SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey

International Water Management Institute, P.O. Box 2075, Colombo, Sri Lanka
Journal of Hydrology (Impact Factor: 2.96). 01/2000; DOI: 10.1016/S0022-1694(99)00202-4

ABSTRACT Surface Energy Balance Algorithm for Land (SEBAL) is a relatively new parameterization of surface heat fluxes based on spectral satellite measurements. SEBAL requires spatially distributed, visible, near-infrared and thermal infrared data, which can be taken from Landsat Thematic Mapper. The SEBAL parameterization is an iterative and feedback-based numerical procedure that deduces the radiation, heat and evaporation fluxes. The sensible and latent heat fluxes across the lower Gediz River Basin in Western Turkey have been estimated. The energy balance during satellite overpass, and the integrated 24 h fluxes are computed on a pixel-by-pixel basis. The temporal variability in heat fluxes between June and August will be evaluated. The effect of irrigation on the partitioning of energy and crop water stress is discussed.

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    ABSTRACT: Satellite-based thermal infrared remote sensing has greatly contributed to the development and improvement of remote sensing-based evapotranspiration (RS-ET) mapping algorithms. Radiometric temperature (Ts) derived from thermal sensors is inherently different from the aerodynamic temperature (To) required for solving the bulk formulation of sensible heat (H). The scalar roughness length (zoh) representing heat transport mechanism and described by the dimensionless parameter kB−1 was used to account for the discrepancy between Ts and To. Surface Energy Balance Algorithm for Land (SEBAL), with its indigenous approach of linearly relating dT (near-surface temperature gradient) with Ts across the imagery, maintained that this approach would absorb the impacts of differences between Ts and To. Therefore, it utilized a constant kB−1 value of 2.3 in its initial version, and later switched to a constant zoh (z1) value of 0.1. In this study, we investigated the influence of these changes in SEBAL by testing four different approaches: (i) zoh derived from a constant kB−1 of 2.3, (ii) constant zoh (z1) = 0.1 m, (iii) constant zoh (z1) = 0.01 m, and (iv) spatially variable zoh from kB−1 parameterization. SEBAL was applied on 10 high-resolution airborne images acquired during BEAREX07-08 (Bushland Evapotranspiration and Agricultural Remote Sensing Experiment) and validated against measurements from four large weighing lysimeters installed on two irrigated and two dryland fields. The spatially variable kB−1 produced statistically different and improved ET estimates compared to that with constant kB−1 and constant z1 (zoh) approaches. SEBAL performance for irrigated fields representing high ET and complete ground cover surfaces was markedly different from that for dryland fields representing greater soil water deficits with sparser vegetation cover. A variable kB−1 value derived from a physical model generated good overall estimates while delivering improved performance for dryland agricultural systems. Overall, this study focused on the classical problem of estimating heat transfer from two contrasting hydrological regimes i.e. irrigated and dryland agriculture and illustrated the existing need for a realistic consideration of excess resistance to heat transfer in single-source resistance modeling frameworks.
    Journal of Hydrology 12/2013; 509:231-244. · 2.96 Impact Factor
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    ABSTRACT: The irrigated Indus Basin in Pakistan has insufficient water resources to supply all its stakeholders. Information on evaporative depletion across the Basin is an important requirement if the water resources are to be managed efficiently. This paper presents the Surface Energy Balance Algorithm for Land (SEBAL) method used to compute actual evapotranspiration for large areas based on public domain National Oceanic and Atmospheric Administration (NOAA) satellite data. Computational procedures for retrieving actual evapotranspiration from satellites have been developed over the last 20 years. The current work is among the first applications used to estimate actual evapotranspiration on an annual scale across a vast river basin system with a minimum of ground data. Only sunshine duration and wind speed are required as input data for the remote sensing flux algorithm. The results were validated in the Indus Basin by comparing results from a field-scale transient moisture flow model, in situ Bowen ratio measurements, and residual water balance analyses for an area of 3 million ha. The accuracy of assessing time-integrated actual annual evapotranspiration varied from 0% to 10% on a field scale to 5% at the regional level. Spatiotemporal information on actual evapotranspiration helps to evaluate water distribution and water use between large irrigation project areas. Wide variations in evaporative depletion between project areas and crop types were found. Satellite-based measurements can provide such information and avoid the need to rely on field databases.
    Water Resources Research 01/2002; 38(12). · 3.15 Impact Factor
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    ABSTRACT: Estimation of evapotranspiration (ET) over large heterogeneous areas using numerous satellite-based algorithms is increasing; however, further analysis of uncertainties is limited. The objective of this study was to evaluate impacts of varying input variables, size of the modeling domain, and spatial resolution of satellite sensors on sensible heat flux (H) estimates from the Surface Energy Balance Algorithm for Land (SEBAL). First, sensitivity analysis of SEBAL is conducted by varying its input variables using Moderate Resolution Imaging Spectroradiometer (MODIS) data for 29 cloud-free days in 2007 covering the Baiyangdian watershed in North China. Domain dependence of the H estimates is quantified by estimating H for subwatersheds of different sizes and the entire watershed using MODIS data for 4 cloud-free days in May 2007. Landsat Thematic Mapper (TM) and MODIS based H estimates are compared to evaluate the effect of spatial resolution of satellite sensors. Results of sensitivity analysis indicate that the H estimates from SEBAL are most sensitive to temperatures of hot and cold pixels and available energy of the hot pixel. Results of domain dependence show that the mean absolute percentage difference (MAPD) and root mean square deviation (RMSD) in the H estimates between different domain sizes up to 53.9% and 75.7 W m-2, respectively. Although areally averaged H estimates from MODIS and Landsat TM sensors are similar, the MODIS-based H estimates show an RMSD of 52.3 W m-2 and a bias of 26.5 W m-2 relative to Landsat TM-based counterparts. Unlike other models, the standard deviation of H estimates from SEBAL using high spatial resolution images can be smaller than that using low spatial resolution images. Furthermore, H estimates from the input upscaling scheme (aggregating input variables) are generally consistent with those from the output upscaling scheme (aggregating the output) for the same sensor, given similar differences between hot and cold pixels for low and high spatial resolution. The resulting H flux and ET estimates from SEBAL can therefore vary with differing extreme pixels selected by the operator, domain size, and spatial resolution of satellite sensors. This study provides insights into various factors that should be considered when applying SEBAL to estimate ET and helps correctly interpret the SEBAL outputs.
    Journal of Geophysical Research 11/2011; 116(D21):21107-. · 3.17 Impact Factor


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Jun 4, 2014