Using Thermal Imaging to Measure Water Stress in Creeping Bentgrass Putting Greens

Authors

Joe Foral Jr., University of Nebraska-LincolnFollow

First Advisor

Dr. William Kreuser

Second Advisor

Dr. Timothy Arkebauer

Third Advisor

Dr. Elizabeth Walter-Shea

Date of this Version

Summer 6-2021

Citation

Foral, J. G. (2021). Using Thermal Imaging to Measure Water Stress in Creeping Bentgrass Putting Greens [Master's thesis, University of Nebraska- Lincoln]. Digital Commons

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Agronomy, Under the Supervision of Professor William C. Kreuser. Lincoln, Nebraska: June, 2021

Copyright © 2021 Joseph G. Foral Jr.

Abstract

Thermal imaging is a developing tool that can help turf managers reduce water consumption and improve irrigation scheduling, but in-depth studies are needed to maximize this potential. This study evaluated the ability of thermal imaging to identify water stress in a creeping bentgrass (Agrostis stolonifera ‘007’) putting green. Water use and canopy temperature (T_c) were measured for plots subjected to three levels of measured water replacement (full, half, and none) to evaluate changes over a range of soil water potentials (SWP). Water use was consistent across the irrigation treatments up to several days before observed wilt with crop coefficients (K_c) between 0.83-1.01. As drought conditions progressed (SWP c decreased. Segmented linear regression was used to quantify the trends and identify the critical value of -1501 kPa. Various metrics utilizing T_c were evaluated for a response to water stress. Two metrics, standard deviation of T_c and T_c relative to non-water stressed turf, show potential to indicate periods of stress prior to visible wilt. A strong diurnal pattern was observed in all T_c metrics confirming the need to normalize T_c for current weather conditions. Multiple regression using 2018 data was used to develop a model using weather parameters of air temperature, solar radiation, relative humidity, and wind speed to estimate T_c values of a non-water stressed baseline. A two parameter model using air temperature and solar radiation input provided a strong fit (adjusted R²=0.955) and when applied to unpublished dataset from a 2016 study measuring T_con a creeping bentgrass putting

green. This study shows water use remained consistent until SWP reached a wilting point, followed by a sharp decrease in water use approaching K_c of zero. We show that metrics utilizing T_c can be early indicators of water stress in turfgrass. However, further research with different microclimates and plot sizes would be needed to identify specific values of these metrics that quantify water stress. We also describe a multiple regression model to predict T_c of non-water stressed baseline under various weather conditions. Understanding how T_c of turf with no water stress behaves in different weather can improve identification of water stress.

Advisor: William C. Kreuser