Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding

Estimating leaf water potential (Ψleaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψleaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) has been proven to be a simple and efficient technique. However, models constructed by existing methods and VIs still face challenges regarding the generalizability and limited ranges of field experiment datasets. In this study, leaf dehydration experiments of three maize cultivars were applied to provide a dataset covering a wide range of Ψleaf variations, which is often challenging to obtain in field trials. The analysis screened published VIs highly correlated with Ψleaf and constructed a model for Ψleaf estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. Models were constructed using PLSR and MLR for each cultivar and PLSR, MLR, and RF for the samples from all three cultivars. The performance of the models developed for each cultivar was compared with the performance of the cross-cultivar model. Simultaneously, the normalized ratio (ND) and double-difference (DDn) were applied to determine the VIs and models. Finally, the relationship between the optimal VIs and Ψleaf was analyzed using discontinuous linear segmental fitting. The results showed that leaf spectral reflectance variations in the 350~700 nm bands and 1450~2500 nm bands were significantly sensitive to Ψleaf. The RF method achieved the highest prediction accuracy when all three cultivars’ data were used, with a normalized root mean square error (NRMSE) of 9.02%. In contrast, there was little difference in the predictive effectiveness of the models constructed for each cultivar and all three cultivars. Moreover, the simple linear regression model built based on the DDn(2030,45) outperformed the RF method regarding prediction accuracy, with an NRMSE of 7.94%. Ψleaf at the breakpoint obtained by discontinuous linear segment fitting was about −1.20 MPa, consistent with the published range of the turgor loss point (ΨTLP). This study provides an effective methodology for Ψleaf monitoring with significant practical value, particularly in irrigation decision-making and drought prediction.