Understanding the relationship between plant water status and productivity and between plant water status and plant mortality is required to effectively quantify and predict the effects of drought on plants. Plant water status is closely linked to leaf water content that may be estimated using remote sensing technologies. Here, we used an inexpensive miniature hyperspectral spectrometer in the 1550–1950 nm wavelength domain to measure changes in silver birch (Betula pendula Roth) leaf water content combined with leaf gas exchange measurements at a sub-minute time resolution, under increasing vapor pressure deficit, CO2 concentrations, and light intensity within the measurement cuvette; we also developed a novel methodology for calibrating reflectance measurements to predict leaf water content for individual leaves. Based on reflectance at 1550 nm, linear regression modeling explained 98–99% of the variation in leaf water content, with a root mean square error of 0.31–0.43 g cm−2. The prediction accuracy of the model represents a c. ten-fold improvement compared to previous studies that have used destructive sampling measurements of several leaves. This novel methodology allows the study of interlinkages between leaf water content, transpiration, and assimilation at a high time resolution that will increase understanding of the movement of water within plants and between plants and the atmosphere.