The ability to detect early signs of stress in turfgrass stands using a rapid, inexpensive, and nondestructive method would be a valuable management tool. A study was conducted to determine if digital image analysis and spectroradiometric readings obtained from drought- and salinity- stressed turfgrasses accurately reflected the varying degrees of stress. Greenhouse drought and salinity experiments were conducted on hybrid bluegrass [Poa arachnifera (Torr.) x pratensis (L.)] cv. Reveille and bermudagrass [Cynodon dactylon (L.)] cv. Princess 77. Percent green cover and hue values obtained from digital image analysis, and Normalized Difference Vegetation Index (NDVI) were moderately to highly correlated with visual ratings, relative water content (RWC), and leaf osmolality. In addition, percent green cover obtained from digital image analysis was strongly correlated with most of the spectral ratios, particularly the ratio of fluorescence peaks (K), modified triangular vegetation index (MTVI), and NDVI, suggesting that spectral reflectance and digital image analysis are equally effective at detecting changes in color brought on by stress. The two methods differed in their ability to distinguish between drought and salinity stress. Hue values obtained from digital image analysis responded differently to increasing drought stress than to increasing salinity stress. Whereas the onset of drought stress was reflected by increased hue values followed by a decrease in values as drought stress increased, there was no increase in hue values at the onset of salinity stress. Thus, changes in hue could be a key to distinguish drought and salinity stress. Both digital image analysis and spectroradiometry effectively detected drought and salinity stress and may have applications in turfgrass management as rapid and quantitative methods to determine drought and salinity stress in turf.