Evaluation of Remote Sensing to Measure Plant Stress in Creeping Bentgrass (Agrostis stolonifera L.) Fairways

Abstract

Turfgrass irrigation strategies must be clearly defined in response to increasing concerns over quality water availability. Water conservation may be achieved with technologies such as remote sensing. The objectives of this research were to (i) correlate reflectance measurements from creeping bentgrass (Agrostis stolonifera L.) under stress with volumetric water content and turf quality measurements, (ii) determine if remote sensing can detect plant stress before visual observation, and (iii) explore the influence of nitrogen fertility on water stress detection. Four experiments were conducted in the 2006 and 2007 growing seasons on 3‐m2 creeping bentgrass plots maintained under fairway conditions at the University of Minnesota agriculture campus. Treatments were irrigation at 20, 40, 60, and 80% of predicted evapotranspiration every 3 d on plots fertilized with 98 or 244 kg N ha−1 yr−1 Reflectance measurements from two remote sensors were more highly correlated (r = 0.73–0.91) than reflectance and turf quality (r = 0.42–0.71) or reflectance and volumetric water content (r = 0.28–0.64). Reflectance measurements detected water stress 0 to 48 h before visual observation across all treatments and experiments. Nitrogen did not influence the ability to detect drought‐stressed turfgrass. Use of remote sensors to monitor and detect drought stress has the potential to improve turfgrass irrigation and ultimately conserve water.

Enhanced Soil Moisture Assessment using Narrowband Reflectance Vegetation Indices in Creeping Bentgrass

“…Jiang et al (2009) demonstrated a moderate, yet significant (P = 0.001), correlation between NDVI and both soil water content (r = 0.22-0.30) and leaf relative water content (r = 0.39-0.72). Similarly, Johnsen et al (2009) established a relationship between NDVI and volumetric water content of soil (r = 0.28-0.64) as well as turf quality (r = 0.42-0.71), indicating that factors beyond water availability were impacting overall turf quality.…”

Using Hyperspectral and Multispectral Indices to Detect Water Stress for an Urban Turfgrass System

“…The normalized difference vegetation index has demonstrated correlation in turfgrass systems to soil moisture, N fertilization, tissue biomass, tissue chlorophyll concentration, and turfgrass quality parameters (e.g., color, density, and uniformity) [18,[28][29][30][31][32][33][34][35][36]. Additional VI have shown useful in quantifying plant responses.…”

Optical and Thermal Remote Sensing of Turfgrass Quality, Water Stress, and Water Use under Different Soil and Irrigation Treatments

“…Optical and thermal remote sensing techniques can be used by turf managers in a consistent and non-destructive fashion to evaluate the quality and irrigation needs of turfgrass systems, before stress signs intensify enough to become perceptible to human eyes [7,9,[14][15][16]. Previous studies have showed that spectral reflectance of turfgrass in different visible and near-infrared portions of the electromagnetic (EM) spectrum is sensitive to several factors including, but not limited to: fertilizer and herbicide application rates [9,12]; carbon concentrating mechanism (C3 vs. C4 grasses) [9]; irrigation amount and uniformity [14]; and, management practices such as mowing height [11].…”