Using Remote Sensing Techniques to Assess Drought and Salinity Stress in Hybrid Bluegrass under Field Conditions

Frequent monitoring of stress levels in turfgrass is key to maintaining healthy turfgrass stands. Current methods used to monitor turfgrass for drought and salinity stress can be time consuming and expensive. This study was conducted to determine if data obtained from digital image analysis and spectroradiometry could accurately detect drought and salinity stress in hybrid bluegrass. Furthermore, our objective was to determine if there were strong correlations between these data and visual ratings, relative water content (RWC), and leaf osmolality. The field studies were conducted in 2006 on hybrid bluegrass [Poa arachnifera (Torr.) x pratensis (L.)] cv. Reveille at the turfgrass research site near New Mexico State University's golf course. Increasing drought levels decreased relative water content (RWC), increased osmolality, decreased visual ratings, percent green cover, and Normalized Difference Vegetation Index (NDVI). Spectral reflectance ratios (e.g. K and NDVI) calculated from spectral reflectance data correlated moderate to high with visual ratings and percent green cover. Our results suggest that hue can be used to distinguish between drought and salinity stress. Both digital image analysis and spectral reflectance effectively detected drought and salinity stress and may have applications in turfgrass management as rapid