## 4 Discussion
### Reference Evaptranspiration (ETo) Estimates  
The comparison of three ETo models--Hargreaves-Samani, Blaney-Criddle, and Priestley-Taylor--revealed notable differences in their performance in estimating the water demand of the avocado orchard. Each model employs distinct approaches and parameterizations, influencing their applicability and accuracy under varying environmental conditions. Our analysis indicated that while all three models can estimate ETo, their performance varies depending on the local climatic factors and the available meteorological data. The Hargreaves-Samani model, based primarily on temperature data, showed higher variability in its ETo estimates, particularly during rapid temperature changes. While commonly used in areas with limited meteorological data, its performance in tropical conditions revealed limitations, particularly in capturing the nuances of water demand during the wet and dry phases of the growing season. Similar observations were made by the authors of [67], who indicated that while the Hargreaves-Samani model is accurate and uses readily available data, its accuracy is low when geographical and climatic conditions vary. However, this model remains helpful in regions where high-quality wind speed and humidity data are unavailable. This is similar to what was observed by the authors of [68], who indicated that this model is very precise for the estimation of ETo, especially if it is used in combination with agrometeorological information from ERA5-land, which is an enhanced version of ERA5. The Blaney-Criddle model, which relies on temperature and daylight hours, provided a more consistent estimate of ETo throughout the growing season. This model is more straightforward and effective in regions where solar radiation data may not be available, making it a practical choice for areas with limited meteorological infrastructure. However, its estimates could have been more precise during extreme conditions, such as rapid changes in temperature or varying precipitation, highlighting its limitations under tropical conditions. The Priestley-Taylor model, like the Blaney-Criddle model, showed lower variability in its estimates compared to the Hargreaves-Samani model. This aligns with findings by the authors of [69], who noted that the coefficients of this model displayed little variation across four regions, suggesting its potential for ETo calculation. It uses solar radiation as the primary factor for evapotranspiration, making it well suited for humid regions where energy-driven evapotranspiration dominates. However, its accuracy during drier months could have been more reliable, as it needed to fully account for changes in water availability driven by temperature fluctuations and relative humidity. This limitation underscores the importance of selecting the most appropriate model based on the local climate and available meteorological parameters. In practice, accurate ETo estimates, particularly from the Blaney-Criddle and Priestley-Taylor models, combined with the Kc values derived from the NDVI, provided a robust framework for estimating the crop water demand in the avocado orchard. This study was able to estimate crop water needs more precisely across different phenological stages by integrating satellite-derived vegetation indices with reference evapotranspiration values. This combination offers a more data-driven approach to irrigation, allowing for real-time adjustments based on the crop's actual water needs and environmental conditions. Each ETo model in this study presents strengths and limitations depending on the climatic conditions. The Hargreaves-Samani model, while suitable for data-limited regions, may underperform in humid conditions where additional climatic factors like humidity and wind speed play a role. The Blaney-Criddle model provides a stable estimate but may lack responsiveness to sudden changes in precipitation and temperature. The Priestley-Taylor model performs well in humid environments but may not fully account for advection effects in dry conditions.