Leaf Water Potential in a Mixed Mediterranean Forest from Machine Learning and Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Imaging

Leaf water potential ((Formula presented.) _leaf) is a key indicator of plant water status, but its measurement is labor-intensive and limited in spatial coverage. While remote sensing has emerged as a useful tool for estimating vegetation water status, (Formula presented.) _leaf remains unexplored, particularly in mixed forests. Here, we use spectral indices derived from unmanned aerial vehicle-based hyperspectral imaging and machine learning algorithms to assess (Formula presented.) _leaf in a mixed, multi-species Mediterranean forest comprised of five key woody species: Pinus halepensis, Quercus calliprinos, Cupressus sempervirens, Ceratonia siliqua, and Pistacia lentiscus. Hyperspectral images (400–1000 nm) were acquired monthly over one year, concurrent with (Formula presented.) _leaf measurements in each species. Twelve spectral indices and thousands of normalized difference spectral index (NDSI) combinations were evaluated. Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM)—were used to model (Formula presented.) _leaf. We compared the machine learning model results with linear models based on spectral indices and the NDSI. SVM, using species information as a feature, performed the best with a relatively good (Formula presented.) _leaf assessment (R² = 0.53; RMSE = 0.67 MPa; rRMSE = 28%), especially considering the small seasonal variance in (Formula presented.) _leaf ((Formula presented.) = 0.8 MPa). Predictions were best for Cupressus sempervirens (R² = 0.80) and Pistacia lentiscus (R² = 0.49), which had the largest (Formula presented.) _leaf variances ((Formula presented.) > 1 MPa). Aggregating data at the plot scale in a ‘general’ model markedly improved the (Formula presented.) _leaf model (R² = 0.79, RMSE = 0.31 MPa; rRMSE = 13%), providing a promising tool for monitoring mixed forest (Formula presented.) _leaf. The fact that a non-species-specific, ‘general’ model could predict (Formula presented.) _leaf implies that such a model can also be used with coarser resolution satellite data. Our study demonstrates the potential of combining hyperspectral imagery with machine learning for non-invasive (Formula presented.) _leaf estimation in mixed forests while highlighting challenges in capturing interspecies variability.