Discussion of “Application of SEBAL Model for Mapping Evapotranspiration and Estimating Surface Energy Fluxes in South-Central Nebraska” by Ramesh K. Singh, Ayse Irmak, Suat Irmak, and Derrel L. Martin

Bastiaanssen, W.G.M.; Thoreson, Bryan; Clark, Byron; Davids, Grant

doi:10.1061/(asce)ir.1943-4774.0000216

Cited by 29 publications

(19 citation statements)

References 6 publications

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“…Major disadvantages of this method are that (1) Subjective specifications of representative hot/dry and wet/cool pixels within the image are required [92,93] to determine model parameters a and b. The resulting H flux and ET estimates from SEBAL can vary with differing extreme pixels selected by the operator, domain size, and spatial resolution of satellite sensors [94]; (2) Over mountainous regions, some adjustments are required based on a digital elevation model for T s and u to account for the lapse rate [95,96]; (3) Estimated H is greatly affected by the errors in surface-air temperature differences or surface temperatures measurements; and (4) Ignoring the effect of radiometer viewing angle, can cause variation in T s by several degrees for some images.…”

Section: Surface Energy Balance Algorithm For Land (Sebal)mentioning

confidence: 99%

“…However, application of SEBAL by Trezza [132] for a variety of crops in Kimberly, ID resulted in ET estimation errors ranging from 2.7% to 35% with an average error of 18.2%, while the results presented from our present study show a bias of evaporation fraction of 6.3% compared with the results of SEBAL using in-situ eddy covariance data. Moreover, over mountainous areas, SEBAL shows some limitations [95,96]. These limitations are solved in the METRIC model.…”

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

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Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

2014

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Abstract:With the advent of new satellite technology, the radiative energy exchanges between Sun, Earth, and space may now be quantified accurately. Nevertheless, much less is known about the magnitude of the energy flows within the climate system and at the Earth's surface, which cannot be directly measured by satellites. This review surveys the basic theories, observational methods, and different surface energy balance algorithms for estimating evapotranspiration (ET) from landscapes and regions with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Although some of these algorithms were built up for specific land covers like irrigation areas only, methods developed for other disciplines like hydrology and meteorology, are also reviewed, where continuous estimates in space and in time are needed. Temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evapotranspiration are also discussed. A review of these different satellite based remote sensing approaches is presented. The main physical bases and assumptions of these algorithms are also discussed. Some results are shown for the estimation of evapotranspiration on a rice paddy of Chiayi Plain in Taiwan using remote sensing data.

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Section: Surface Energy Balance Algorithm For Land (Sebal)mentioning

confidence: 99%

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

2014

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“…[41] Selection of end-members is another critical issue that has been controversial in the remote sensing-based ET estimation community. The model developers claimed difficulties and subjectivity in selecting end-members [e.g., Allen et al, 2007;Bastiaanssen et al, 2010], and other researchers have confirmed this through a number of theo-retical and experimental studies [e.g., Choi et al, 2009;French et al, 2005aFrench et al, , 2005bLong and Singh, 2012b;McVicar and Jupp, 1998;Timmermans et al, 2007]. This is because different extents of study sites and spatial resolutions of satellite images could have variable contrast in soil moisture and f c , which may fundamentally determine the magnitudes of T max and T min and therefore result in differing EF and LE retrievals.…”

Section: Whether the End-members Can Be Appropriately Selected Or Detmentioning

confidence: 99%

Assessing the impact of end‐member selection on the accuracy of satellite‐based spatial variability models for actual evapotranspiration estimation

