## 1 Introduction
The normalized difference vegetation index, or NDVI [15], is a well-known VI widely utilized for monitoring woody crops [16; 17; 18] due to its direct correlation with the vegetation present in the field [19]. Additional VIs, such as the green normalized difference vegetation index, GNDVI [20]; the normalized difference red edge index, NDRE [21]; and the crop water stress index, CWSI [22; 23], have proven beneficial in precision agriculture [24; 25; 26]. The CWSI, in particular, which relies on thermal imagery, provides a comprehensive approach to understanding crop water usage and stress, identifying temperature variations within and between crop canopies, and offering critical insights into plant transpiration rates and potential irrigation-related problems [27; 28; 29]. Several studies have centered on imaging techniques in relation to pistachios. For instance, [PERSON] et al. [30] created models based on Vis/NIR hyperspectral imaging and multivariate analysis for predicting the moisture content and textural features of pistachio kernels roasted under different conditions. [PERSON] et al. [31] assessed different multivariate classification methods applied to shortwave infrared range (SWIR: 1000-2500 nm) hyperspectral images for identifying contaminants in edible pistachio products. [PERSON] et al. [32] developed a rapid, nondestructive technique to detect green pea adulteration in pistachio nut granules using Raman hyperspectral imaging, principal component analysis, and partial least squares regression, and [PERSON] et al. [33] were able to distinguish between two types of pistachios (Kirmizi and Siirt) commonly grown in Turkey, using imagery and convolutional neural networks. However, the majority of these studiesconcentrate on post-harvest fruits, showing a knowledge gap regarding the use of images for managing pistachio cultivation in the field. Only a few studies have demonstrated the potential of using images for managing pistachio crops in the field. For instance, [PERSON] et al. [34] exploited the power of Unmanned Aerial Vehicles (UAVs) to capture the seasonal changes of pistachio tree crowns in a pistachio orchard, providing valuable insights into tree characteristics. [PERSON] et al. [35] developed an innovative method that leverages the shadows cast by the canopies of pistachio trees to estimate their volume in a cost-effective manner. In a similar vein, [PERSON] et al. [36] validated the effectiveness of NDVI as a reliable tool for identifying the vigor of pistachio trees. [PERSON] et al. [37] found that CWSI served as a sensitive indicator of water stress in pistachio trees, highlighting clear differences between well-irrigated and deficit-irrigated trees. Moreover, [PERSON] et al. [38] utilized NDVI data derived from Landsat-8 to compute various coefficients linked to pistachio water use, revealing distinct seasonal variations. While these studies have significantly progressed the field and explored the potential of imaging technologies in pistachio crop management, there still exists an evident gap in establishing a robust link between such imagery and two critical aspects of pistachio cultivation: yield and nut quality. Therefore, the novel contribution of the present study lies in exploring the relationship between multispectral and thermal images and the most important agronomic and quality parameters for pistachio cultivation. The focus of this work centers on harnessing the potential of UAV-based remote sensing that employs thermal and multispectral imaging technologies, with the aim of optimizing pistachio crop management, emphasizing three key factors: irrigation, yield, and quality. Intending to foster sustainable and efficient agricultural approaches in pistachio cultivation, this study strives to highlight the relationships between remote sensing techniques and the agronomic factors and quality features of pistachios.