## 1 Introduction  
Site-specific management is one of the main drivers of precision agriculture (PA). Spatial and temporal variability can be deployed to support management decisions and improve the profitability and sustainability of agricultural production, taking advantage of information and communications technologies [1]. Soil properties, vegetation or yield features can be used to delineate homogeneous management zones in order to apply site-specific management [2]. The identification of within-field variability is not a trivial task and can be supported by different technologies. Although homogeneous management zones are usually identified based on farmers' knowledge [3], it is common practice to take advantage of proximal or remote sensors [4], taking into consideration standardized and consistent data. Proximal sensors collect data from a distance less than 2 m from the target (e.g., soil or canopy) [5]. The most widely used proximal sensors in PA are yield monitors, weather stations [6], soil apparent electrical conductivity sensors [7] and vegetation spectrometers [8]. Proximal data provided by tractor-embedded sensors can be used to detect indirect suitable information to manage field variability (e.g., speed, engine power supplied, fuel consumption). Proximal sensors can collect high-resolution data (high number of samples per hectare). However, they require field surveys, which may be time-consuming and tricky to perform (e.g., after the emergence of arable crops). Conversely, remote sensors collect data from a distance greater than 2 m [9]. At such distances, sensors commonly use electromagnetic radiation. Different platforms equipped with remote sensors can collect a large amount of data in a short time, ensuring fast data acquisition over large areas. Remote sensors can be classified according to four main characteristics. The first is the spatial resolution, which identifies the minimum surface detectable by the sensors (in m or m\({}^{2}\) pixel\({}^{-1}\)). Spatial resolution directly affects the minimum area manageable with site-specific management. The second feature is the spectral resolution, which identifies the wavelength region to which the sensors are sensitive and the relative bandwidth (nanometers). The third characteristic is the radiometric resolution, which represents the number of values assumed by the reflectance when it is converted to an electrical signal by the sensors (in bits pixel\({}^{-1}\)). The last feature of remote sensors is the temporal resolution, or revisit time, which represents the number of images provided per unit of time. The temporal resolution of a remote sensor defines the frequency of agricultural field monitoring. Spectral resolution plays a key role in remote sensing applications in agriculture, since healthy vegetation's spectral signature is characterized by a specific reflectance peak in the near-infrared (800-900 nm) and lower reflectance values in the red region (650-750 nm). The majority of the vegetation indices (VIs) are based on these two wavelengths, thus VIs are usually retrieved from passive optical sensors, even if some application of active radar sensors exists [10]. The most commonly used VI for crop monitoring is the NDVI [11]. VIs are widely used in PA because of their correlation with biomass and Leaf Area Index [12]. Moreover, remote sensors and VIs have been profitably used for variable nitrogen management [13], identification of mechanization level [14], support of Common Agricultural Policy [15], crop stress detection [16], and field variability detection [17]. Remote sensors take advantage of different platforms, among which the most used in agriculture are satellites, planes, and Unmanned Aerial Vehicles (UAVs). VIs have been used to manage variable rate nitrogen application since the end of the 1990s, with the first application using active proximal sensors [18]. In 1999 the Yara's Research and Development Center developed a sensor (N-sensor, Yara International ASA, Oslo-Norway) to determine crop nitrogen demand.