Abstract
Green urban areas are increasingly affected by water scarcity and climate change. The combination of warmer temperatures and increasing drought poses substantial challenges for water management of urban landscapes in the western U.S. A key component for water management, actual evapotranspiration (ETa) for landscape trees and turfgrass in arid regions is poorly documented as most rigorous evapotranspiration (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 and relatively small areas occupied by turfgrass in urban areas. Therefore, to understand water consumption processes in these landscapes, efforts using standard measurement techniques, such as the eddy covariance (EC) method as well as ET remote sensing-based modeling are necessary. While previous studies have evaluated the performance of the remote sensing-based two-source energy balance (TSEB) in natural and agricultural landscapes, the validation of this model in urban turfgrass remains unknown. In this study, EC flux measurements and hourly flux footprint models were used to validate the energy fluxes from the TSEB model in green urban areas at golf course near Roy, Utah, USA. High-spatial resolution multispectral and thermal imagery data at 5.4 cm were acquired from small Unmanned Aircraft Systems (sUAS) to model hourly ETa. A protocol to measure and estimate leaf area index (LAI) in turfgrass was developed using an empirical relationship between spectral vegetation indices (SVI) and observed LAI, which was used as an input variable within the TSEB model. In addition, factors such as sUAS flight time, shadows, and thermal band calibration were assessed for the creation of TSEB model inputs. The TSEB model was executed for five datasets collected in 2021 and 2022, and its performance was compared against EC measurements. For ETa to be useful for irrigation scheduling, an extrapolation technique based on incident solar radiation was used to compute daily ETa from the hourly remotely-sensed UAS ET. A daily flux footprint and measured ETa were used to validate the daily extrapolation technique. Results showed that the average of corrected daily ETa values in summer ranged from about 4.6 mm to 5.9 mm in 2021 and 2022. The Near Infrared (NIR) and Red Edge-based SVI derived from sUAS imagery were strongly related to LAI in turfgrass, with the highest coefficient of determination (R2) (0.76–0.84) and the lowest root mean square error (RMSE) (0.5–0.6). The TSEB’s latent and sensible heat flux retrievals were accurate with an RMSE 50 W m−2 and 35 W m−2 respectively compared to EC closed energy balance. The expected RMSE of the upscaled TSEB daily ETa estimates across the turfgrass is below 0.6 mm day−1, thus yielding an error of 10% of the daily total. This study highlights the ability of the TSEB model using sUAS imagery to estimate the spatial variation of daily ETa for an urban turfgrass surface, which is useful for landscape irrigation management under drought conditions.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Change history
23 January 2024
A Correction to this paper has been published: https://doi.org/10.1007/s00271-023-00913-3
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Acknowledgements
This study was possible thanks to support from Utah Water Research Laboratory Student Fellowship and the United States Golf Association. The authors are also grateful for the extraordinary support from the Utah State University AggieAir sUAS program staff and Agricola Research team for data collection support and analysis. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.
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K.M, A.T.R and L.H authors contributed to the conception and design of the study, experimental setup, data acquisition, and processing, and wrote the main manuscript text. Data acquisition was handled by L.C, R.G, C.C, I.G. W.K, H.N, K.K, V.B.L and M.P.M authors reviewed and edited the manuscript.
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Appendix
Appendix
a High, b intermediate, and c low turfgrass quality for sampling leaves
a A sampling location with four replications and b sampling turfgrass with a turfgrass cup cutter
Crow profile from a 3D reconstruction using sUAS point cloud data
Linear regression of thermal band calibration using infrared thermometer sensors (IRTs)
Time series of net radiation (Rn), sensible heat flux (H), latent heat flux (LE), and ground heat flux (G) from closed energy balance in 2022
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Meza, K., Torres-Rua, A.F., Hipps, L. et al. Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model. Irrig Sci (2023). https://doi.org/10.1007/s00271-023-00899-y
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DOI: https://doi.org/10.1007/s00271-023-00899-y