# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 1 Introduction  
Water availability is the main limiting factor for the growth and yield of crops in the Mediterranean region, which is expected to undergo dramatic changes in temperature and precipitation due to climate change [1, 2]. Climate change will also likely increase the frequency of heat waves and prolonged periods of drought [3, 4], inducing uncertainties about the potential productivity of crops. Irrigation can help to mitigate the impact of climate change on perennial crops, including olive trees. Although olive trees are drought tolerant, many studies showed the beneficial effects of irrigation on vegetative growth, yield components, and oil quality [5, 6, 7, 8, 9, 10]. An important aspect in irrigation management is to understand the multiple interactions between water availability and soil properties or canopy vigour, which may potentially cause variations in yield and vegetative growth. In this respect, site-specific management of inputs, such as fertilizers and irrigation water, requires the understanding of the spatial distribution of soil characteristics, tree vigour, and productivity. Homogenous zones within the orchard for tree vigour and soil properties can be identified by soil sampling protocols and by monitoring parameters of vegetative growth (e.g., canopy volume increment, trunk growth rate), albeit at a high cost of money and labor [11, 12]. Recently, [PERSON] et al. [13] developed a predictive model based on soil features to identify homogeneous zones within olive orchards, but the precision and the accuracy of these maps were strictly related to the number of soil samples analysed. The use of remote and proximal sensing technologiesfor estimating field variability is becoming more and more common in precision agriculture due to their relatively lower cost and the non-invasive approach with respect to conventional methods [14; 15; 16]. Among proximal soil sensing (PSS) technologies, electromagnetic induction (EMI) has been the most commonly used for the last 20 years [17; 18]. Several authors used EMI sensors to map soil features and homogeneous zones in vineyards and orchards [19; 20; 21]. These sensors measure the apparent electrical conductivity (ECa), which is closely related to soil texture, coarse fragments, salinity, and moisture [17]. Mobile gamma-ray spectroradiometry is an additional promising PSS technique for precision agriculture [22; 23; 24]. Gamma-ray spectroradiometry measures natural \(\gamma\)-emissions from the main radionuclides (40 K, 238 U, and 232 Th) and the total emissions from all elements in the soil depth of the first 0.3-0.4 m. The concentrations of these \(\gamma\)-rays emitters are strongly related to the mineral composition of the parent material, but also to clay percentage and superficial stoniness [22]. In the absence of soil salinity, ECa responds primarily to the amount of clay, moisture, and bulk density, while gamma-ray spectroradiometry is more influenced by the variation in parent material mineralogy [14]. Homogeneous zones for precision orchard management can also be identified based on geometrical and spectral canopy information derived from aerial images. Remote sensing techniques have recently been shown to be effective in estimating tree vigour and canopy geometry as an alternative to the on-ground field measurements in orchards and vineyards [25; 26; 27; 28; 29]. In previous studies aerial images and spectral indices were used to determine the canopy projection in the olive orchard and to derive relationships with soil and tree structures to be applied in site-specific management [30; 31]. Recent studies also focused on predicting yield based on geometrical and spectral canopy characteristics [32; 33]. Despite the advantages provided by the above-mentioned technologies for soil and crop sensing, different authors suggest a combined use of remote and proximal sensing data as a much more reliable approach to identify homogeneous zones in orchards and vineyards [34; 35; 36].