Thermal-based Two-Source Energy Balance Research Articles

Applications of a Thermal-Based Two-Source Energy Balance Model Coupling the Sun-Induced Chlorophyll Fluorescence Data

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> ), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> are complex and difficult to derive empirically. An empirical SIF- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> and soil evaporation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> . By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> from ET, while the TSEB model tended to overestimate the contribution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> to ET.

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.

Evaluating different metrics from the thermal-based two-source energy balance model for monitoring grapevine water stress

Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon.

Applications of a thermal-based two-source energy balance model coupled to surface soil moisture

The two-source energy balance (TSEB) model using the land surface temperature (LST) as a key boundary has been used to estimate land surface evapotranspiration (ET) over various landcovers and environmental conditions. However, LST may not always provide an adequate boundary condition to simultaneously constrain the soil evaporation and plant transpiration especially under water limited conditions. A refinement to TSEB model by coupling surface soil moisture information to derive the soil and vegetation component temperatures and a new transpiration algorithm was developed (TSEB-SM). The TSEB-SM model was evaluated under a wide range of surface soil water content values and vegetation cover conditions and compared with the performance with the original TSEB model using only LST. While the results showed that the TSEB-SM model produced similar agreement in the fluxes and ET as the original TSEB for the cropland and grassland sites, TSEB-SM model performance was notably improved at the shrub-forest and desert steppe sites with a significant reduction in mean absolute percent difference in daily ET from nearly 65% to 25% and from approximately 50% to 40%, respectively. It also appears to be more reliable in partitioning ET into soil evaporation and plant transpiration when compared to the partitioning using the water use efficiency (uWUE) approach in combination with the eddy covariance measurements. With satellite data such as MODIS LST and leaf area index, and surface soil moisture retrievals from microwave satellite observations, the TSEB-SM model may potentially be a more reliable tool for monitoring regional ET partitioning under sparse canopy cover conditions.

An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

Abstract. Thermal-based two-source energy balance modeling is essential to estimate the land evapotranspiration (ET) in a wide range of spatial and temporal scales. However, the use of thermal-derived land surface temperature (LST) is not sufficient to simultaneously constrain both soil and vegetation flux components. Therefore, assumptions (about either soil or vegetation fluxes) are commonly required. To avoid such assumptions, an energy balance model, TSEB-SM, was recently developed by Ait Hssaine et al. (2018b) in order to consider the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc) normally used. While TSEB-SM has been successfully tested using in situ measurements, this paper represents its first evaluation in real life using 1 km resolution satellite data, comprised of MODIS (MODerate resolution Imaging Spectroradiometer) for LST and fc data and 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations. The approach is applied during a 4-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco. The field used was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural seasons, while it remained unploughed (as bare soil) during the 2015–2016 (B1) agricultural season. The classical TSEB model, which is driven only by LST and fc data, significantly overestimates latent heat fluxes (LE) and underestimates sensible heat fluxes (H) for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W m−2 for LE and −104, −71, −128 and −181 W m−2 for H, for S1, S2, S3 and B1, respectively. Meanwhile, when using TSEB-SM (SM and LST combined data), these errors are significantly reduced, resulting in mean bias values estimated as 39, 4, 7 and 62 W m−2 for LE and −10, 24, 7, and −59 W m−2 for H, for S1, S2, S3 and B1, respectively. Consequently, this finding confirms again the robustness of the TSEB-SM in estimating latent/sensible heat fluxes at a large scale by using readily available satellite data. In addition, the TSEB-SM approach has the original feature to allow for calibration of its main parameters (soil resistance and Priestley–Taylor coefficient) from satellite data uniquely, without relying either on in situ measurements or on a priori parameter values.

Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem

The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.

