Inter-comparison of energy balance and hydrological models for land surface energy  flux estimation over a whole river catchment

Guzinski, Radoslaw; Nieto, Héctor; Stisen, Simon; Fensholt, Rasmus

doi:10.5194/hess-19-2017-2015

Cited by 19 publications

(16 citation statements)

References 46 publications

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“…However, for pixels with a f g of zero the soil heat flux is increased significantly at the expense of the latent heat flux, which is set to zero. This reflects the model’s assumptions that no transpiration occurs from pixels with a f g of zero and for pixels without transpiration also no evaporation from the soil occurs (Guzinski et al 2015). The rational for this assumption is that if the Priestley–Taylor coefficient α falls below zero in the iterative process (meaning no transpiration from this pixel), this indicates very dry conditions under which no evaporation from the soil would occur.…”

Section: Resultsmentioning

confidence: 76%

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Brenner

Zeeman

Bernhardt

et al. 2018

International Journal of Remote Sensing

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Spatially distributed high-resolution data of land surface temperature (LST) and evapotranspiration (ET) are important information for crop water management and other applications in the agricultural sector. While satellite data can provide LST high-resolution data of 100 m, the current development of unmanned aerial systems (UAS) and affordable low-weight thermal cameras allows LST and subsequent ET to be derived at resolutions down to centimetre scale.In this study, UAS-based images in the thermal infrared (TIR) and visible spectral range were collected over a managed temperate grassland in July 2016 at the Terrestrial Environmental Observatories Networks TERENO preAlpine observatory site at Fendt, Germany. The UAS set-up included a lightweight thermal camera (Optris Pi Lightweight) and a regular digital camera (Sony α 6000) that allowed for the acquisition of thermal and optical images with a ground resolution of 5 cm and 1 cm, respectively. Three TIR-based ET models of different complexity were applied and the resulting ET estimates were compared to the Eddy covariance (EC) observations of turbulent energy fluxes and also to the evaporative fraction. While the Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature (DATTUTDUT) model and the Triangle Method belong to the group of simpler contextual models, the Two-Source Energy Balance (TSEB) model incorporates a more physically based formulation of the surface energy balance. In addition to the comparison of UAS-based estimates of latent heat fluxes to EC observations, the effect of the spatial resolution of the model imagery input on the modelled results was analysed by running the models with imagery from the native resolution of the acquired images to resolutions that were aggregated up to 30 m.The results show that both contextual models are sensitive to the input image resolution and that the agreement with the EC observations improves with increasing image resolution. The TSEB model assumes that LST pixels represent a mixed signal of the soil and canopy components, thus an image resolution coarse enough to ensure this assumption should be chosen. However, with the exception of the native image resolution of 5 cm, we found no effect of image resolution on the spatially weighted mean TSEB estimates.For the studied grassland, the comparison of model estimates with EC observations indicates that all three models are able to reproduce observed energy fluxes with comparable accuracy with mean absolute errors of ET between 20 and 40 W m−2. The TSEB model showed larger deviations from the reference observations under cloudy conditions with rapid fluctuations of LST within the 30 min averaging period for EC. The two contextual models yielded similar results for most of the flights. The good performance of the DATTUTDUT model, which had the lowest input requirements of the three models, is especially promising in view of the application of UAS for routine near-real-time ET monitoring.

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Section: Resultsmentioning

confidence: 76%

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Brenner

Zeeman

Bernhardt

et al. 2018

International Journal of Remote Sensing

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“…This enables a partition of heat flux estimations into its components from canopy and soil respectively. This approach is hereafter referred to as TSEB-PT in order to differentiate it from other TSEB approaches, such as TSEB-LUE (Houborg et al, 2012), based on the Light Use Efficiency concept, or TSEB-2ART, which utilises dualangle LST observations for direct retrieval of soil and canopy temperatures (Guzinski et al, 2015). Remotely sensed LST may deviate from the actual surface temperature by several degrees Kelvin due to atmospheric and surface emissivity effects.…”

Section: H Hoffmann Et Al: Estimating Evaporation With Thermal Uav mentioning

confidence: 99%

“…For an in-depth review of the TSEB-PT and DTD models including all equations, see Guzinski et al (2014Guzinski et al ( , 2015.…”

Section: Dtdmentioning

confidence: 99%

Estimating evaporation with thermal UAV data and two-source energy balance models

Hoffmann

Nieto

Jensen

et al. 2016

Hydrol. Earth Syst. Sci.

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143

144

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Abstract. Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperaturedifference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

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“…For this purpose, satellite remote sensing comes into play as an independent data source with the required spatial resolution and coverage for many catchment-scale applications. Satellite imagery has been used for estimation of numerous states and fluxes of interest to hydrological modeling, such as snow cover (Immerzeel et al, 2009), groundwater storage change (Chen et al, 2016;Rodell et al, 2009;Sutanudjaja et al, 2013;Richey et al, 2015), soil moisture (SM; Wanders et al, 2014), vegetation water content (Mendiguren et al, 2015), land surface temperature (LST; Corbari et al, 2015) or actual evapotranspiration (ET; Guzinski et al, 2015). The conversions of the remotely sensed signal to hydrological variables are far from trivial and usually require in situ measurements and observations for model evaluation.…”

Section: G Mendiguren Et Al: a Remote-sensing-based Diagnostic Apprmentioning

confidence: 99%

Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

Mendiguren

Koch

Stisen

2017

Hydrol. Earth Syst. Sci.

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Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DKmodel), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DKmodel in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.

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Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

Cited by 19 publications

References 46 publications

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Estimating evaporation with thermal UAV data and two-source energy balance models

Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

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