Leaf water content is a fundamental physiological characteristic parameter of crops, and plays an important role in the study of the ecological environment. The aim of the work reported in this paper was to focus upon the retrieval of leaf water content from leaf-scale reflectance spectra by developing a physical inversion model based on the radiative transfer theory and wavelet analysis techniques. A continuous wavelet transform was performed on each of leaf component specific absorption coefficients to pick wavelet coefficients that were identified as highly sensitive to leaf water content and insensitive to other components. In the present study, for identifying the most appropriate wavelet, the six frequently used wavelet functions available within MATLAB were tested. Two bior1.5 wavelet coefficients observed at the scale of 200 nm are provided with good performance, their wavelength positions are located at 1 405 and 1 488 nm, respectively. Two factors (a and Δ) of the predictive theoretical models based on the bior1.5 wavelet coefficients of the leaf-scale reflectance spectra were determined by leaf structure parameter N. We built a database composed of thousands of simulated leaf reflectance spectra with the PROSPECT model. The entire dataset was split into two parts, with 60% the calibration subset assigned to calibrating two factors (a and Δ) of the predictive theoretical model. The remaining 40% the validation subset combined with the LOPEX93 experimental dataset used for validating the models. The results showed that the accuracy of the models compare to the statistical regression models derived from the traditional vegetation indices has improved with the highest predictive coefficient of determination (R2) of 0.987, and the model becomes more robust. This study presented that wavelet analysis has the potential to capture much more of the information contained with reflectance spectra than previous analytical approaches which have tended to focus on using a small number of optimal wavebands while discarding the majority of the spectrum.