Unique Methods for Assessing Water Stress of Agrostis Stolonifera and C. Dactylon x C. Transvaalensis .

Water conservation is an important responsibility for today’s golf course superintendents. Improvements are needed in our ability to accurately and efficiently monitor soil moisture stress. Time-domain reflectometry allows for precise and timely method for assessing irrigation needs. However, this method is time consuming and irrigation needs from unsampled locations may be missed. Researchers have attempted to estimate moisture stress by establishing relationships between soil moisture and remotely sensed data, including the normalized difference vegetation index (NDVI). However, NDVI is directly impacted by numerous stressors which limits its reliability for assessing soil moisture. An alternative water band index (WBI) is influenced by near infrared light absorption from the plant canopy. Previous research showed that WBI was most closely related to soil moisture stress of creeping bentgrass (CBG) grown on sand-based root zones. Subsequently, ‘007’ CBG and ‘Latitude 36’ hybrid bermudagrass (HBG) grown on three varying soil textures (90:10 sand/peat, sand loam, and clay) were arranged in a 2 x 3 factorial design and replicated six times. Reflectance data was collected daily throughout dry-down cycles. The WBI was strongly correlated with soil moisture of CBG grown on all soils (r > 0.73), and with HBG grown on sand/peat and sand loam (r > 0.84) but not on clay. The WBI became more reliable as soil moisture decreased and particle size increased. Additionally, examination of algorithms known to predict abiotic stress and changes highlighting water absorption indicate other useful moisture stress assessments. However, these formulas require specialized equipment and currently have limited practical application. The WBIs potential to rapidly assess soil moisture needs will allow ongoing exploration to predict irrigation needs without interference from other stressors. Subsequent studies will investigate how WBI and related indices can be utilized to remotely sense large scale turf surfaces assessing moisture needs.