Evaluating Soil Resistance Formulations in Thermal‐Based Two‐Source Energy Balance (TSEB) Model: Implications for Heterogeneous Semiarid and Arid Regions

AbstractRelatively small fluctuations in the surface energy balance and evapotranspiration in semiarid and arid regions can be indicative of significant changes to ecosystem health. Therefore, it is imperative to have approaches for monitoring surface fluxes in these regions. The remote sensing‐based two‐source energy balance (TSEB) model is a suitable method for flux estimation over sparsely vegetated semiarid and arid landscapes since it explicitly considers surface energy flux contributions from soil and vegetation. However, previous studies indicate that TSEB generally underestimates sensible heat flux (H) and hence overestimates latent heat flux (LE) or evapotranspiration for these regions unless soil resistance coefficients are modified based on additional ground information. In this study, TSEB is applied over semiarid and arid regions on three continents using the original soil resistance formulation with modified coefficients and a recently developed physically based soil resistance formulation. Model sensitivity analysis demonstrates the high sensitivity of TSEB with original soil resistance formulation to soil resistance coefficients, while TSEB with the new soil resistance formulation has relatively low sensitivity to uncertainties in all coefficients. The performance of TSEB using different soil resistance formulations is evaluated by comparing modeled H against eddy covariance measurements in six semiarid and arid study sites and ranking the error statistics. Our results indicate that incorporating the new soil resistance formulation into TSEB would enhance its utility in flux estimation over heterogeneous landscapes by obviating its reliance on semiempirical coefficients and thus provide more robust fluxes over sparsely vegetated regions without model calibration and/or parameter tuning.

Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery.

The thermal-based Two-Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand these effects, very high spatial resolution remote-sensing data from an unmanned aerial vehicle were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite T rad, and assume initially that the canopy transpires at the Priestley-Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery partition T rad into soil and canopy component temperatures, which are then used to drive the TSEB without requiring a priori assumptions regarding initial canopy transpiration rate. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and T rad to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high-resolution remote-sensing data for estimating ET and E and T partitioning at the canopy level is also discussed.

Utility of the two-source energy balance (TSEB) model in vine and interrow flux partitioning over the growing season

For monitoring water use in vineyards, it becomes important to evaluate the evapotranspiration (ET) contributions from the two distinct management zones: the vines and the interrow. Often the interrow is not completely bare soil but contains a cover crop that is senescent during the main growing season (nominally May–August), which in Central California is also the dry season. Drip irrigation systems running during the growing season supply water to the vine plant and re-wet some of the surrounding bare soil. However, most of the interrow cover crop is dry stubble by the end of May. This paper analyzes the utility of the thermal-based two-source energy balance (TSEB) model for estimating daytime ET using tower-based land surface temperature (LST) estimates over two Pinot Noir (Vitis vinifera) vineyards at different levels of maturity in the Central Valley of California near Lodi, CA. The data were collected as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Local eddy covariance (EC) flux tower measurements are used to evaluate the performance of the TSEB model output of the fluxes and the capability of partitioning the vine and cover crop transpiration (T) from the total ET or T/ET ratio. The results for the 2014–2016 growing seasons indicate that TSEB output of the energy balance components and ET, particularly, over the daytime period yield relative differences with flux tower measurements of less than 15%. However, the TSEB model in comparison with the correlation-based flux partitioning method overestimates T/ET during the winter and spring through bud break, but then underestimates during the growing season. A major factor that appears to affect this temporal behavior in T/ET is the daily LAI used as input to TSEB derived from a remote sensing product. An additional source of uncertainty is the use of local tower-based LST measurements, which are not representative of the flux tower measurement source area footprint.

