Karem Meza Capcha, Alfonso Torres-Rua, Larry Hipps, Kelly Kopp, Chase M. Straw, Rui Gao, Sergio Alvarez-Carrion, Ian Gowing, & Cal Coopmans
Abstract
Golf courses are increasingly affected by water scarcity and climate change. The combination of warmer temperatures and increasing drought poses substantial challenges to the management of urban landscapes in the western U.S. To address the irrigation needs of these landscapes and their relationship to turfgrass quality (TQ), a better understanding and modeling of actual evapotranspiration (ETa) using ground instrumentation and spatial information is necessary. Eddy covariance (EC) measurements allow for precise quantitative estimates of ETa, while TQ may be measured using spectral indices. ETa is a complex and non-linear process, and especially difficult to measure and estimate in urban landscapes due to the large spatial variability in land cover/land use. Therefore, the objective is to evaluate the performance of the Two Source Energy Balance (TSEB) model employing Uncrewed Aircraft System (UAS) information for the spatial estimation of actual ET over irrigated turfgrass and then validate these estimates with EC measurements. Additionally, we aim to explore their relationship with TQ at a golf course in Utah. Daily ETa was measured using EC method in 2021 and 2022. TQ was modeled using the Random Forest (RF) algorithm and spectral indices. High-resolution multispectral and thermal imagery data were acquired from UAS to execute the TSEB model and RF algorithm. Results showed that the average daily ETa values in summer ranged from 4.6 mm to 5.9 mm. The Root Mean Square Error of the upscaled TSEB daily ETa estimates was 0.6 mm day-1. ETa rates were positively correlated with TQ, R2 values ranging from 0.61 to 0.71, for fairways under summer conditions. This study highlights the ability of the TSEB model using UAS imagery to estimate the spatial variation of daily ETa for turfgrass and RF model capability to build a customized TQ model, which is useful for landscape irrigation management under drought conditions.
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