## 4 Discussion  
The domain of remote sensing is rapidly evolving and is facilitating profound crop management possibilities. Nonetheless, methods applicable to certain crops may not necessarily be suitable or effective for others, highlighting the need for designing and implementing experiments tailored to the specific crop under study. Given the recent surge in interest around pistachios, numerous researchers have started leveraging unmanned aerial vehicles (UAVs) for pistachio management, employing aerial imagery for comprehensive field oversight. In this way, [PERSON] et al. [34] used UAVs equipped with multispectral imaging to track the crown characteristics (height, size, shape, and NDVI) of pistachio trees throughout the growing season and compared UAV-derived metrics with the corresponding measurements taken in the field, highlighting the value of UAV imaging for comprehensive and real-time orchard management. Similar to our research, the study reported the highest NDVI value in June, when most trees registered between 0.9 and 1, nearing NDVI saturation. [PERSON] et al. [35] developed a novel, cost-effective, and rapid methodology based on segmenting RGB images and subsequently measuring the vegetation planar area along with ground shadows to estimate the canopy volume of pistachio trees by combining UAV-acquired imagery and machine learning techniques. They achieved very strong correlations with the actual canopy shape, demonstrating that is possible to effectively measure the amount of vegetation using UAV remote sensing. Unlike this study, the authors utilized spectral data solely for distinguishing soil, vegetation, and shadows, not for computing the vegetation index values of the pistachio canopies. [PERSON] et al. [47] assessed the spatial variations in water status and irrigation requirements in a large Californian pistachio farm using high-resolution thermal imagery obtained from an unmanned aerial system, with the CWSI calculated for individual tree crowns. The authors demonstrated that CWSI, derived from this thermal imagery, is a beneficial tool for assessing the spatial variability of crop water status within a commercial pistachio orchard and can be utilized for precision irrigation. Similarly, we found a high correlation between CWSI in both dates and pistachio tree irrigation. However, this correlation was slightly lower in the CWSI of September. Moreover, the correlation existing in June between CWSI and agronomic parameters disappeared in September. This might be due to the fact that the flight that occurred in September was nearer to the harvest date. Consequently, we hypothesize that as the harvest date approaches, the relevance of CWSI in pistachio decreases, while other VIs that are not reliant on thermal information, except for NDRE, maintain their effectiveness. Related to CWSI, our study highlights the significant influence of the sun's position, emphasizing its role in illuminating specific areas of tree crowns, namely warming them and, consequently, increasing the CWSI values. This phenomenon underscores the vital importance of taking the sun's position into account when interpreting the CWSI in agricultural research due to its substantial impact on the spatial distribution of the index. The sun's position during the day is a key factor that shapes how light interacts with tree crowns and vegetation, resulting in less illuminated leaves and shadows on the ground and within the canopy [35; 48], which can potentially affect the spectral information gathered from remote sensing techniques. In fact, [PERSON] et al. [49] suggest that there is a time window for accurate plant water stress mapping via CWSI-UAV thermography. Additional studies have also emphasized the role of temperature distribution in the CWSI. [PERSON] et al.