Long

Singh

2013

Water Resources Research

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[1] This study examines the impact of end-member (i.e., hot and cold extremes) selection on the performance and mechanisms of error propagation in satellite-based spatial variability models for estimating actual evapotranspiration, using the triangle, surface energy balance algorithm for land (SEBAL), and mapping evapotranspiration with high resolution and internalized calibration (METRIC) models. These models were applied to the soil moisture-atmosphere coupling experiment site in central Iowa on two Landsat Thematic Mapper/Enhanced Thematic Mapper Plus acquisition dates in 2002. Evaporative fraction (EF, defined as the ratio of latent heat flux to availability energy) estimates from the three models at field and watershed scales were examined using varying end-members. Results show that the end-members fundamentally determine the magnitudes of EF retrievals at both field and watershed scales. The hot and cold extremes exercise a similar impact on the discrepancy between the EF estimates and the ground-based measurements, i.e., given a hot (cold) extreme, the EF estimates tend to increase with increasing temperature of cold (hot) extreme, and decrease with decreasing temperature of cold (hot) extreme. The coefficient of determination between the EF estimates and the ground-based measurements depends principally on the capability of remotely sensed surface temperature (T s ) to capture EF (i.e., depending on the correlation between T s and EF measurements), being slightly influenced by the end-members. Varying the end-members does not substantially affect the standard deviation and skewness of the EF frequency distributions from the same model at the watershed scale. However, different models generate markedly different EF frequency distributions due to differing model physics, especially the limiting edges of EF defined in the remotely sensed vegetation fraction (f c ) and T s space. In general, the end-members cannot be properly determined because (1) they do not necessarily exist within a scene, varying with the spatial extent, resolution, and quality of satellite images being used and/or (2) different operators can select different end-members. Furthermore, the limiting edge of EF ¼ 0 in the f c -T s space varies with the model, with SEBAL-type models having inherently an increasing curvilinear limiting edge of EF ¼ 0 with f c . The spatial variability models therefore require careful calibration in order to deduce reasonable EFlimiting edges and then confine the magnitudes of EF estimates.Citation: Long, D., and V. P. Singh (2013), Assessing the impact of end-member selection on the accuracy of satellite-based spatial variability models for actual evapotranspiration estimation, Water Resour.

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“…It is critically important to note that almost all studies pertaining to SEBAL, in particular those of the model developers [ Allen et al , 2007; Bastiaanssen et al , 2010], recognize the key role of extremes in the resulting estimates; a large uncertainty in the model outputs could be introduced by differing extremes manually identified by the operator. The consensus on the difficulty of selecting appropriate extremes, compounded by limited ground‐based measurements of surface fluxes which are rarely available in most developing countries, seems to hinder a greater understanding of the deficiencies of the model physics and a substantial improvement in this model.…”

Section: Introductionmentioning

confidence: 99%

A modified surface energy balance algorithm for land (M‐SEBAL) based on a trapezoidal framework

Long¹,

Singh²

2012

Water Resources Research

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[1] The surface energy balance algorithm for land (SEBAL) has been designed and widely used (and misused) worldwide to estimate evapotranspiration across varying spatial and temporal scales using satellite remote sensing over the past 15 yr. It is, however, beset by visual identification of a hot and cold pixel to determine the temperature difference (dT) between the surface and the lower atmosphere, which is assumed to be linearly correlated with surface radiative temperature (T rad ) throughout a scene. To reduce ambiguity in flux estimation by SEBAL due to the subjectivity in extreme pixel selection, this study first demonstrates that SEBAL is of a rectangular framework of the contextual relationship between vegetation fraction (f c ) and T rad , which can distort the spatial distribution of heat flux retrievals to varying degrees. End members of SEBAL were replaced by a trapezoidal framework of the f c -T rad space in the modified surface energy balance algorithm for land (M-SEBAL). The warm edge of the trapezoidal framework is determined by analytically deriving temperatures of the bare surface with the largest water stress and the fully vegetated surface with the largest water stress implicit in both energy balance and radiation budget equations. Areally averaged air temperature (T a ) across a study site is taken to be the cold edge of the trapezoidal framework. Coefficients of the linear relationship between dT and T rad can vary with f c but are assumed essentially invariant for the same f c or within the same f c class in M-SEBAL. SEBAL and M-SEBAL are applied to the soil moisture-atmosphere coupling experiment (SMACEX) site in central Iowa, U.S. Results show that M-SEBAL is capable of reproducing latent heat flux in terms of an overall root-mean-square difference of 41.1 W m À2 and mean absolute percentage difference of 8.9% with reference to eddy covariance tower-based measurements for three landsat thematic mapper/enhanced thematic mapper plus imagery acquisition dates in 2002. The retrieval accuracy of SEBAL is generally lower than M-SEBAL, depending largely on the selected extremes. Spatial distributions of heat flux retrievals from SEBAL are distorted to a certain degree due to its intrinsic rectangular framework.Citation: Long, D., and V. P. Singh (2012), A modified surface energy balance algorithm for land (M-SEBAL) based on a trapezoidal framework, Water Resour. Res., 48, W02528,

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Discussion of “Application of SEBAL Model for Mapping Evapotranspiration and Estimating Surface Energy Fluxes in South-Central Nebraska” by Ramesh K. Singh, Ayse Irmak, Suat Irmak, and Derrel L. Martin

Cited by 29 publications

References 6 publications

Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

Assessing the impact of end‐member selection on the accuracy of satellite‐based spatial variability models for actual evapotranspiration estimation

A modified surface energy balance algorithm for land (M‐SEBAL) based on a trapezoidal framework

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