## 5 Conclusions  
This study comprehensively evaluated satellite-based remote sensing technologies integrated with field-based soil moisture sensors for assessing water stress in avocado orchards. The key findings highlight the effectiveness of satellite-derived vegetation indices, such as the NDVI, NDWI, and SAVI, in monitoring vegetation health. The 30 cm depth sensor data consistently correlated strongly with satellite-derived soil moisture data. Integrating these tools enabled the detection of water stress dynamics, offering an innovative and cost-effective approach to irrigation management, especially in remote agricultural areas where traditional methods are often impractical.  
The results underscore the potential of using remote sensing technologies as a viable alternative to conventional irrigation monitoring systems. This study advances precision agriculture by showing how satellite-derived data and field-based soil moisture measurements can enhance water management practices, even in regions with limited access to high-resolution data and infrastructure. This approach offers a scalable solution for monitoring crop water stress across large areas, thus improving water-use efficiency and supporting sustainable agricultural practices, particularly in water-limited tropical regions. However, the study acknowledges certain limitations, such as the medium spatial resolution of satellite imagery and the limited coverage of field sensors. Future research should address these constraints by integrating higher-resolution satellite data, deploying additional field sensors, and refining the models with additional vegetation indices and climatic variables. Expanding the data's spatial and temporal coverage in future studies could enhance the accuracy of crop water stress assessments and improve irrigation scheduling. Therefore, this study highlights remote sensing technologies' promising role in avocado orchards' water stress assessment and irrigation management. It paves the way for future advancements in precision agriculture under limited-data scenarios. It provides a foundation for further research to refine and scale integrated approaches to optimize water management in worldwide agricultural systems.  
**Author Contributions:** Supervision, conceptualization, formal analysis, writing--original draft, F.F.-P. and E.T.-Q.; conceptualization, investigation, writing--original draft preparation, F.F.-P. and E.T.-Q.; investigation, formal analysis, writing--review and editing, K.G. and A.B.; writing--review and editing, F.F.-P., E.T.-Q. and K.G.; project administration, F.R., J.M.M. and W.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministerio de Educacion Superior, Ciencia y Tecnologia de la Republica Dominicana, through the Fondo Nacional de Innovacion y Desarrollo Cientifico y Tecnologico, project 2018-2019-2D5-221, and ANID FONDECYT de Iniciacion en Investigacion 2024 No. 11241342.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors of this research thank the Stress Mitigation in Agricultural Research for Targeted-crops (S.M.A.R.T.) international initiative, the International Initiative for Digitalization in Agriculture (IIDA) and Corporacion de Tecnologas Avanzadas para la Agricultura Macrozona Centro-Sur (CTAA).  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the study's design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.