Leaf water content and leaf water potential reflect the state of water in plant tissues and are important indicators of plant water availability and water use efficiency. To investigate the differences in leaf water content and leaf water potential modelling based on UAV multispectral image inversion at different altitudes, multispectral image data were collected at three flight altitude treatments F30, F60, and F100 (30m, 60m, and 100m). By using six combinations of spectral reflectance + empirical vegetation index (EVI) and ground data for correlation analysis, the inversion models and their decision coefficients of the combinations of spectral reflectance + EVI with leaf water content and leaf water potential at different flight altitudes were obtained. Support vector machine (SVM), random forest (RF) and radial basis neural network (RBFNN) models were constructed based on the determination coefficients to analyze the accuracy of UAV multispectral inversion models for leaf water content and leaf water potential of aromatic camphor at different flight altitudes. It was found that the inversion accuracy of the RF-based model was higher than that of the SVM model and the RBFNN model at all three flight altitudes. The F30 treatment was better than the F60 and F100 treatments for leaf water content and leaf water potential inversion. The sensitive spectral reflectance+vegetation index combinations for leaf water content inversion in the F30 treatment were reflectance in the red band (R), reflectance in the red-edge 1 band (RE1), reflectance in the red-edge 2 band (RE2), near-infrared reflectance (NIR), and enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI). The R2, RMSE, and MRE for the training set of the RF model were 0.845, 0.548% and 0.712%, respectively;and for the test set, the R2, RMSE, and MRE were 0.832, 0.683% and 0.897%, respectively. The sensitive spectral reflectance + vegetation index combinations for leaf water potential inversion were R, RE2, NIR, EVI, SAVI, anthocyanin reflectance index (ARI). The R2, RMSE, and MRE for the training set of the RF model were 0.814, 0.073MPa and 3.550%, respectively;and for the test set, R2, RMSE, and MRE were 0.806, 0.095MPa and 4.250%. The results showed that the 30m flight altitude and RF method were the optimal spectral acquisition altitude and optimal model construction method for inverting leaf water content and leaf water potential, respectively. The research result can provide technical support for the moisture monitoring of Cinnamomum camphora based on UAV platform, and can provide application reference for screening UAV multispectral bands and empirical vegetation indices, and realising rapid estimation of plant growth parameters.