Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

Nassar, Ayman; Torres‐Rua, Alfonso F.; Hipps, Lawrence E.; Kustas, W. P.; McKee, Mac; Stevens, David King; Nieto, Héctor; Keller, Daniel L.; Gowing, Ian; Coopmans, Calvin

doi:10.3390/rs14020372

Cited by 15 publications

(6 citation statements)

References 46 publications

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“…The high spatial resolution of multispectral imagery (Table 1) allows separation of turfgrass and tree canopies from the impervious areas. Normalized difference vegetation index (NDVI) imagery was used to estimate vegetation cover (French et al 2003;Simpson et al 2021;Nassar et al 2022) and pixels were classified into vegetation and vegetation categories by ArcGIS Pro image processing software. Fractional cover, 𝑓 𝑐 , was calculated with NDVI.…”

Section: And Canopy Heightmentioning

confidence: 99%

“…2021; Nassar et al 2022). The sUAS has been widely used in agricultural applications (Hassler and Baysal-Gurel 2019;Katz et al 2022;Tang et al 2022;Kumar Yadav et al 2023), but less effort has been directed toward for turfgrass research.…”

Section: Introductionmentioning

confidence: 99%

“…Formulas and more details have been published in previous research(Xia et al 2016;Cheng and Kustas 2019;Nieto et al 2019;Simpson et al 2021;Gao et al 2022;Nassar et al 2022) for agricultural and natural ecosystems. The TSEB -PT approach was used to model ET in an urban ecosystem (Fig.4), and an open-source python repository (https://github.com/hectornieto/pyTSEB (accessed on 2 September 2022)) was used to calculate energy fluxes.…”

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confidence: 99%

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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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Section: And Canopy Heightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…The wavelet transform (WT) differs from the STFT in that its time-frequency window is not uniformly distributed in the phase plane, and its unique multi-scale and multi-resolution characteristics create a unique advantage for signal composition analysis [22][23][24][25][26][27]. But once the wavelet basis function is determined, the distribution of the time-frequency window in the time-frequency plane is then fixed with poor adaptability [28][29][30][31]. The above algorithms achieve the time-frequency decomposition analysis of long time series data, but none of them have good adaptive properties.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Monitoring and Evaluation in Agricultural Fields Based on NDVI Long Time Series and CEEMDAN

Li,

Wang,

et al. 2023

Remote Sensing

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The North China Plain is an important area for agricultural economic development in China. But water shortages, severe groundwater over-exploitation and drought problems make it difficult to exercise the topographic resource advantages of the plain. Therefore, the precise monitoring of soil moisture is of great significance for the rational use of water resources. Soil characteristics vary in natural farmland ecosystems, crops are constrained by multiple compound stresses and the precise extraction of soil moisture stress is a difficult and critical problem. The long time series was decomposed via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain different intrinsic mode function (IMF) components, and the statistical descriptors of each component were calculated to realize the precise discrimination of soil moisture stress. A quantitative evaluation model of soil moisture was established, and the different noise addition ratios and modeling types were set respectively to investigate the optimal inversion model. The results showed that: (1) The reconstruction error of the CEEMDAN was small and almost 0; it had a high reconstruction accuracy and was more suitable for the decomposition of the long time series. The first two components, IMF1 and IMF2, were soil moisture stress subsequences, and it could effectively reflect the moisture stress situation. (2) The inversion model performed well when ε was 0.05 and the model type was quadratic, with a coefficient of determination R2 of 0.98, which gave a better fit and less error. (3) The overall soil moisture content in the study area was low, basically in the range of 6.9% to 15.7%, with the central part, especially the south-central part, being the most affected by soil moisture stress, and the overall impact of soil moisture stress showed a decreasing trend from February to May. The utilization of CEEMDAN further enhances the accuracy of soil moisture inversion in agricultural fields, realizing the effective application of remote sensing observation technology and time-frequency analysis technology in the field of soil moisture research.

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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

Preprint

Self Cite

View full text Add to dashboard Cite

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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Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

Cited by 15 publications

References 46 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

Soil Moisture Monitoring and Evaluation in Agricultural Fields Based on NDVI Long Time Series and CEEMDAN

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

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