The objective of this study was to estimate leaf water content based on continuous wavelet analysis from the far infrared (2.5 - 14.0 μm) spectra. The entire dataset comprised of 394 far infrared spectra which were divided into calibration (262 spectra) and validation (132 spectra) subsets. The far infrared (2.5 - 14.0 μm) spectra were first transformed into a wavelet power scalogram, and then linearly plotted against leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlation with leaf water content (R2 = 0.86 maximum, 0.83 minimum), as well as low RMSE (maximum 8.56%, minimum 9.27%). The combination of four wavelet features produced the most accurate model (R2 = 0.88, RMSE = 8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from mid to thermal infrared electromagnetic radiation.