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Predicting Soil Water Content through Remote Sensing of Vegetative Characteristics in a Turfgrass System

^a Univ. of Florida, P.O. Box 110670, Gainesville, FL 32611
^b Dep. of Horticulture, Iowa State Univ., 106 Horticulture Hall, Ames, IA 50011. This journal paper of the Iowa Agric. and Home Econ. Exp. Stn., Ames, Iowa, Project No. 3601 was supported by Hatch Act and State of Iowa funds

^* Corresponding author (jkdk{at}ufl.edu).

Scouting to determine soil water status throughout a golf course or large athletic field complex is quite time consuming and requires numerous observations to characterize variability across the site. The objective of this research was to evaluate the use of a ground-based remote sensing system to predict soil water content through partial least squares regression analysis of canopy reflectance data collected from perennial ryegrass (Lolium perenne L.) maintained at 12.7 mm and creeping bentgrass (Agrostis stolonifera L.) maintained at 6.3 mm during 2002 and 2003 on a Coland silty clay loam. Volumetric soil water at a 5 cm depth was measured by time domain reflectometry and was collected in conjunction with spectral radiance measurements obtained using a fiber optic spectrometer. Volumetric soil water content was best predicted with partial least squares regression analysis of creeping bentgrass canopy reflectance data with a maximum r² of 0.64 (P < 0.001) 1 d before development of drought stress symptoms. Similar results were observed for canopy reflectance data collected from perennial ryegrass plots, indicating that this technology and method of data analysis may be useful in the development of automated turfgrass irrigation management systems.

Abbreviations: ET, evapotranspiration • MLR, multiple linear regression • NIR, near-infrared • PC, principle component • PCA, principle component analysis • PLS, partial least squares • PRESS, predicted residual sum of squares • SEP, standard error of prediction • TDR, time domain reflectometry

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Received for publication January 23, 2006.