## 3 Results  
First, we conducted a visual inspection of the generated VIs maps obtained through remote sensing (CWSI, NDVI, NDRE, and GNDVI), aiming to gain insights into the patterns and relationships among the examined VIs (Figure 4). Upon examining the CWSI values, it was observed that the vegetation values ranged from 0 to 1 and higher values were present on the south and east sides of the canopy crowns, due to the position of the sun. On the other hand, when analyzing the non-thermal-based VIs, the values ranged from 0.5 to 1 and an interesting observation across all maps was the positive relationship between the canopy size and the values of NDVI, NDRE, and GNDVI. In contrast, NDRE values were not as high, and there was greater variability within each canopy crown compared to NDVI. GNDVI, on the other hand, exhibited a pattern similar to NDRE, indicating that both indices capture comparable information about the overall vegetation growth. In our study, we employed principal component analysis (PCA, Figure 5) as a statistical technique to both reduce the dimensionality of our data and investigate inherent trends and relationships. Thus, the first component accounted for 33.8% of the total variability within the data set, which is a substantial proportion, considering it is just one of many potential components. Meanwhile, the second component contributed to explaining a further 21.2% of the total variability. Cumulatively, these two components alone were able to reveal more than half (approximately 55%) of the total variability within the data set. The visual representation of this PCA reveals clusters that distinguish between different treatments and geographical locations, showing that the data did not differ according to irrigation treatments (Figure 5a) but did differ according to plot location (Figure 5b). In a further exploration of our data, Figure 6 presents a biplot of the principal component analysis (PCA), where variables are grouped based on two different factors: irrigation  
Figure 4: Maps of the pistachio orchard. Top: (**a**) orthomosaic, providing a detailed visual overview. Bottom: vegetation indices, including (**b**) CWSI (crop water stress index), (**c**) NDVI (normalized difference vegetation index), (**d**) NDRE (normalized difference red edge), and (**e**) GNDVI (green normalized difference vegetation index). Figure 5: Principal component analysis (PCA) grouping the differences between (**a**) irrigation treatments and (**b**) plot location based on the vegetation indices, agronomic parameters, and nut quality at harvest. Two locations (”Moraleja” and “La Seca”) under two different irrigation treatments (control vs. high levels). and location. Upon conducting this biplot analysis, contrary to our observations from the previous figure, we noticed a distinct difference in treatment due to the irrigation applied in \"Moraleja\" plot. However, the \"La Seca\" plot maintained its consistent pattern, showing no discernible differences in relation to the irrigation. Turning our attention to the VIs, we observed that the NDVI in June correlates with control irrigation in \"Moraleja\". This index exhibited a close relationship with the GNDVI in June and September, as well as the caliber and certain fatty acids, such as oleic and stearic. Meanwhile, the NDVI in September was also associated with control irrigation, but here, the NDVI demonstrated a stronger correlation with the NDRE in June, as well as with the percentage of non-split nuts. Moreover, it maintained the relationship of June with the commercial size, oleic and stearic. In addition, NDRE in September correlated with calcium and magnesium content. On another note, both the CWSI in June and September were related to both control and high irrigation in \"La Seca\". The CWSI in June was more associated with carbohydrate levels and iron content, whereas the CWSI in September was correlated with linolenic and linoleic. The high irrigation treatment applied in \"Moraleja\" did not seem to correlate with any vegetation index.