## 3 Results
Finally, we performed [PERSON]'s correlation analysis (Figure 7), analyzing individually the relationships between the various VIs and the yield and quality parameters of  
Figure 6: Principal component analysis (PCA) of vegetation indices, including CWSI (crop water stress index), NDRE (normalized difference red edge), NDVI (normalized difference vegetation index), and GNDVI (green normalized difference vegetation index) at two different time points (June and September). Agronomic parameters include fresh and dry fruit weight per tree (Yield_FW and Yield_DW), number of bunches (Bunch) per tree, and average bunch weight (Yield_raw). The commercial caliber, the percentage of open husk, closed husk, and empty nuts (Caliber, Split, Non_Split, and Blank) and the weight of open husk, closed husk, and empty nuts per tree (DW_split, DW_non_split and DW_blank). Regarding quality, fiber, fat (Fat), carbohydrates (CBH), protein (PROT), calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K), sodium (Na), iron (Fe), zinc (Zn) and several fatty acids (palmitic, palmitoyl, stearic, oleic, and linoleic) are included. Data collected from two locations (”Moraleja” and “La Seca”) and their interaction with the different irrigation treatments are applied. pistachio crops. Our investigation revealed that VIs bear a considerable correlation with both the yield and quality of the pistachio harvest. Specifically, the VIs derived from the first drone flight, which correspond to the development phases of the nut (F1-F2), generally exhibited strong positive correlations with most yield and quality parameters. The exception was the percentage of split or blank nuts, which did not show a significant relationship with these early stage VIs (except for the CWSI, which showed a weak correlation). Upon closer inspection, we found that non-thermal VIs demonstrated high positive correlations with yield, especially for NDVI and GNDVI. However, these indices correlated less strongly with nutritional parameters, in contrast to NDRE. CWSI displayed a similar trend to NDRE and, although the correlations with yield parameters were significant, they were weaker, and CWSI exhibited significant correlations with numerous quality and nutritional parameters, such as carbohydrate percentage, magnesium, and various fatty acids, such as stearic, oleic, and linoleic acid. Turning to the VIs calculated from the images obtained during the second drone flight, we observed that NDRE did not show significant correlations with the pistachio traits. In general, only GNDVI showed a significantly strong correlation with agronomic and quality parameters. Nevertheless, NDVI still maintained significant correlations with certain quality parameters, such as Fe, CBH, and organic acids. CWSI did not correlate with agronomic parameters, but it still correlated with quality parameters better than non-thermal-based vegetation indices. In addition to [PERSON]'s correlation analysis, linear regression analyses were carried out (Figure 8) to show the strongest correlations between VIs and agronomic and quality parameters of pistachio. The highest correlations in June were found between NDRE and carbohydrates (r = 0.78), CWSI and oleic acid (r = 0.77), GNDVI and the number of bunches per tree (r = 0.62), and NDVI and carbohydrates (r = 0.52). On the other hand, the highest correlations in June were found to be more significant than the other ones. Figure 7: [PERSON]’s correlation matrix of the irrigation conditions, agronomic, nut quality traits, and vegetation indices, considering 20 trees at two different locations in the “Castilla y Leon” region. Asterisks indicate significant correlations (\({}^{*}\), \(p<0.05\), \({}^{**}\); \(p<0.01\); \({}^{***}\), \(p<0.001\)). Blue indicates positive correlations between two traits according to the color key and red negative correlations.