## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Satellite Data  
The seasonal vegetation health and water status dynamics were analyzed using the NDVI, SAVI, and NDWI derived from Landsat 8 and 9 satellite imagery collected during the 2021 and 2022 growing seasons. Given the 30 m spatial resolution of Landsat 8 and 9, each pixel may contain a mixture of avocado canopy, bare soil, and surrounding vegetation. To address this, we focused on homogeneous orchard blocks and computed the standard deviation of the vegetation indices to assess the pixel variability. This approach allowed us to estimate the pixel heterogeneity and minimize potential biases. The trends in these indices were analyzed in conjunction with soil moisture data and local climatic conditions to provide a comprehensive understanding of crop responses to water stress across the orchard. By focusing on uniform orchard blocks, we ensured consistency between the satellite data and field conditions. This ensured that the indices were representative of the entire orchard, minimizing the impact of heterogeneous areas. It is important to note that Landsat 8 and 9 imageries were exclusively used to derive the vegetation indices (NDVI, SAVI, NDWI) for monitoring crop health and water status. Soil moisture estimates from in situ sensors and ERA5 reanalysis data provided complementary ground-truth information. To mitigate the limitations of Landsat's 30 m resolution, we focused on uniform orchard blocks and complemented the satellite data with field measurements. For each index, satellite-derived values were interpolated to daily measurements to track temporal variability. The standard deviations of these values were calculated to assess spatial variability within the study area, highlighting heterogeneous vegetation responses to environmental conditions. The daily vegetation index values and their standard deviations were graphed to illustrate the temporal dynamics and variability of vegetation health and water availability across the growing seasons. The NDVI, a primary index for vegetation vigor and chlorophyll content, was calculated using the following formula [31; 32]:  
\[\text{NDVI}=\frac{\text{NIR}-\text{Red}}{\text{NIR}+\text{Red}} \tag{1}\]  
Figure 1: **(a)** The geographical location of the study site; **(b)** a high-resolution satellite image of the orchard. Both maps include latitude and longitude references in degrees (WGS 84/EPSG:4326) to ensure spatial accuracy. where NIR and Red represent near-infrared and red reflectance values, respectively. High NDVI values indicate dense and healthy vegetation. To account for soil reflectance, particularly in areas with partial canopy cover, the Soil-Adjusted Vegetation Index (SAVI) was employed [33; 34]. The SAVI is calculated as follows:  
\[\text{SAVI}=\frac{(\text{NIR}-\text{Red})(1+\text{L})}{\text{NIR}+\text{Red}+ \text{L}} \tag{2}\]  
where L is a correction factor set to 0.5 for intermediate canopy cover conditions. This adjustment improves the accuracy of vegetation monitoring in heterogeneous landscapes. The Normalized Difference Water Index (NDWI) was calculated to estimate the canopy water content using the following formula [35; 36]:  
\[\text{NDWI}=\frac{\text{Green}-\text{NIR}}{\text{Green}+\text{NIR}} \tag{3}\]  
where Green and NIR represent green and near-infrared reflectance values, respectively. The NDWI is highly sensitive to the water content within the canopy and offers insights into plant water status. Figure 2 illustrates the spectral coverage and complementarity of the relevant Landsat 8 and Landsat 9 bands, highlighting the bands used to calculate the NDVI, SAVI, and NDWI, with their corresponding wavelength ranges. The satellite imagery was preprocessed using the Google Earth Engine (GEE) platform to ensure data reliability and consistency. This preprocessing involved cloud masking, radiometric calibration, and atmospheric corrections. Cloud masking was applied using the Landsat surface reflectance QA band to exclude pixels affected by clouds and shadows.