Site-specific irrigation and turfgrass quality using UAS imagery

Water scarcity and climate change are pressing concerns for golf course management, requiring site-specific irrigation strategies to maintain turfgrass quality (TQ) while conserving water. Traditional irrigation methods do not account for the spatial variability in reliable estimates of actual evapotranspiration (ET_a) and TQ. Therefore, an approach that integrates accurate spatial estimates of ET_a and TQ and their relationship is essential for enhancing the best precise irrigation practices, particularly in semi-arid regions.

In a new study published in Crop Science, researchers addressed this challenge by integrating aerial mapping with advanced modeling techniques to estimate ET_a and TQ at a golf course in Utah. They used multispectral and thermal imagery from unmanned aircraft systems (UAS) in conjunction with the Two-Source Energy Balance model and machine-learning models to estimate high-resolution spatial ET_a and TQ, respectively. They validated ET_a estimates against gold-standard eddy covariance data commonly used in scientific research and compared random forest TQ model estimates to ground-based and visual TQ assessments.

The findings demonstrate that incorporating spatial TQ and ET_a data into site-specific irrigation zones enables more informed water management decisions. This approach may reduce water use, lower costs, and improve irrigation efficiency in golf courses in semi-arid regions. The study underscores the value of UAS-based remote sensing for optimizing turfgrass management in water-limited environments.