Abstract
Water content is an important indicator of the health and vitality of standing trees and is widely used in forest physiological research, ecohydrological analysis, and forest management. Consequently, real‐time quantification of this variable in standing trees has become a critical research frontier. Currently, the most intensively studied measurement methods include the resistance method, the dielectric constant method, and the equivalent internal resistance of power source method. The latter is an in situ technique that estimates trunk water status by measuring the electrical response of trees, thereby reflecting their physiological condition without external stimulation. However, the accuracy of relative water content estimation using the equivalent internal resistance of power source technique is highly sensitive to environmental variables (e.g., air temperature and humidity) and the tree's physiological state. Moreover, robust correction strategies to mitigate these influences are still lacking. In this study, we introduced privileged information (PI) into a support vector regression (SVR) framework and developed a PI‐SVR model that uses the equivalent internal resistance of power source to estimate relative water content in standing trees. We collected long‐term measurements from three tree species—poplar, willow, and maple—and incorporated physiological and environmental variables as privileged information during model training. The resulting model predicted relative water content using measurable features such as equivalent internal resistance, sampling‐point temperature, and air humidity. Validation across the three species yielded absolute errors below 6%, demonstrating the effectiveness of the proposed approach.