Meza Capcha, Karem, Torres-Rua, Alfonso F., Hipps, Lawrence, Kustas, William P., Kopp, Kelly, Gao, Rui, Christiansen, Laura, Hargreaves, Oliver, Osorio, Katherine, Gowing, Ian, Coopmans, Calvin
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 the management of urban landscapes in the western U.S. To address the water irrigation needs from these landscapes, a better understanding and modeling of consumptive water use (Evapotranspiration, ET) and the factors influencing this parameter is needed. A surface energy balance model called the two-source energy balance (TSEB) remote sensing-based model offers the capability of delivering spatial maps of actual ET at urban areas for which leaf area index (LAI) is a key input. However, there is a lack of instrumentation and documentation (protocols) to measure and model ground-based LAI for these landscapes. The objective of this research is to present a protocol and results for spatially estimate LAI in urban turfgrass at a golf course in Roy, Utah, USA using high-resolution multispectral imagery as part of the Turfgrass tree Urban Remote sensing and energy Fluxes Experiment (TURFEX). Destructive LAI samples were collected from multiple campaigns over the study site for the 2021 and 2022 summers. Multispectral and thermal imagery data were acquired from Unmanned Aircraft Systems (UAS) by the AggieAir UAV research group on the same days as LAI data collection. Found relationship between spectral vegetation indices (SVI) derived from UAS imagery were used to generate LAI maps which were used to model Evapotranspiration using the TSEB model. This study will showcase the developed protocols and results to model turfgrass Leaf Area Index and its influence on TSEB results.
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