UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Simpson, Jake E.; Holman, Fenner H.; Nieto, H.; El‐Madany, Tarek S.; Migliavacca, Mirco; Martín, M. Pilar; Burchard-Levine, Vicente; Cararra, Arnaud; Blöcher, Solveig; Fiener, Peter; Kaplan, Jed O.

doi:10.1016/j.agrformet.2022.108981

Cited by 13 publications

(11 citation statements)

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“…Estimates of λE from satellite data are typically within 15%–30% of ground‐based flux measurements with an RMSD of ∼50 W m −2 (Kalma et al., 2008). UAV‐based studies have reported RMSD values between 10% and 30% of measured fluxes in irrigated crops (Hoffmann et al., 2016; Kustas et al., 2016; Ortega‐Farias et al., 2021), grasslands (Brenner et al., 2018), and savannas (Simpson et al., 2022). Unlike these and other UAV studies, which rely on local flux towers for meteorological input variables, we measured all variables necessary for resolving the SEB equation from the UAV itself.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, UAV data can be acquired at high temporal frequencies and during overcast conditions to understand and monitor changes in plant physiology and surface‐atmosphere mass and energy fluxes under varying radiative, climatic, and hydrological conditions (Vivoni et al., 2014; S. Wang et al., 2020; Simpson et al., 2022). Recently, UAVs have been used to estimate and map ET and its components in a variety of landscapes, including forests (e.g., Marzahn et al., 2020), semi‐arid savannas (e.g., Simpson et al., 2022), grasslands (e.g., Brenner et al., 2017, 2018), and agricultural fields and vineyards (e.g., Hoffmann et al., 2016; Kustas et al., 2019; Mokhtari et al., 2021; Nieto et al., 2019; Park et al., 2021). To date, however, UAV‐based studies have relied on ground measurements for meteorological data and algorithms designed for coarse‐resolution satellite imagery to estimate ET.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, fine‐scale TIR imagery acquired from UAVs often has to be downsampled to generate coarser resolution mixed pixels in order for existing satellite algorithms to be applied (Brenner et al., 2018; Nieto et al., 2019), which undermines the value of such high‐resolution data sets. To overcome these challenges, new approaches must be developed specifically for fine‐resolution thermal imagery acquired from UAVs (Brenner et al., 2018; Chen & Liu, 2020; Simpson et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al, 2020;Simpson et al, 2022). Recently, UAVs have been used to estimate and map ET and its components in a variety of landscapes, including forests (e.g., Marzahn et al, 2020), semi-arid savannas (e.g., Simpson et al, 2022), grasslands (e.g., Brenner et al, 2017Brenner et al, , 2018, and agricultural fields and vineyards (e.g., Hoffmann et al, 2016;Kustas et al, 2019;Mokhtari et al, 2021;Nieto et al, 2019;Park et al, 2021). To date, however, UAV-based studies have relied on ground measurements for meteorological data and algorithms designed for coarse-resolution satellite imagery to estimate ET.…”

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confidence: 99%

“…In satellite applications, these parameters are typically retrieved from distant weather stations, models, or reanalysis products, which can differ substantially from local conditions (Chen & Liu, 2020;Norman et al, 2000). UAV-based ET studies have primarily used meteorological data from collocated flux towers as inputs to SEB algorithms (e.g., Brenner et al, 2018;Simpson et al, 2022;S. Wang et al, 2019), which has limited the application of UAVs for ET estimation to sites with existing ground instrumentation.…”

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confidence: 99%

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Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles

Morgan,

Caylor

2023

Water Resources Research

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Accurate and timely observations of individual‐scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well‐constrained. We present two novel approaches for independently quantifying fine‐scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine‐scale thermal imagery; the second uses profiles of air temperature (Ta) and humidity (hr) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (λE), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of λE estimates for each approach. The SEB approach generated independent, UAV‐based estimates of λE within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. λE calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in Ta and hr, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically‐based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles

Morgan,

Caylor

2023

Water Resources Research

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Thermal Analysis of Evapotranspiration in Cultivated Fields for the Detection of Archaeological Anomalies

Ciccone

2024

Archaeological Prospection

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Archaeological aerial thermography has traditionally focused on bare ground terrain; however, recent developments in drone technology have prompted a reconsideration of thermal analysis on cultivated fields. This study investigates three different sites using drones equipped with thermal, RGB and multispectral sensors to identify archaeological anomalies. This research challenges the traditional focus of thermal cameras on vegetation‐free terrains by investigating cultivated land, where the perceived temperature is influenced by evapotranspiration—a combination of soil evaporation and vegetation transpiration. While agricultural studies have emphasized the ability of thermal sensors to detect varying temperatures in irrigated vegetation, archaeology has mainly used multispectral sensors for vegetated land. The study shows that in wheat‐covered fields, thermal analysis outperforms multispectral and RGB sensors in detecting anomalies associated with archaeological features. Unexpectedly, optimal anomaly detection occurs during mid‐morning and mid‐afternoon flights, challenging traditional ideas about the timing of thermal analysis. The research highlights the need for renewed interest in the use of thermal cameras for archaeological anomaly detection in cultivated fields. However, further comparative studies between thermal and multispectral analyses on different sites are essential to establish the wider effectiveness of thermal sensors. This study challenges established notions of archaeological aerial thermography and argues for a re‐evaluation of sensor selection and flight timing to improve the detection of archaeological features in cultivated fields.

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Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

Meza,

Torres-Rua,

Hipps

et al. 2023

Irrig Sci

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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. Additionally, factors such as sUAS flight time, shadows, and thermal band calibration were assessed for the creation of 2 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 actual ET 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 (R 2 ) (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 ET 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 actual ET fo...

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UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Cited by 13 publications

References 70 publications

Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles

Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles

Thermal Analysis of Evapotranspiration in Cultivated Fields for the Detection of Archaeological Anomalies

Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

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