Light reflectance from plants can be used as a non-invasive predictor of health and yield for many cropping systems, and has been investigated to a lesser extent with managed turfgrass systems. The frequent agronomic inputs associated with maintaining golf course grasses allow for exceptional stand quality under harsh growing conditions, but often expend resources inefficiently, leading to either stand loss or unnecessary inputs in localized areas. Turfgrass researchers have adopted some basic principles of light reflectance formerly developed for cropping systems, but field radiometric-derived narrow-band algorithms for turfgrass-specific protocols are lacking. Research was conducted to expand the feasibility of using radiometry to detect various turfgrass stressors and improve speed and geographic specificity of turfgrass management. Methods were developed to detect applied turfgrass stress from herbicide five days before visible symptoms developed under normal field growing conditions. Soil volumetric water content was successfully estimated using a water band index of creeping bentgrass canopy reflectance. The spectral reflectance of turfgrass treated with conventional synthetic pigments was characterized and found to erroneously influence plant health interpretation of common vegetation indices because of near infrared interference by such pigments. Finally, reflectance data were used to estimate root zone temperatures and root depth of creeping bentgrass systems using a gradient of wind velocities created with turf fans. Collectively, these studies provide a fundamental understanding of several turfgrass-specific reflectance algorithms and support unique opportunities to detect stresses and more efficiently allocate resources to golf course turf.