Travis Roberson, 300 Turner Street NW Mail Code 0312, Virginia Tech, Blacksburg, VA, David S. McCall, Virginia Tech, Blacksburg, VA and Erik H Ervin, CSES, Virginia Tech, Blacksburg, VA
Water conservation is a vital responsibility for golf course superintendents because of increasing global demand of this limited resource, and the need to shift water usage towards feeding a growing population. Improvements are needed in our ability to monitor localized soil moisture stress more accurately and efficiently. Time-domain reflectometry is becoming increasingly common, because it allows a precise and timely method for assessing irrigation needs. However, the use of such devices requires personal attention at each sampling site, which may result in missed irrigation needs at unsampled locations. Researchers have attempted to improve monitoring efficiency by rapidly assessing irrigation needs with several technologies, such as estimating water availability through an established relationship between soil moisture and the normalized difference vegetation index. However, this index is also impacted by numerous stressors which contribute to turf decline, and limits its usefulness for solely assessing irrigation needs. Previously, the water band index (WBI) was most accurate among numerous vegetation indices at predicting soil moisture deficiencies of creeping bentgrass grown on a sand-based root zone, independent of chlorophyll concentrations. However, the water reflectance characteristics have not been explored with different grasses grown in varying soil textures. Research was conducted on the spectral characteristics of ‘007’ creeping bentgrass and ‘Latitude 36’ hybrid bermudagrass grown on five soil textures; pure sand, 90:10 sand/peat, silt loam, clay loam, and clay. This research has demonstrated that water absorption features within near infrared spectra reported in previous research were also present with both grass species, regardless of soil texture. The presence of this spectral feature will allow ongoing exploration for using this unique vegetation index to predict irrigation needs without interference from other stressors. Subsequent studies will investigate how the WBI and related indices can be used to remotely sense irrigation needs across larger turfgrass surfaces.