## 1 Introduction  
Wheat provides 20% of the global population's calorie intake and a similar percentage of its daily protein [1]. The global average rate of yield increase for wheat is 0.9% per year, while predictions of increase in demand reach 2.4% [2]. This means that the rate of genetic improvement required in the near future is greater than those currently being achieved [3]. Well-focused high-throughput phenotyping will be crucially important in developing new avenues for genetic improvement [4]. When the number of lines is limited or the studies are conducted under controlled environments, this task should be afforded by technologies such as high-throughput phenotyping platforms [5]. However, the performance of breeding programs on crop yield and productivity must be assessed under natural conditions [6, 7]. Field-based phenotyping (FBP) is recognized as the only approach capable of delivering the required throughput and an accurate description of trait expression in real-world cropping systems [6, 8]. A review of the approaches available for FBP and sensors commonly used can be found in [PERSON] _et al._[7]. Nevertheless, and in spite of recent progress, FBP remains a bottleneck for future advance in breeding [9]. The development of improved varieties relies on the ability to identify the best genetic variations for advancement, but the spatial variability that is generally observed in fields and the interaction with the environment (G \(\times\)E) add more complexity to the analysis of crop performance [8]. Several features, related to crop physiology and agronomy, are involved, making difficult the identification of simple traits for phenotyping purposes [4]. Spatial variability must be taken into account when the results of phenotyping experiments are being evaluated. For this reason, plant breeders have been displaying a growing interest in high-resolution remote sensing for phenotyping purposes [5, 6, 9, 10]. Remote sensing has proven useful for monitoring vegetation in the context of plant phenotyping [9]. Most efforts in this field employ RGB (visible) or CIR (color infrared) cameras to produce high-resolution maps of the Normalized Difference Vegetation Index (NDVI) or pseudo-NDVI as an indicator of plant vigor and structure both from near-field and airborne scales [11, 12, 13]. The development of techniques and methodologies to assess crop performance by such structural indices has been favored by its direct link with the interception of solar radiation and thus with potential yield [14] along with the relatively simple and inexpensive sensors required to acquire the imagery. Nevertheless, under rainfed growing conditions in arid environments such as the Mediterranean area, the combination of limited availability of water and high temperatures, especially during grain filling, tends to seriously limit yields below their potential. In such environments, the unpredictable and variable rainfall season is largely responsible for the variation in yield [15]. Under these conditions, interactions between genotype, year, and location tend to mask the genetic variations affecting yield. This is the reason why the genetic advances in wheat yield in dry areas have not been as successful as in high-yielding environments [15]. In this context, hyperspectral remote sensing has the potential to provide much richer datasets by the collection of several narrow spectral bands that are sensitive to the absorption of specific photosynthetic pigments [16], which are also capable of quantifying the chlorophyll fluorescence naturally emitted by vegetation [17]. Evidence exists of the usefulness of hyperspectral data for phenotyping crops using hand-held spectroradiometers for near-field data acquisition [18, 19, 20, 21]. Nevertheless, the potential of hyperspectral cameras installed on manned or unmanned vehicles for plant phenotyping purposes remains uncertain.