## 4 Discussion
### Implications for Agricultural Water Management  
Scalability of Satellite-Based Approaches  
The findings from this study suggest that satellite-based approaches for monitoring crop water stress and optimizing irrigation management can be effectively applied to other tropical crops or regions facing similar water management challenges. Integrating satellite-derived vegetation indices, such as the NDVI, NDWI, and SAVI, with ground-based soil moisture sensors has proven to be a reliable method for assessing water stress in avocado orchards, and this methodology holds excellent potential for broader application. Tropical crops such as bananas, citrus, and mangoes, which similarly experience high water demands and are sensitive to water stress, can benefit from these techniques. By adapting this approach to different crops, agriculturals can improve water-use efficiency and crop productivity in water-scarce regions, contributing to sustainable agriculture.  
Although this study was conducted on avocado orchards, the presented methodology provides valuable insights for other crops in water-scarce areas, particularly in tropical regions. We recognize that different crops have unique growth cycles and water requirements, which may influence their response to water stress. However, the relationship between vegetation indices (such as the NDVI) and water indices (such as the CWSI) remains consistent across many crops, including other tropical species, based on general physiological principles governing plant water stress. For instance, many tropical crops share similar responses to water deficits, which makes the model adaptable to other crops with some calibration. To enhance the applicability of this study to other crops, future research should focus on calibrating the model for various tropical species, taking into account their specific growth cycles and water needs. This would involve validating the model against various crops under varying climatic conditions, allowing for fine-tuning of the parameters to ensure that crop-specific characteristics do not negatively impact the model's effectiveness. Additionally, we propose further field trials to test the model's scalability to a broader range of tropical crops, ensuring its robustness and adaptability.  
Although this study primarily relied on vegetation indices and soil moisture sensors to assess water stress, we recognize that additional factors such as soil texture, topography, and microclimate variations influence crop water availability. Future studies could benefit from incorporating high-resolution soil maps and localized meteorological models, enhancing the precision of water stress assessments and irrigation recommendations. Such approaches would enable more site-specific hydrological analysis, thus improving precision agriculture decision-making.  
Moreover, the scalability of this approach is supported by the availability of global satellite data, such as Landsat 8 and 9 imageries, which offer free access to consistent, high-resolution data for vegetation monitoring. This cost-effectiveness makes satellite-based methods an attractive alternative to traditional, resource-intensive irrigation manage ment systems, especially in regions where financial and logistical constraints hinder the widespread use of ground-based sensors. With minimal field validation required, satellite-based data can provide large-scale insights into water dynamics, allowing for real-time adjustments to irrigation schedules across vast areas, potentially improving water resource management at the regional or national level.