1.66 m pixels, which were resampled to 1 m size in the geometric correction. The nearest neighbor resampling was used to prevent spectral mixture. Atmospheric correction was carried out by the data provider using ATCOR-4 TM and ancillary measurements of water vapor and aerosol optic thickness using a CIMEL CE318-NE sun photometer (Cimel Electronic, Paris, France) when available. Atmospheric correction was refined using the Empirical Line method [42] and ground spectral measurements of dark and bright calibration targets measured simultaneously to the overpasses using an ASD FieldpsecTM 3 spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO, USA).  
After atmospheric correction, images were classified using ENVI 5.1 + IDL 8.3.0 (Harris Geospatial Solutions, Boulder, CO, USA). Supervised classification based on Mahalanobis distance allowed discriminating the following classes: \"water\", \"grass\", \"trees\", \"shadows\" and \"soils/roads\", using both the noiseless Hemispherical-Directional Reflectance Factors (_HDRF_) between 425 and 980 nm and Spectral Vegetation Indices (SVI) (see Table S1 in the Supplementary Materials). Since spectral calibration of the airborne sensor varied between campaigns, _HDRF_ at the center of the bands used for hyperspectral SVI computation was estimated by linear interpolation. In addition,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **GMT Time** & **Tower** & **Flight Azimuth (\({}^{\circ}\))** & **Solar Azimuth (\({}^{\circ}\))** \\ \hline
5 May 2011 & 10:31 & CTRL & 120 & 170.1 \\
4 October 2012 & 11:23 & CTRL & 118 & 127.2 \\
8 April 2014 & 12:31 & CTRL & 127 & 163.2 \\
8 April 2014 & 12:38 & N-ADD & 74 & 182.6 \\
8 April 2014 & 12:55 & NP-ADD & 75 & 186.1 \\
23 April 2015 & 11:47 & CTRL & 75 & 182.6 \\
23 April 2015 & 11:55 & N-ADD & 75 & 162.1 \\
23 April 2015 & 11:29 & NP-ADD & 74 & 166.4 \\
3 July 2015 & 11:25 & CTRL & 74 & 153.8 \\
3 July 2015 & 11:35 & N-ADD & 84 & 138.1 \\
3 July 2015 & 11:07 & NP-ADD & 66 & 143.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the eleven overpasses of the Compact Airborne Spectrographic Imager (CASI) imagery acquired in the Majadas del Tétar research area.  
spectral measurements of 25 m \(\times\) 25 m grass plots were obtained in the surroundings of the three towers simultaneously to the airborne overpasses. Field protocols are described in described in [PERSON] et al. [43]. Additionally, field spectral measurements were acquired in several dedicated campaigns between 2009 and 2015. For each flight campaign, some of the SVI computed from the field spectroscopy measurements collected in the grass plots close to the EC towers were compared to the \"grass\" airborne derived SVI averaged on a 1 km\({}^{2}\) square centered on each tower. The Pearson correlation coefficient (\(r\)) and the \(p\)-value (\(p\)) were used to assess the strength of the relationships.