## 4 Discussion
In June, the index most correlated with CWSI was NDRE, while in September, NDVI showed the highest correlation, although GNDVI also demonstrated a significant correlation. These relationships were echoed in the results, as NDRE in June showed similar correlations to CWSI with respect to pistachio parameters, while NDVI and GNDVI showed comparable values in September. On a more detailed level, the examination of VIs reveals similarities in several patterns exhibited by NDVI, NDRE, and GNDVI, which aligns with findings from other studies. These studies observed that NDVI correlated well with biomass and other agronomic parameters in crops, as did NDRE [56]. However, our research indicated that NDVI values were consistently higher and near \"1\" across all canopy crowns, demonstrating less variability between different tree crowns and within the same crown compared to NDRE and GNDVI. This can be attributed to the well-known NDVI disadvantage of reduced sensitivity when crop biomass exceeds a certain threshold, resulting in saturation at high biomass values [52]. Moreover, in certain circumstances, the NDVI saturation issue may be exacerbated by eliminating soil background contamination from images and concentrating solely on the canopy [57], which is often the case when working with high-resolution UAV imagery, allowing for precise tree crown segmentation, as in this study. [PERSON] et al. [26] also identified differences between NDVI, GNDVI, and NDRE, showing a strong correlation between NDVI and GNDVI values but not NDRE. Similar to these results, we did not find correlations for NDRE in September. These authors suggested that utilizing NDRE instead of the conventional NDVI could be particularly beneficial in detecting growth inhomogeneities in crops. These differences could be because NDRE is similar in the calculation to NDVI but incorporates a red-edge band instead of red, making it more resistant to the saturation problem inherent to NDVI and more sensitive to variations in chlorophyll content. In fact, [PERSON] et al. [58] emphasized the red-edge band's significance as a crucial spectral region for both leaf area index (LAI) and chlorophyll content, indicating that NDRE is a sensitive vegetation index for mapping both chlorophyll and LAI. [PERSON] et al. [59] conducted a study in which they found that VIs based on the red-edge (RE) spectral band exhibit heightened sensitivity to chlorophyll content, facilitating the creation of empirical models for estimating leaf area index across a diverse range of crops. Additionally, [PERSON] et al. [60] employed four red edge-based indices (CCCI, MTCI, NDRE, and CIred edge), finding that these indices performed better across bandwidths for estimating plant nitrogen uptake compared to the NDVI for maize nitrogen status. [PERSON] et al. [61] discovered that using the same platform and sensor, NDRE provided measurements sensitive enough to inform nitrogen fertilizer management in their system, while NDVI was more limited in assessing nitrogen status and predicting grain yield in rice cropping systems. Conversely, [PERSON] et al. [62] asserted that the effectiveness of VIs that rely on the RE band is limited when it comes to accurately quantifying vegetation ground cover, as the utilization of the RE band in VIs leads to a decreased sensitivity in the quantification of vegetation ground cover. They attributed this to the unsatisfactory performance of the RE band when employed individually, observing that the RE band negatively impacts the accuracy of the VIs that incorporate it, such as the NDRE index. Nevertheless, in our study, we identified that not only NDRE but also GNDVI showed different patterns than NDVI. [PERSON] et al. [63] observed similar outcomes, suggesting that the saturation of NDVI at low Chlab levels is due to its use of red reflectance, as opposed to GNDVI green reflectance and NDRE red-edge reflectance, which were less impacted by Chlab absorption.