Management of irrigation and fertility on a golf course or other large turfgrass area requires a significant amount of time scouting for and identifying problem areas to maintain optimum turfgrass quality. The objectives of these studies were to evaluate the relationship between remotely sensed reflectance data collected from a turfgrass canopy and the associated phosphorus and nitrogen content of turfgrass tissue, and to determine the relationship between reflectance data and soil moisture content as determined by time domain reflectometry (TDR). Phosphorus deficiency symptoms decreased and biomass production increased at P rates above 1.0 g m-2 with a single application while no increase in soil-P was observed. Reflectance measurements were taken in increments from 400 to 1050 nm and correlated with plant tissue P concentration, chlorophyll content, plant biomass and visual quality. Stepwise regression identified a model utilizing reflectance in the blue, yellow, orange, and red regions of the spectrum that explained 73% of the variability in plant tissue P concentration for all sampling dates in 2002 and 2003. Few correlations were found between vegetative indices such as the normalized difference vegetation index (NDVI) and plant response. Results indicate that P deficiencies of creeping bentgrass can be detected through the use of remote sensing. P deficiencies were corrected with a single foliar application of P at rates above 1.5 g m-2. Using partial least-squares regression, our results indicate a weak relationship between the actual and predicted values for turfgrass quality, biomass production, and chlorophyll content under varying rates of N fertilization. However, a strong relationship was observed between actual and predicted values for N concentration of the plant tissue during 2002 and 2003 (r2 = 0.90 and 0.74 respectively). Similarly, no correlation was observed between visual drought stress ratings and the associated soil moisture content for samples collected one day before the onset of visible drought stress. However, PLS regression indicates a strong relationship between actual and predicted soil moisture content based on reflectance data one day prior to onset of drought stress symptoms (r = 0.79).