Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions
- 1 October 2006
- journal article
- research article
- Published by Informa UK Limited in International Journal of Remote Sensing
- Vol. 27 (19), 4137-4157
- https://doi.org/10.1080/01431160600851843
Abstract
A two‐year study was conducted in 2002 and 2003 at the University of Nevada, Las Vegas's center for urban water conservation to assess canopy spectral response of annual ryegrass (Lolium multiflorum Lam.) grown under various combinations of N and irrigation (based on leaching fraction: LF) treatments. Multispectral measurements were acquired using a ground‐based spectroradiometer (200–1100 nm) on a biweekly basis during the growing season (October–May) in 2002 and 2003. Multispectral parameters were correlated with soil–plant parameters and temporal variability was investigated. Results showed that the normalized difference vegetation index (NDVI), stress index (SI), photochemical reflectance index (PRI) and canopy reflectance at 693 nm, were highly correlated with tissue N concentration (TN), tissue moisture content (TM), TN×TM and canopy colour, as influenced by N and LF treatment combinations. Coefficients of determination ranged from 0.50 to 0.79 (P<0.001) based on single‐day correlations and correlations established over the entire growing period in 2002 and in 2003. TN was mainly predicted from wavelengths in the VIS portion of the spectrum, while TM was predicted from wavelengths in the VIS and NIR. Correlations were inconsistent between spectral parameters and physiological parameters throughout the study confirming the problem of temporal variation associated with spectral signatures of turfgrass species. However, spectral reflectance showed significant potential for monitoring turfgrass N and moisture status, and was able to capture temporal variability over the same growing period and from one year to another. The results provide a sound basis for future validation of ground‐based remote sensing for turfgrass management on golf courses.Keywords
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