Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation

Authors: MEZA, KAREM - Utah State University; TORRES-RUA, ALFONSO - Utah State University; HIPPS, LAWRENCE - Utah State University; KOPP, KELLY - Utah State University; STRAW, CHASE - Texas A&M University; Kustas, William; CHRISTIANSEN, LAURA - Utah State University; COOPMANS, CALVIN - Utah State University; GOWING, IAN - Utah State University

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2024
Publication Date: 1/13/2025
Citation: Meza, K., Torres-Rua, A., Hipps, L., Kopp, K., Straw, C., Kustas, W.P., Christiansen, L., Coopmans, C., Gowing, I. 2025. Relating spatial turfgrass quality to actual evapotranspiration for precision golf course irrigation. Crop Science. https://doi.org/10.1002/csc2.21446.
DOI: https://doi.org/10.1002/csc2.21446

Interpretive Summary: Healthy turfgrass–soil systems are a key asset in urban environments, particularly in arid and semi-arid regions that could help to mitigate the effects of global warming. However, turfgrasses face significant challenges related to abiotic stresses such as heat and drought, which are major factors that decrease turfgrass quality in cool-season turfgrasses during the summer. An understanding of actual evapotranspiration and turfgrass quality relationships for site-specific management zones is critical for the implementation of precision turfgrass management for mitigating impact of heat and drought on turfgrass quality. A remote sensing turf quality- random forest model was developed using Unpiloted Aircraft System (UAS) imagery collected 2021-2023 growing season over golf course turfgrass in an urban environment. This study found that the turf quality-random forest model and the actual evapotranspiration-site-specific management zones relationships could identify spatial variation in turf conditions for the purpose of landscape irrigation management. The utilization of these research findings will enhance turf grass best management practices, particularly in the context of water conservation in arid and semi-arid landcsapes.

Technical Abstract: Golf courses are increasingly affected by water scarcity and climate change. Precision irrigation has the potential to serve as a practical approach for minimizing water usage on golf courses through the implementation of customized and location-specific irrigation methods and schedules. An understanding of actual evapotranspiration (ETa) and turfgrass quality (TQ) site-specific management zones (SSMZ) is also important for the implementation of precision turfgrass management (PTM). Therefore, the main objectives of this study were to quantify the relationship between remotely-sensed TQ and ETa estimates and to evaluate the spatial variations of TQ and ETa SSMZ at a golf course in Roy, Utah. Ground-based Normalized Difference Vegetation Index (NDVI) was collected using a TCM-500 sensor and aerial multispectral and thermal imagery data were acquired from Unpiloted Aircraft Systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ- Random Forest (RF) model was developed using six datasets of UAS spectral indices (SIs) and the machine learning RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ETa estimates. The TQ and ETa SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results showed that the Blue Normalized Difference Vegetation Index (BNDVI), the Simple Ratio Index (SR), and the Red Green Blue Vegetation Index (RGBVI) were the top-ranked SIs for predicting TQ and validation demonstrated that TQ was accurate within a RMSE of 0.05. The correlation between TQ-RF and ETa was stronger for fairways (R2=0.74), tees (R2=0.66), and roughs (R2=0.75) as compared to greens (R2=0.25) and the driving range (R2=0.36) on July 20, 2022. Actual evapotranspiration SSMZ in combination with TQ-RF and reference ET fraction (ETf) relationship is useful for irrigation scheduling, addressing questions of when and how much to irrigate. Turfgrass quality SSMZ exhibited both green and non-green areas, with stressed turfgrass exhibiting NDVI values less than 0.6, which may be indicative of water supply scarcity; other potential reasons could include turfgrass wear and soil from cart or human foot traffic. This study demonstrates the ability of TQ-RF and ETa SSMZ to identify spatial variation for the purpose of landscape irrigation management in arid and semi-arid areas.