Canopy water content is a dynamic quantity that depends on the balance between water losses from transpiration and water uptake from the soil. Absorption of short-wave radiation by water is determined by various frequencies that match overtones of fundamental bending and stretching molecular transitions. Leaf water potential and relative water content are important variables for determining water deficit and drought effects; however, these variables may only be indirectly estimated from leaf and canopy spectral reflectance. We review the state of understanding in remote sensing measurements of leaf equivalent water thickness and canopy water content. Indexes using different combinations of spectral bands estimate leaf and canopy water contents, albeit sometimes with large errors caused by differences in canopy structure and soil surface reflectance. Inversion of leaf and canopy radiative transfer models, such as PROSPECT and SAIL, or learning algorithms, like artificial neural networks and genetic algorithms trained on radiative transfer models, are promising methods for creating global datasets of canopy water content.