Using hyperspectral radiometry to predict the green leaf area index of turfgrass

Abstract

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.

Pertinent Water-Saving Management Strategies for Sustainable Turfgrass in the Desert U.S. Southwest

“…Advancements in irrigation system technologies along with the development and implementation of best management practices have drastically improved the capacity for water savings while maintaining turfgrass quality demands. Time-domain reflectometer (TDR) soil moisture sensors (SMS) [90], remote sensing of soil moisture stress through the normalized difference vegetation index (NDVI) [91], highly sensitive imagederived canopy indices [92] such as water band index (WBI), a simple visible vegetation index called green-to-red ratio index (GRI) [93], optical signature of leaves including hyperspectral radiometer [94,95] and other systems have been used to measure volumetric water content (VWC) or estimating moisture stress for irrigation scheduling strategies in turfgrass system. Use of soil moisture sensors was found to reduce water usage by 39% as compared to using historical ET data [96].…”

“…Considerable efforts have also been made to adapt and incorporate soil moisture measurements into irrigation scheduling on golf courses [97] and optical signatures of leaves [95]. The soil moisture sensor technology adds much promise in to soil-water balance maintenance as well as further implementation of precision irrigation practices and turfgrass management which allows for increased input efficiency by utilizing a site-specific, targeted approach to management [98].…”

Mapping and Predicting Non-LinearBrassica rapaGrowth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation

“…Spectral reflectance measurements were collected in 2010 and 2011 as described in Baker et al (2018 ). The spectroradiometer collected wavelengths from 350 to 2500 nm with a 1.4 nm sampling interval for the visible/NIR spectral region (350 nm – 1000 nm) and 2 nm for the short-wave infrared spectral region (1000 nm – 2500 nm) ( A n et al 2015 ); when interpolated, there is one spectral data point per nanometer, for a total of 2151 spectral data points per measurement. For each replicate plant, 10 measurements were collected and averaged to produce a single measurement ( A n et al 2015 ).…”

A review of precision management for golf course turfgrass

“…Hutto et al (2006) Hyperspectral sensors detect critical soil water content 1 day before visual drought symptoms occur with an r 2 of .64 (Dettman- Kruse et al, 2008). An et al (2015) suggested hyperspectral reflectance data can determine the most appropriate wavelengths to determine turfgrass stressors. The water band index measurements from hyperspectral sensors have been reported to exhibit a strong relationship to soil volumetric water content suggesting that the water band index can be used to monitor for turfgrass water stress aside other plant stressors measured by other vegetation indices (Badzmierowski et al, 2019;McCall et al, 2017;Roberson et al, 2021).…”