Detecting citrus tree water status by integrating hyperspectral remote sensing and physiological data in a water flow-storage model

This study presents a method for determining the stem/xylem water potential of non - stressed citrus trees from leaf spectra, taking into account the hydraulic properties of the trees. Existing spectral indexes can only accurately predict the water content of plants but not the water potential, thus limiting their usefulness for irrigation management. In the proposed method, leaf reflectance data was used to derive the water content of non-stressed Satsuma mandarin leaves using an inversion of the PROSPECT model. Crown water content, obtained from up-scaled leaf water content, was then used as an input to a dynamic water flow-storage model. Model validation with the non-stressed dataset gave an R² between the measured and modeled stem water potential of 0.85, with a slope and intercept of 1.03 and 0.31, respectively. These results suggest that integrating hyperspectral and in situ data potentially yields accurate estimates of the plant water potential.