## 2 Materials and Methods
### Assessment of Abiotic Stressors--Crop Water Stress Index  
Canopy temperature, while a valuable indicator of leaf water status [104; 105], remains susceptible to fluctuations induced by various environmental conditions, most notably radiative flux, air temperature, wind speed, and relative humidity [104]. Given the temporal variability in these environmental factors and their influence on crown temperature, a single-shot acquisition is not feasible and thus, normalizing canopy temperature becomes imperative to ensure meaningful comparisons [85]. The process of normalizing actual canopy temperatures involves utilizing high and low boundaries and subsequently calculating the Crop Water Stress Index (CWSI), a well-established approach in accounting for atmospheric condition variations [104; 106; 107; 108]. Within this framework, the CWSI is defined by the following equation: CWSI = (T\({}_{\rm canopy}\) \(-\) T\({}_{\rm wet}\))/(T\({}_{\rm dry}\) \(-\) T\({}_{\rm wet}\)), where T\({}_{\rm canopy}\) signifies the average temperature of the canopy and T\({}_{\rm wet}\) and T\({}_{\rm dry}\) correspond to the temperatures of reference surfaces denoting fully wet and completely dry conditions, simulating maximum and minimum leaf transpiration. The CWSI scale ranges from 0 to 1, signifyingwell-watered to severely stressed conditions, while the derivation of Tdry and Twet can be accomplished through either empirical or analytical methodologies [85]. We employed IRT to estimate the CWSI using a handheld infrared camera (Testo 875-li, Testo SE & Co. KGaA, Lenzkirch, Germany) securely mounted on a stable tripod to ensure precise positioning and stability during image capture. The infrared images were acquired before the olive harvesting season (late September) when the region had experienced a prolonged period of no rainfall for at least two months, from a lateral perspective at a standardized distance of 5.0 m and a height of 1.3 m, allowing for comprehensive cover of the entire tree canopy (Figure 2a). To minimize potential inaccuracies stemming from atmospheric composition [109], global radiation influences [110], and temperature fluctuations caused by solar radiation penetrating the tree canopy, image capture was conducted during morning hours. Subsequently, in the field, we undertook camera calibration by accounting for ambient temperature, relative humidity, and solar irradiance. This calibration process was facilitated through the utilization of a portable weather station and a solar radiation meter (Amprobe SOLAR-100). To further enhance the precision of our measurements, we conducted individual assessments beneath the olive tree canopies. Additionally, to ensure utmost accuracy, emissivity values for the olive tree leaves were set at a constant value of e = 0.98 [86]. We processed the collected infrared images using the TESTO IRSoft(r) software package (v. 4.3, Testo SE & Co., KGaA, Lenzkirch, Germany) to extract canopy temperature information. Initially, a temperature palette was applied to generate a comprehensive dataset of temperature values for the entire infrared image, which had a resolution of 160 \(\times\) 120 pixels. Each pixel within this image represented a distinct temperature, offering insights into the thermal adaptation of the tree canopy to prevailing environmental conditions. To isolate the tree canopy (ROI) and separate it from the background elements such as the sky, soil, terraces, and neighboring olive trees, we employed the ArcGIS Analysis toolbox (v. 10.2, ESRI Inc., Redlands, CA, USA).