Using hyperspectral radiometry to predict the green leaf area index of turfgrass
2015
An Nan, | Goldsby, Anthony L. | Price, Kevin P. | Bremer, Dale J.
The green leaf area index (LAI) is an important indicator of the photosynthetic capacity of turfgrass canopies. The measurement of LAI is typically destructive and requires large plots to allow for multiple sampling dates. Hyperspectral radiometry may provide a rapid, non-destructive means for estimating LAI. Our objectives were to: (1) evaluate the utility of hyperspectral radiometry to predict the LAI of Kentucky bluegrass (Poa Pratensis L.); and (2) determine regions of the spectrum that provide the best LAI predictions. An empirical prediction model of spectral data for LAI was conducted with partial least squares regression (PLSR). The PLSR method created viable, first-iteration models for five of 11 sampling dates (the coefficient of determination (R ²) is 0.52–0.85). Each model had its own set of factors that were analysed to determine their ‘weights’, or specific regions of the spectrum by which they were most strongly influenced. Second iterations of each model were then created using only those regions most strongly influenced, centred on 600, 690, 761, 960, 1330, and 1420 nm (±10 nm). Four of the five second-iteration models had LAI estimation capabilities greater than or similar to the first-iteration models (R ² = 0.72–0.86), indicating that the information contained in all other wavelengths was redundant or irrelevant in regard to predictions of LAI. The robustness of prediction models varied over the growing season, possibly related to changes in canopy properties with environmental conditions. Results suggest hyperspectral radiometry has a significant potential to predict LAI in turfgrass, although different models may be required throughout the growing season.
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