Evaluation of soil resistance formulations for estimates of sensible heat flux in a desert vineyard

For irrigated vineyards, accurate estimates of the sensible heat flux from the soil surface (Hs) is essential for determining the contribution of soil evaporation (E) to evapotranspiration (ET) using thermal-based energy balance approaches. A key to an accurate estimate of Hs is a robust physically-based soil resistance formulation. Here we compare the performance of two soil resistance formulations: a conventional resistance model (rKN) derived from field and laboratory studies which has been extensively implemented in the thermal-based Two-Source Energy Balance (TSEB) model, and a recently developed physically-based soil resistance formulation (rHO) that explicitly accounts for near-surface interactions affecting scalar fluxes at the soil surface in the presence of bluff-body roughness elements. Estimates of Hs using the two resistance formulations were evaluated using in-situ observations from a drip-irrigated vineyard in the arid central Negev Highlands of Israel. The results indicate that the soil resistance model rHO outperforms the rKN formulation using standard model coefficients and provides robust estimates of Hs independent of model calibration or parameter tuning. This offers an opportunity to advance the utility of TSEB model when applied to sparsely vegetated areas where ground-based calibration data are not available for adjusting coefficients in the rKN formulation, and potentially improves its practical applications to heterogeneous landscapes by obviating its reliance on semi-empirical coefficients.

Estimation of surface energy fluxes in the Arctic tundra using the remote sensing thermal-based Two-Source Energy Balance model

Abstract. The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is key to forecasting ecological responses to changing climate conditions in the Arctic. An extensive local evaluation of the Two-Source Energy Balance model (TSEB) – a remote-sensing-based model using thermal infrared retrievals of land surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for Arctic tundra. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean turbulent flux errors generally less than 50 W m−2 at half-hourly time steps, similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. The MODIS leaf area index (LAI) remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data on the local biomass amount. Model refinements found in this work at the local scale build toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions

In this study ground measured soil and vegetation component temperatures and composite temperature from a high spatial resolution thermal camera and a network of thermal-IR sensors collected in an irrigated maize field and in an irrigated cotton field are used to assess and refine the component temperature partitioning approach in the Two-Source Energy Balance (TSEB) model. A refinement to TSEB using a non-iterative approach based on the application of the Priestley-Taylor formulation for surface temperature partitioning and estimating soil evaporation from soil moisture observations under advective conditions (TSEB-A) was developed. This modified TSEB formulation improved the agreement between observed and modeled soil and vegetation temperatures. In addition, the TSEB-A model output of evapotranspiration (ET) and the components evaporation (E), transpiration (T) when compared to ground observations using the stable isotopic method and eddy covariance (EC) technique from the HiWATER experiment and with microlysimeters and a large monolithic weighing lysimeter from the BEAREX08 experiment showed good agreement. Difference between the modeled and measured ET measurements were less than 10% and 20% on a daytime basis for HiWATER and BEAREX08 data sets, respectively. The TSEB-A model was found to accurately reproduce the temporal dynamics of E, T and ET over a full growing season under the advective conditions existing for these irrigated crops located in arid/semi-arid climates. With satellite data this TSEB-A modeling framework could potentially be used as a tool for improving water use efficiency and conservation practices in water limited regions. However, TSEB-A requires soil moisture information which is not currently available routinely from satellite at the field scale.

Utility of a Thermal-based Two-source Energy Balance Model for Estimating Surface Fluxes over Complex Landscapes

Many landscapes are comprised of a variety of vegetation types with different canopy structure, rooting depth physiological characteristics, including r sponse to environmental stressors, etc. Even in agricultural regions different management practices, including crop rotations, irrigation scheduling, planting density, seed varieties, and other factors result in complex patterns in vegetation growth stages, canopy cover, canopy architecture and cropping densities. This variability at the canopy, field and landscape scale, makes it very challenging for quantifying spatially-distributed surface fluxes. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/substrate and vegetation exchange processes affecting the radiative balance and turbulent energy transport with the overlying atmosphere. The thermal-based model, called the Two-Source Energy Balance (TSEB) model solves for the soil/substrate and canopy temperatures that achieves a balance in the radiation and turbulent heat flux exchange with the lower atmosphere for the soil/substrate and vegetation elements. The TSEB scheme permits interaction between soil/substrate and canopy elements which are both coupled to the atmosphere via the canopy-air temperature; this canopy-air temperature is highly correlated to the aerodynamic surface temperature used in computing surface sensible heat flux. As a result, the TSEB modeling framework is applicable to a wide range of atmospheric and canopy cover conditions. An overview of recent applications of the TSEB modeling framework to a variety of agricultural landscapes is presented.

