Daily Evapotranspiration of Urban Turfgrass Using Two Source Energy Balance Model and Remote Sensing

Evapotranspiration (ET) from lawns, trees, ornamental shrubs and plants is the main water use in urban landscapes. Actual ET values for landscape trees and turfgrass in arid regions are poorly documented as most rigorous ET studies have focused on natural or agricultural areas. ET 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. However, ET plays an important role in irrigation water management for these landscapes. Historically, simple models based on the Penman-Monteith equations fail to consider soil-plant-atmosphere interactions, and physiological responses to environmental conditions above the canopy, as well as coupling between the surface and atmospheric conditions. The Two-Source Energy Balance model using the Priestley-Taylor approach (TSEB -PT) employing remote sensing data allows the estimation of spatial evapotranspiration maps, which can be validated with eddy covariance (EC) measurements. Therefore, the objective of this study is to model and measure the daily actual ET in urban turfgrass. Data were collected at a golf course near Roy, Utah, USA. High-spatial resolution multispectral and thermal imagery data were acquired from Unmanned Aircraft Systems (UASs) to model hourly ET. Eddy covariance flux measurements and an hourly flux footprint model were used to validate the energy fluxes from the TSEB-PT model. An extrapolation technique based on incident solar radiation was used to compute daily ET from the hourly remote-sensed UAS ET. The findings from this study demonstrated that RMSE values between the TSEB-PT model and measurements showed that model net radiation ( ), values were in close agreement with observed. The eddy covariance measurements were also adjusted to force energy balance closure using the observed Bowen ratio. The ET values after forcing closure resulted in closer agreement with the TSEB model. The results will demonstrate the ability of this model using UAS imagery to estimate the spatial variation of daily actual ET for an urban turfgrass surface.