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This article in CS

  1. Vol. 51 No. 5, p. 2212-2218
     
    Received: Dec 23, 2010


    * Corresponding author(s): bremer@ksu.edu
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doi:10.2135/cropsci2010.12.0728

Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool-Season Turfgrass: I. Variation among Species and Cultivars

  1. Dale J. Bremer *a,
  2. Hyeonju Leeb,
  3. Kemin Suc and
  4. Steven J. Keeleya
  1. a Dep. of Horticulture, Forestry & Recreation Resources, 2021 Throckmorton Hall, Kansas State Univ., Manhattan, KS 66506
    b Dep. of Plant Pathology, 4024 Throckmorton Hall, Kansas State Univ., Manhattan, KS 66506
    c Dep. of Horticulture and Landscape Architecture, Oklahoma State Univ., Stillwater, OK 74078. Contribution no. 11-181-J from the Kansas Agric. Exp. Station

Abstract

Canopy spectral reflectance may provide an objective means to evaluate visual quality of turfgrass, but evaluations of visual quality may be confounded by differences in reflectance among species or cultivars. In this 3-yr study near Manhattan, KS, we examined effects of species and cultivars on relationships between normalized difference vegetation index (NDVI) and visual quality ratings in Kentucky bluegrass (Poa pratensis L., ‘Apollo’), two Kentucky bluegrass × Texas bluegrass (Poa arachnifera Torr.) hybrids (‘Thermal Blue’ and ‘Reveille’), and tall fescue (Festuca arundinacea Schreb., ‘Dynasty’). A broad range of visual quality was imposed on all four grasses through deficit irrigation and NDVI was measured using broadband spectral radiometry across this range for each grass. Distinct linear regression models of visual quality were found for each grass, and models were also distinct among years in each grass. Relationships between NDVI and visual quality were stronger in the bluegrasses (r2 = 0.41 to 0.83) because they had a greater range in quality under deficit irrigation than tall fescue. The 95% confidence intervals surrounding predictions of visual quality from NDVI ranged from ± 1.25 to 2.10 (on a 1 to 9 scale). Results indicated that the requirement to develop separate models for each grass and in each year, combined with relatively wide confidence intervals, represents a practical limitation to predicting visual quality with NDVI.

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