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Remote Sensing

Remote Sensing of Nitrogen Stress in Creeping Bentgrass

^a Univ. of Wisconsin Extension, Winnebago County, 625 East County Rd. Y, Suite 600, Oshkosh, WI 54901
^b Dep. of Horticulture, Iowa State Univ., 106 Horticulture Hall, Ames, IA 50011

^* Corresponding author (jason.kruse{at}ces.uwex.edu)

Received for publication January 25, 2006. Development of a remote sensing system that can reliably identify nutrient deficiencies may reduce time spent sampling turfgrass areas and allow for site-specific applications of fertilizers. The objectives of this research were to evaluate the use of a ground-based remote sensing system and partial least-squares (PLS) regression to predict the N concentration, biomass production, chlorophyll content, and visual quality of creeping bentgrass (Agrostis stolonifera L. ‘Penncross’) growing under varying N rates, and to compare PLS regression to other vegetative indices. The study consisted of three N treatments (0.0, 12.2, and 24.4 kg ha^–1 15 d^–1) arranged in a randomized complete block design. Spectral radiance measurements were obtained from plots using a fiber-optic spectrometer to calculate vegetative indices. The PLS regression analysis yielded a strong relationship between actual and predicted N concentration of creeping bentgrass plant tissue during 2002 and 2003 (r² = 0.95 and 0.71 respectively). However, PLS regression failed to produce a prediction for the chlorophyll concentration. Regressing the normalized vegetation index (NDVI), Stress1 (R₇₀₆/R₇₆₀), and Stress2 (R₇₀₆/R₈₁₃) ratios against N concentration yielded better results in 2003 when there were distinct differences in N concentration between the N rates. These results indicate that the traditional vegetation indices like NDVI might be better suited for determining the relative N status of turfgrass plants when compared against a well-fertilized control. More research will be required to determine if the PLS regression analysis produces prediction models that are able to specifically identify a particular nutrient deficiency or plant stress, and how the results will vary between grass species.

Abbreviations: CST, central standard time • IR, Infrared • MLR, multiple linear regression • NDVI, normalized difference vegetation index • NIR, near infrared • PLS, partial least-squares • PRESS, predicted residual sum of squares • R, reflectance • SEP, standard error of prediction • Stress1, R₇₀₆/R₇₆₀ • Stress2, R₇₀₆/R₈₀₃ • WL550, spectral reflectance at 550 nm • WL710, spectral reflectance at 710 nm