Remote Sensing and Soil Moisture Sensors for Irrigation Management in Avocado Orchards: A Practical Approach for Water Stress Assessment in Remote Agricultural Areas
Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices.
