Machine Learning-Based Prediction of Stem Water Potential in Olive Orchards Using PlanetScope Imagery and Meteorological Data

Accurate assessment of water status in woody crops is essential for optimizing irrigation management, particularly under Mediterranean conditions characterized by high spatial and temporal variability. Traditional field-based stem water potential measurements are reliable but limited for large-scale operational applications. In this study, a supervised machine learning approach based on Extreme Gradient Boosting was developed to estimate stem water potential in Mediterranean olive orchards by integrating high-resolution PlanetScope multispectral imagery with meteorological variables describing atmospheric evaporative demand. The model was trained and evaluated using a dataset comprising 1628 measurements, curated from 1856 measurements collected during 2021–2025 after temporal matching and quality-control filtering, and 128 predictive features: 44 PlanetScope-derived spectral features and 84 meteorological predictors evaluated across six temporal positions (t0–t5). Model performance was assessed using cross-validation. The model achieved a coefficient of determination of 0.84, a root mean square error of 0.27, and a mean absolute error of 0.21. Air temperature, solar radiation, and reference evapotranspiration were the most influential predictors, while spectral information captured complementary effects related to canopy structure, vegetative vigor, and accumulated physiological responses. Furthermore, the combined use of spectral indices and PlanetScope base-band reflectance values was associated with an approximately 30% lower RMSE than that reported in previous approaches.