Soil water potential (SWP) is a key parameter for characterizing water stress. Typically, a tensiometer is used to measure SWP. However, the measurement range for commercially available tensiometers is limited to -90 kPa and a tensiometer can only provide estimate of SWP at a single location. In this study, a new approach was developed for estimating SWP from spectral reflectance data of a standing rice crop over the visible to shortwave-infrared region (wavelength: 350 nm to 2500 nm). Five water stress treatments corresponding to targeted SWP of – 30 kPa, - 50 kPa, - 70 kPa, -120 kPa and - 140 kPa were examined by withholding irrigation during the vegetative growth stage of three rice varieties. Tensiometers and mechanistic water flow model were used for monitoring SWP. Spectral models for SWP was developed using partial-least-squares regression (PLSR), support vector regression (SVR), and coupled PLSR and feature selection (PLSRFS) approaches. Results showed that the SVR approach was the best model for estimating SWP from spectral reflectance data with the coefficient of determination values of 0.71 and 0.55 for the calibration and validation datasets, respectively. Observed root-mean-squared residuals for the predicted SWPs were in the range of -7 to -19 kPa. A new spectral water stress index was also developed using the reflectance values at 745 nm and 2002 nm, which showed strong correlation with relative water contents and electrolyte leakage. This new approach is rapid and non-invasive and may be used for estimating SWP over large areas.