Remote and proximal sensing assessment of water status and its correlation with yield on almond orchards in Southeast Spain

Abstract This study evaluates the potential of UAS-based and proximal sensing tools to assess water stress and how derived indices correlates with yield in almond orchards in the semiarid conditions of southeast Spain. Two commercial orchards with contrasting irrigation regimes were monitored in 2023 using multispectral and thermal UAS imaging, alongside ground-based physiological and agrometeorological measurements. The Crop Water Stress Index (CWSI), calculated empirically from thermal data, and multispectral vegetation indices (VIs) were validated against stomatal conductance, stem water potential, and gas exchange parameters. Spatial variability in water status was explored using growth variability maps derived from NDVI and cumulative transpiration estimates. Results revealed significant correlations between UAS-based CWSI and water-related traits, with R² values exceeding 0.85 for stem water potential and intrinsic water-use efficiency. VIs, particularly those related to pigment composition (e.g., CCCI, MTCI, and CRI2), also demonstrated predictive capacity for physiological traits while NIR-related indices showed notable correlations with yield. Yield correlations were most accurate when integrating CWSI with pigment-sensitive indices such as PSRIm and chlorophyll-related VIs. Findings in this work are promising; however, challenges including proper calibration of UAS data and the influence of post-harvest physiological changes were also noted. This study highlights the value of combining thermal and multispectral remote sensing to optimize water management, while presenting promising results that open new windows for future yield prediction in almond orchards, offering a scalable approach for precision agriculture.