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Remotely sensed estimation of leaf water content (LWC) using optical data at early crop growth stage is important for identification of water-stressed plants. However, its accurate estimation is currently a major challenge due to the coarse spatial and spectral resolution of the available optical data, and the atmospheric impact on satellite-based remotely sensed data. Moreover, during early growth stages the canopy coverage is low, increasing the effect of the bare soil background on low spatial resolution data. Consequently, broadband optical data is insensitive to overtone frequencies of O-H stretching bonds of water molecules. Accordingly, this research developed a new model for estimating LWC based on newly identified, pure-pixel, water sensitive indices from high spatial resolution hyperspectral data. A hand-held field spectroradiometer and drone-based hyperspectral imager were used to collect temporal high spectral resolution hyperspectral data (Range: 400–1000 nm; Bandwidth: ~2.1 nm) at leaf level, together with destructively sampled leaves to measure their LWC using the oven-drying method. The spectroradiometer data were used to explore the wavelengths sensitive to vibrational overtone frequencies of O-H bonds of water molecules present in leaves. A total of seven water-sensitive wavelengths were identified, and corresponding normalised indices created for use with pure pixel narrowband hyperspectral data from vegetation. Farm scale maps of LWC were then created using drone-based hyperspectral data, based on minimum and maximum values of the above indices and ‘days after sowing’ information, through a gradient boost machine (GBM) model. The early growth stage maps of LWC were able to distinguish between water-stressed and well-irrigated plots with an R2 of 0.93 and RMSE of 1.6% (g/g).