## 1 Introduction  
Nowadays, adequate crop management is crucial to ensure high and sustainable food production. The increasing world population needs higher productivity, but at the same time, reduces the availability of agricultural lands [1]. Moreover, the increased awareness of the environmental impact of fertilizers and water supplies is modifying farming practices and regulations with the objective of minimizing waste of resources [2]. As a result, according to the second and the twelfth Sustainable Development Goals, i.e., zero hunger and responsible consumption and production, agri-food systems are asked to meet the challenge of increasing productivity in a sustainable manner [1]. In this context, automation and digitalization are key, because they allow for optimizing all the phases of the agricultural cycle, from souring to harvesting. However, while automation, i.e., the exploitation of machines able to help and outperform humans in production tasks, has been widespread in agricultural practice for several years [1], digitalization, i.e., the creation and exploitation of digital twins of farmlands [3], is still limited. Digital twins are virtual equivalents of physical objects [4] with whom they are connected in real time. Their intrinsic dynamic nature includes the representation of the current behavior of real objects, as well as the prediction of their future state [5]. In the case of agricultural systems, the parameters to be collected for building such a representation are related to both the environment (such as air temperature, humidity, precipitations, etc.) and the vegetation. These are traditionally retrieved through field measurements that, despite their precision, become unsustainable in presence of distributed targets. Therefore, the exploitation of remote [6] and close-range sensing technologies is essential [7; 8; 9]. Indeed, resolution issues and the flexibility of close-range remote sensing platforms, with particular reference to the possibility of carrying different sensors, make drones the preferred tools for precision agriculture. In particular, hyperspectral imagery is mostly exploited because of the spectral resolution and detail it can offer [10]. The approaches developed to retrieve vegetation biophysical parameters from remote sensing data can be classified as physical-based or empirical [11]. In the first case, a model based on radiative transfer theory is inverted. This requires significant knowledge of the scene, which has to be adequately parametrized both at canopy structure and atmospheric levels, since the solution of the problem is to find the best match between the simulated signal and the measured one. In turn, empirical methods rely on the direct calibration of a relationship between the measured signal and the biophysical variable of interest. These methods are constrained by how representative the calibration data is compared to the behavior of the object to be modeled [11]. However, if significant training samples are provided, empirical methods, such as the partial least squares regression (PLSR) [12], are able to deliver accurate predictions of many variables of agricultural interest. Despite some studies raised concerns about the ability of such methodologies to model nonlinear relation between remote sensing data and, as an example, the biomass [13], the literature widely exploited PLSR to estimate quantities such as grapevine yield and berry weight [8], grassland biomass [14], nitrogen [9] and phosphorus [15] content and carotenoids content in cotton crops [16]. Using empirical methods, the selection of significant samples is fundamental for model calibration. In the literature, this concept is usually expressed as active sample selection or active learning [17]. Its purpose is to extend the forecasting capability of an existing training dataset by adding a limited number of samples from newly available acquisitions [9; 18] in order to allow new predictions with limited field work.