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.

绿色城市地区越来越受到水资源短缺和气候变化的影响。气温升高和干旱加剧相结合，对美国西部城市景观的水管理提出了重大挑战。水资源管理的一个关键组成部分，干旱地区景观树木和草坪的实际蒸散量 (ETa) 很少被记录为最严格的蒸散量 (ET)。 ）研究主要集中在自然或农业领域。ET 是一个复杂且非线性的过程，尤其难以在城市景观中测量和估计，因为城市地区土地覆盖/土地利用的空间变异性较大，且草坪草占用的面积相对较小。因此，为了了解这些景观的水消耗过程，有必要使用标准测量技术，例如涡度协方差（EC）方法以及基于ET遥感的建模。虽然之前的研究评估了基于遥感的双源能量平衡（TSEB）在自然和农业景观中的性能，但该模型在城市草坪中的验证仍然未知。在本研究中，使用 EC 通量测量和每小时通量足迹模型来验证美国犹他州罗伊附近高尔夫球场绿色城市地区 TSEB 模型的能量通量。从小型无人机系统 (sUAS) 获取 5.4 厘米处的高空间分辨率多光谱和热图像数据，以模拟每小时的预计到达时间 (ETa)。利用光谱植被指数 (SVI) 和观测到的 LAI 之间的经验关系开发了测量和估计草坪草叶面积指数 (LAI) 的协议，该协议用作 TSEB 模型中的输入变量。此外，还评估了 sUAS 飞行时间、阴影和热带校准等因素，以创建 TSEB 模型输入。TSEB 模型针对 2021 年和 2022 年收集的五个数据集执行，并将其性能与 EC 测量结果进行比较。为了使 ETa 对灌溉调度有用，基于入射太阳辐射的外推技术被用于从每小时遥感 UAS ET 计算每日 ETa。每日通量足迹和测量的 ETa 用于验证每日外推技术。结果显示，2021 年和 2022 年夏季校正的每日 ETa 值的平均值约为 4.6 毫米至 5.9 毫米。源自 sUAS 图像的近红外 (NIR) 和基于红边的 SVI 与草坪草中的 LAI 密切相关，最高的决定系数 ( R ² ) (0.76–0.84) 和最低的均方根误差 (RMSE) (0.5–0.6)。与 EC 闭合能量平衡相比，TSEB 的潜热通量和感热通量反演准确，RMSE 分别为 50 W m ^-2和 35 W m ^{-2 。}整个草坪上 TSEB 每日 ETa 估计值的预期 RMSE 低于 0.6 mm day ^-1，从而产生每日总量的 10% 的误差。本研究强调了 TSEB 模型使用 sUAS 图像来估计城市草坪表面每日 ETa 空间变化的能力，这对于干旱条件下的景观灌溉管理非常有用。