## 2 Materials and Methods
### Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Dry Mass** & & **Nitrogen** \\ \hline \multicolumn{2}{c}{**N\({}_{\mathbf{VIP}}\)**} & **VIP** & **N\({}_{\mathbf{VIP}}\)** & **VIP** \\ \hline \multirow{2}{*}{\(\mathrm{D_{1}}\)} & \multirow{2}{*}{7} & B112 (0.780), B113 (0.742), B114 (0.633), B115 & \multirow{2}{*}{8} & B100 (0.732), B101 (0.754), B109 (\(-\)0.293), B112 (0.626), B113 (0.439), B127 (0.439), B128 (0.474), B135 (0.591) & \multirow{2}{*}{9} & B100 (0.732), B101 (0.754), B109 (\(-\)0.293), B112 (0.626), B113 (0.467), B114 (0.901), B115 (0.802), B127 (0.610) \\ \multirow{2}{*}{\(\mathrm{D_{2}}\)} & \multirow{2}{*}{5} & B101 (0.158), B112 (0.065), B113 (0.115), B115 (0.115) & \multirow{2}{*}{9} & B65 (0.685), B99 (0.343), B100 (0.130), B101 (0.182), B112 (0.266), B127 (0.333) & \multirow{2}{*}{10} \\  & & (0.266), B127 (0.333) & & (0.135), B113 (0.263), B114 (0.253), B115 (0.345), B127 (0.519) & \multirow{2}{*}{9} & B65 (0.780), B64 (0.767), B69 (0.719), B100 (0.255), B108 \\ \multirow{2}{*}{\(\mathrm{D_{3}}\)} & \multirow{2}{*}{9} & (\(-\)0.134), B112 (0.432), B113 (0.358), B114 & \multirow{2}{*}{11} & (\(-\)0.080), B190 (\(-\)0.242), B112 (0.526), B113 (0.441), B114 (\(-\)0.543), B115 (\(-\)0.427), B127 (0.183) & \multirow{2}{*}{10} & (\(-\)0.381), B115 (\(-\)0.243), B127 (0.232) \\ \multirow{2}{*}{\(\mathrm{D_{4}}\)} & \multirow{2}{*}{9} & B63 (0.545), B64 (0.588), B108 (0.249), B109 & \multirow{2}{*}{10} & B63 (0.435), B64 (0.477), B69 (0.527), B108 (0.136), B109 (0.192), B112 (0.669), B113 (0.661), B114 (0.233), B115 (0.187), B127 (0.579) \\ \multirow{2}{*}{\(\mathrm{D_{5}}\)} & \multirow{2}{*}{9} & B63 (0.474), B64 (0.469), B108 (0.189), B109 & \multirow{2}{*}{10} & B57 (0.278), B58 (0.420), B63 (0.547), B64 (0.538), B108 \\ \multirow{2}{*}{\(\mathrm{D_{5}}\)} & \multirow{2}{*}{9} & (\(0.124\)), B112 (0.287). B113 (0.259), B114 & \multirow{2}{*}{11} & (\(0.253\)), B109 (0.190), B112 (0.340), B113 (0.317), B114 (\(-\)0.168), B115 (\(-\)0.232), B127 (0.215) & \multirow{2}{*}{12} \\  & & & & (\(-\)0.211), B115 (\(-\)0.280), B127 (0.280) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variables used for regression determined via VIP scoring. NVIP stands for number of VIP variables. in the prediction of the nitrogen content at D\({}_{2}\) and D\({}_{3}\). Starting from D\({}_{4}\), it was possible to make reasonable predictions, even with a significantly higher approximation with respect to active learning. The random sampling test has been evaluated through calculation of the maximum RMSE and the average has been evaluated. In the first case, the range was between 7.67 kg/ha and 13.4 kg/ha. In the second case it was between 4.71 kg/ha and 7.93 kg/ha. The GSx approach returned an average RMSE of 5.42 kg/ha. In Figure 6, the Pearson coefficient trends, with respect to available biomass (Figure 6a) and nitrogen (Figure 6b), data are reported. It is remarkable that the higher RMSE was registered for the acquisition, the one made on 14 October 2014 (see the orange line in the graphs) shows the lower correlation in the vegetation indices area. In Tables 2 and 3 it is also reported the number of samples selected by the proposed active learning technique. Clearly, in an operational environment, the samples marked as informative for the estimation of all the biophysical parameters under investigation are collected simultaneously. With the help of Figure 7, it is possible to have a more precise idea about the amount of sampling necessary to implement the proposed framework. In this graphics, each rectangle represents a plot. Each of them is divided in five parts, each one representative of an available acquisition. Sub-parts colored in green stand for selected samples. Figure 7a represents the sampling schema for biomass estimation, while Figure 7b depicts the one for nitrogen content investigation.