## 2 Materials and Methods
### Methodology
Therefore, it is common practice in the literature to assume as threshold for retaining informative variables a VIP score larger than one. This means that those variables have an above average influence on the building of the model explaining the observations [53]. The variables selected through VIP scoring are used for the final regression. In order to choose the optimal number of components [54], a leave-one-out cross-validation has been implemented [55]. This procedure consists in the building of a model leaving out from the calibration set one by one all the samples. In other words, if 20 samples are available, the model is built each time using 19 samples. The one left out changes at each iteration. The model built in such way is evaluated by using as test set the sample left out and the \begin{table}
\begin{tabular}{c c c c} \hline
**Index** & **Full Name** & **Formula** & **Reference** \\ \hline
124 & Normalized Difference Red & \(\text{NDRE}_{720,795}=\frac{\text{R}_{720}-\text{R}_{720}}{\text{R}_{750}+ \text{R}_{720}}\) & [43] \\  & Edge Index & \(\text{NDRE}_{750,70}=\frac{\text{R}_{720}-\text{R}_{750}}{\text{R}_{750}+ \text{R}_{750}}\) & [8] \\
126 & NIR—red edge—red normalized & \(\text{NRERE}=\frac{\text{R}_{720}-\text{R}_{720}}{\text{R}_{720}+\text{R}_{7 20}}\) & [8] \\  & difference vegetation index & \(\text{TVI}=0.5(120(\text{R}_{750}-\text{R}_{550})-200(\text{R}_{570}-\text{R}_ {550}))\) & [44] \\
127 & Transformed Vegetation Index & \(\text{MTVI}=1.2[1.2(\text{R}_{800}-\text{R}_{450})-2.5(\text{R}_{600}-\text{R}_ {540})]\) & [8] \\
128 & Weighted triangle & \(\text{EVI}_{850,660}=2.5\left(\frac{(\text{R}_{800}-\text{R}_{450})}{(\left( \text{R}_{800}+\text{R}_{800}-7.5\text{R}_{450}+1\right)}\right)\) & [43] \\
129 & Enhanced Vegetation Index & \(\text{EVI}_{800,670}=2.5\left(\frac{(\text{R}_{800}-\text{R}_{800})}{(\left( \text{R}_{800}+\text{R}_{800}-7.5\text{R}_{800}+1\right)}\right)\) & [8] \\
130 & Enhanced Vegetation Index & \(\text{LCI}=\frac{\text{R}_{800}-\text{R}_{800}}{\text{R}_{800}+\text{R}_{800}}\) & [45] \\
131 & Leaf Chlorophyll Index & \(\text{MTCI}_{\text{var}}=\frac{\text{R}_{800}-\text{R}_{800}}{\text{R}_{800}+ \text{R}_{800}}\) & [8] \\
132 & Modified Normalized Difference & \(\text{MTCI}_{\text{var}}=\frac{\text{R}_{800}-\text{R}_{800}}{\text{R}_{800}+ \text{R}_{800}}\) & [8] \\
133 & Vegetation Index & \(\text{NRI}=\frac{\text{R}_{800}-\text{R}_{800}}{\text{R}_{800}+\text{R}_{800}}\) & [46] \\
134 & Nitrogen Reflectance Index & \(\text{PRI}=\frac{\text{R}_{800}+\text{R}_{800}}{\text{R}_{800}+\text{R}_{800}}\) & [47] \\
135 & Spectral Polygon Vegetation Index & \(\text{SVPI}=0.4(3.7(\text{R}_{800}-\text{R}_{670})-1.2(\text{R}_{530}-\text{R}_ {670}))\) & [48] \\
136 & Simple Ratio & \(\text{SR}_{710}=\frac{\text{R}_{800}}{\text{R}_{800}}\) & [49] \\
137 & Simple Ratio & \(\text{SR}_{680}=\frac{\text{R}_{800}}{\text{R}_{800}}\) & [49] \\
138 & Ratio Vegetation Index & \(\text{RVI}=\frac{\text{R}_{800}}{\text{R}_{800}}\) & [50] \\
139 & Vogelmann Index & \(\text{VOG}=\frac{\text{R}_{800}}{\text{R}_{800}}\) & [51] \\
140 & Gitelson and Merzlyak index & \(\text{GM}=\frac{\text{R}_{800}}{\text{R}_{800}}\) & [8] \\
141 & Modified normalized difference & \(\text{MND}=\frac{\text{R}_{800}-\text{R}_{800}}{\text{R}_{800}+\text{R}_{800}+ 2\text{R}_{800}}\) & [8] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: Scatter plots of the predicted dry mass against ground data. (**a**) D\({}_{1}\) (R\({}^{2}\) = 0.831), (**b**) D\({}_{2}\) (R\({}^{2}\) = 0.002), (**c**) D\({}_{3}\) (R\({}^{2}\) = 0.350), (**d**) D\({}_{4}\) (R\({}^{2}\) = 0.485), (**e**) D\({}_{5}\) (R\({}^{2}\) = 0.267), (**f**) all data (R\({}^{2}\) = 0.923). The figures show the scatter plots of the predicted dry mass and nitrogen content against the corresponding ground data. In both cases, the last one, i.e., the graph (f), refers to the whole dataset.