## 4 Discussion
These results suggest that active sampling is beneficial, especially when the dimensionality of the problem is increased by several acquisitions, as it is able to include in the calibration set the samples mostly representative of the variability of the data. As for GSx, it can be argued that its performance is equivalent with that provided by the proposed entropy-based approach. However, GSx has one important parameter that should be set by the operator, i.e., the number of samples to be selected by active learning [56]. The experiments presented here have been implemented using the same number of samples output by the proposed approach, but it is in principle unknown. As the parameter affects the results of the calibration, this could be a serious drawback, especially in presence of extended targets. Moreover, GSx requires the projection of data in a homogeneous feature space for the computation of the distance between samples and this increases the computational complexity of the approach [56]. PLSR is a well-established linear statistical approach suitable for the analysis of multilinear spectral dataset, thus making full use of redundant information [62]. This technique has been widely exploited to estimate crop biophysical variables from hyperspectral remote sensing data [24; 25; 26]. However, the literature also revealed that the large amount of data usually exploited in PLSR may contain irrelevant information, which could reduce the performance of the technique [62]. To cope with this, the VIP score calculated following a tentative regression has been used as discriminator and only the variable exhibiting a VIP value higher than one have been used for the final analysis [52]. Reference [9] allows for an analogy with the results here reported as its authors made available the data exploited for this study. However, significant differences in the methodology are present. In reference [9], the active sampling procedure relies on a technique developed in [21], opportunely reworked in order to improve the selection of the most representative samples. Two important aspects to be remarked about this framework are the necessity of supervision in active learning and the fact that it is based on predictive models already available, which are retrieved via bootstrapping [63]. In other words, the prediction model constituting the base for active sampling is built by splitting the calibration set in two parts. The first part is used to fit the prediction, while the second for validation. This process is repeated several times changing the composition of the two subsets. The final model is the one delivering the lowest RMSE. The main innovation introduced in this paper is the active learning technique which, exploiting Shannon's entropy as diversity criterion, allows for the set-up of an unsupervised methodology suitable for the implementation of an incremental monitoring framework starting from scratch. The obtained results are fully comparable with the literature, which mainly relies on supervised techniques needing an already available model for active sampling implementation. This makes the proposed methodology very well suited for operational environments, in which predictive models are usually not available for specific crop fields and automation is highly requested. In this context, a crucial aspect is represented by the amount of sampling necessary for the successful calibration of PLSR, as this could be a bottleneck for real-world implementation of monitoring activities. As reported in Section 3, the proposed methodology requires an initial massive sampling, as the entropy tend to increase when few calibration points are considered. However, as reported in Table 2, Table 3 and Figure 6, this behavior is strongly mitigated beginning with the second acquisition, in which the number of samples selected for calibration is almost halved and the trend is decreasing as soon new data are acquired.