High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System

Simpson, Jake E.; Holman, Fenner H.; Nieto, Héctor; Voelksch, Ingo; Mauder, Matthias; Klatt, Janina; Fiener, Peter; Kaplan, Jed O.

doi:10.3390/rs13071286

Cited by 18 publications

(14 citation statements)

References 71 publications

(116 reference statements)

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“…We found a strong R 2 of 0.95 and RMSE for Rn estimates of 32 W m -2 . According to other studies (Nassar et al 2021;Simpson et al 2021;Burchard-Levine et al 2021;Gao et al 2022), TSEB estimates of Rn have good agreement with measurements. High-resolution imagery (< 5 m) allows for the discrimination between grass and tree pixels and achieves better accuracy of estimated ET with RMSE ~50 Wm -2 (Burchard-Levine et al, 2021b).…”

Section: • Assessing the Hourly Tseb -Pt Versus Ec Measurementssupporting

confidence: 70%

Spatial Estimation of Actual Evapotranspiration over Irrigated Turfgrass Using sUAS Thermal and Multispectral Imagery and TSEB Model

Meza

Torres‐Rua

Hipps

et al. 2023

Preprint

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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 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 (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 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 for an urban turfgrass surface, which is useful for landscape irrigation management under drought conditions.

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Section: • Assessing the Hourly Tseb -Pt Versus Ec Measurementssupporting

confidence: 70%

Spatial Estimation of Actual Evapotranspiration over Irrigated Turfgrass Using sUAS Thermal and Multispectral Imagery and TSEB Model

Meza

Torres‐Rua

Hipps

et al. 2023

Preprint

Self Cite

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“…It has become one of the most competitive tools, providing excellent possibilities for precision agriculture [ 21 – 23 ]. A typical example, such as the Micasense Altum camera, is the integration of multispectral and thermal infrared cameras into a single unit, which has the advantage of overlapping fields of view and simultaneous access to canopy spectral, structural, and thermal information [ 24 ]. Although a variety of data can be obtained by using the sensors integrated with UAV, few studies on their comprehensive utilization/fusion and potential in LAI application is still unknown.…”

Section: Introductionmentioning

confidence: 99%

Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery

Deng

Guo

et al. 2022

Plant Methods

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Background Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion. Methods To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression. Results The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat. Conclusions The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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“…UAS-borne TIR could fill this gap by providing data at spatial resolutions that are high enough to capture LST variation across different surface components, but at the same time, can be repeated with a flexible time frame to capture LST dynamics (Simpson et al, 2021).…”

Section: Vegetation Stress and Thermal Functionmentioning

confidence: 99%

“…rocks, soil, water, snow) in coarse‐resolution satellite pixels makes it challenging to interpret the biological and environmental controls on LST (Cable et al, 2016). UAS‐borne TIR could fill this gap by providing data at spatial resolutions that are high enough to capture LST variation across different surface components, but at the same time, can be repeated with a flexible time frame to capture LST dynamics (Simpson et al, 2021). In Figure 5, we show an example of UAS‐collected LST at a low‐arctic tundra site.…”

Section: Vegetation Applications Of Uas‐based Remote Sensing In the A...mentioning

confidence: 99%

Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate

et al. 2022

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The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure and function that have important feedbacks to the global climate system. The heterogeneous nature of arctic landscapes creates challenges for monitoring and improving understanding of these ecosystems, as current efforts typically rely on ground, airborne or satellite‐based observations that are limited in space, time or pixel resolution. The use of remote sensing instruments on small unoccupied aerial systems (UASs) has emerged as an important tool to bridge the gap between detailed, but spatially limited ground‐level measurements, and lower resolution, but spatially extensive high‐altitude airborne and satellite observations. UASs allow researchers to view, describe and quantify vegetation dynamics at fine spatial scales (1–10 cm) over areas much larger than typical field plots. UASs can be deployed with a high degree of temporal flexibility, enabling observation across diurnal, seasonal and annual time‐scales. Here we review how established and emerging UAS remote sensing technologies can enhance arctic plant ecological research by quantifying fine‐scale vegetation patterns and processes, and by enhancing the ability to link ground‐based measurements with broader‐scale information obtained from airborne and satellite platforms. Synthesis. Improved ecological understanding and model representation of arctic vegetation is needed to forecast the fate of the Arctic in a rapidly changing climate. Observations from UASs provide an approach to address this need, however, the use of this technology in the Arctic currently remains limited. Here we share recommendations to better enable and encourage the use of UASs to improve the description, scaling and model representation of arctic vegetation.

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High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System

Cited by 18 publications

References 71 publications

Spatial Estimation of Actual Evapotranspiration over Irrigated Turfgrass Using sUAS Thermal and Multispectral Imagery and TSEB Model

Spatial Estimation of Actual Evapotranspiration over Irrigated Turfgrass Using sUAS Thermal and Multispectral Imagery and TSEB Model

Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery

Remote sensing from unoccupied aerial systems: Opportunities to enhance Arctic plant ecology in a changing climate

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