Using leaf chlorophyll to parameterize light-use-efficiency within a thermal-based carbon, water and energy exchange model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C ab into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO 2 and energy fluxes using an analytical, light-use-efficiency (LUE) approach to estimating bulk canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes, internally estimating fluctuations in effective LUE from a nominal (species-dependent) value (LUE n) in response to short-term variations in environmental conditions. LUE n, however, may vary on a daily timescale, responding to changes in plant phenology, physiological condition and nutrient status. Therefore, remote sensing methodologies for improving daily estimates of LUE n have been investigated. Day-to-day variations in LUE n were assessed for a heterogeneous corn crop field in Maryland, U.S.A. through model optimization with eddy covariance CO 2 flux tower observations. The optimized daily LUE n values were then compared to gridded estimates of C ab over the tower flux footprint, retrieved from a canopy reflectance model driven by green, red and near-infrared imagery acquired with an aircraft imaging system. The tower-calibrated LUE n data were generally well correlated with airborne retrievals of C ab, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C ab for delineating spatio-temporal variations in LUE n. The study highlights the potential synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for estimating carbon, water and heat fluxes within a two-source energy balance framework.

The impact of in-canopy wind profile formulations on heat flux estimation in an open orchard using the remote sensing-based two-source model

Abstract. For open orchard and vineyard canopies containing significant fractions of exposed soil (&gt;50%), typical of Mediterranean agricultural regions, the energy balance of the vegetation elements is strongly influenced by heat exchange with the bare soil/substrate. For these agricultural systems a "two-source" approach, where radiation and turbulent exchange between the soil and canopy elements are explicitly modelled, appears to be the only suitable methodology for reliably assessing energy fluxes. In strongly clumped canopies, the effective wind speed profile inside and below the canopy layer can strongly influence the partitioning of energy fluxes between the soil and vegetation components. To assess the impact of in-canopy wind profile on model flux estimates, an analysis of three different formulations is presented, including algorithms from Goudriaan (1977), Massman (1987) and Lalic et al. (2003). The in-canopy wind profile formulations are applied to the thermal-based two-source energy balance (TSEB) model developed by Norman et al. (1995) and modified by Kustas and Norman (1999). High resolution airborne remote sensing images, collected over an agricultural area located in the western part of Sicily (Italy) comprised primarily of vineyards, olive and citrus orchards, are used to derive all the input parameters needed to apply the TSEB. The images were acquired from June to October 2008 and include a relatively wide range of meteorological and soil moisture conditions. A preliminary sensitivity analysis of the three wind profile algorithms highlights the dependence of wind speed just above the soil/substrate to leaf area index and canopy height over the typical range of canopy properties encountered in these agricultural areas. It is found that differences among the models in wind just above the soil surface are most significant under sparse and medium fractional cover conditions (15–50%). The TSEB model heat flux estimates are compared with micro-meteorological measurements from a small aperture scintillometer and an eddy covariance tower collected over an olive orchard characterized by moderate fractional vegetation cover (≈35%) and relatively tall crop (≈3.5 m). TSEB fluxes for the 7 image acquisition dates generated using both the Massman and Goudriaan in-canopy wind profile formulations give close agreement with measured fluxes, while the Lalic et al. equations yield poor results. The Massman wind profile scheme slightly outperforms that of Goudriaan, but it requires an additional parameter accounting for the roughness sub-layer of the underlying vegetative surface. The analysis also suggests that within-canopy wind profile model discrepancies become important, in terms of impact on modelled sensible heat flux, only for sparse canopies with moderate vegetation coverage.