Spectral Reflectance of Tall Fescue (Festuca Arundinacea Schreb.) Under Different Irrigation and Nitrogen Conditions

The issue of water and climate change is present in many countries. Drought stress is one of the main abiotic stresses influencing turfgrass growth and quality. Tall fescue is the most suitable cool-season turfgrass for the Mediterranean region. This species has a better heat tolerance than perennial ryegrass and Kentucky bluegrass. The analysis of radiation reflected by turfgrass can supply precious information on drought stress and nutritional status. In this study a Linear Gradient Irrigation System (LGIS) was adopted on a Festuca arundinacea turf with 9 water replenishment levels and 2 nitrogen conditions, to evaluate the proximity sensed spectral reflectance. ET0 was estimated using the Hargreaves and Samani method. The following parameters were determined: turf quality, drought tolerance, pest problems, temperature of the surface, clippings weight and relative nitrogen content, turf growth and soil moisture. Spectral reflectance data were acquired using a LICOR 1800 spectroradiometer. Pearson correlation coefficients were studied among all parameters and vegetation indices. Nitrogen fertilization influenced significantly turf quality, clippings weight, nitrogen content and turf growth. Water replenishment influenced significantly all parameters except nitrogen content. Among all parameters the highest correlation coefficient was registered relating drought tolerance with turf quality (r = 0.88) and with surface temperature (r = -0.88). Among vegetation indices results showed that Water Index (WI) and Normalized Difference Water Index (NDWI), are the most suitable to discriminate between different levels of water replenishment. Comparing WI with NDWI, the correlation coefficients were higher for Water Index in all the parameters, in particular the highest WI value was registered for drought tolerance (r = 0.91). This preliminary research demonstrates that spectral remote sensing can be a useful diagnostic tool to detect water stress in turfgrasses. (C) 2015 The Authors. Published by Elsevier B.V.