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.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Riparian vegetation represents significant areas of water use in landscapes. An understanding of hydrology and vegetation dynamics within riparian zones is important for the development of ecologically appropriate management plans for these complex systems. To achieve this, measures of vegetation extent, health and activity are required, as these provide important information about the status of riparian vegetation. This paper demonstrates the ability of remotely sensed data to provide information about vegetation extent, health, and water use. This study shows that remotely sensed data provide repeatable, objective measurements of riparian vegetation status. The ability of remotely sensed data to provide information as a time series creates the capacity to identify temporal changes and trends in vegetation status as well as provide information at a single point in time. Satellite imagery also provides information on the spatial distribution of these variables, which offers valuable insight into the diversity present within the ecosystem. These factors all provide important information for the management of riparian systems. Introduction This study aimed to develop measurement and monitoring tools that addressed extent, health and activity of significant riparian sites in southern Australia. This paper demonstrates how measures of riparian vegetation extent, health and water use can be derived from remotely sensed data, and how these data can potentially improve management of these systems.
    15th Australian Remote Sensing and Photogrammetric Conference, Alice Springs, Northern Territory; 09/2010
  • Source
    Zhengjia Liu, Quanqin Shao, Jiyuan Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: The aims are to validate and assess the performances of MODIS gross primary production (MODIS-GPP) and evapotranspiration (MODIS-ET) products in China's different land cover types and their sensitivity to remote sensing input data. In this study, MODIS-GPP and -ET are evaluated using flux derived/measured data from eight sites of ChinaFLUX. Results show that MODIS-GPP generally underestimates GPP (R 2 is 0.58, bias is −6.7 gC/m 2 /8-day and RMSE is 19.4 gC/m 2 /8-day) at all sites and MODIS-ET overestimates ET (R 2 is 0.36, bias is 6 mm/8-day and RMSE is 11 mm/8-day) when comparing with derived GPP and measured ET, respectively. For evergreen forests, MODIS-GPP gives a poor performance with R 2 varying from 0.03 to 0.44; in contrast, MODIS-ET provides more reliable results. In croplands, MODIS-GPP can explain 80% of GPP variance, but it overestimates flux derived GPP in non-growing season and underestimates flux derived GPP in growing season; similar overestimations also presented in MODIS-ET. For grasslands and mixed forests, MODIS-GPP and -ET perform good estimating accuracy. By designing four experimental groups and taking GPP simulation as an example, we suggest that the maximum light use efficiency of croplands should be optimized, and the differences of meteorological data have little impact on GPP estimation, whereas remote sensing leaf area index/fraction of photo-synthetically active radiation (LAI/FPAR) can greatly affect GPP/ET estimations for all land cover types. Thus, accurate remote sensing parameters are important for achieving reliable estimations. OPEN ACCESS Remote Sens. 2015, 7(1)
    Remote Sensing 12/2014; 7(1):135-152. · 2.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Remote sensing can provide estimates of spatially distributed actual evapotranspiration (ET) at different spatial scales. Methods that combine remotely sensed observations with other ground-based information on land and atmospheric properties usually allow improved estimates of spatially distributed evapotranspiration (ET) than when remotely sensed data are used alone. Spatial resolution of remotely sensed ET estimates depends on the specifications of the satellite instruments (e.g., 60 m for Landsat, 1 km for MODIS and 10 km for AVHRR). Since satellite sensors have different spatial, spectral and radiometric resolutions, it is essential to understand the consistency of ET estimates from different sensors. The main objective of this paper is to understand the spatial scaling effects of remotely sensed ET estimates from coarse resolution AVHRR data, through the analysis of MODIS data in a highly heterogeneous catchment. This study was undertaken in the Musi catchment in the Krishna River Basin, India. The average land parcel size in this catchment is 0.5 ha with highly variable crops and land management practices. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate spatially distributed evapotranspiration by combining ground-based metrological data and remotely sensed data from MODIS sensor. Observed pan evaporation data from weather station were used for validation of the 250m resolution MODIS ET output, before up scaling to course resolution products. In order to quantify the difference in ET estimates originating solely from the observation scale, ET estimates were aggregated to the AVHRR scale using two different approaches. First, high-resolution ET was estimated at the original pixel resolution of MODIS and then aggregated to the coarser resolution scale (output up scaling). Second, ET estimated directly at the coarser resolution by aggregating the fine resolution input data to the coarse resolution scale (input up-scaling). The relationship between the sub-pixel-scale heterogeneity and scaling effects on ET estimate is investigated and other factors affecting the observed discrepancies between ET estimates from MODIS and AVHRR are discussed. Overall a 0.12mm difference observed in daily ET between the two up scaled process at catchment scale analysis.
    MODSIM 2011; 12/2011


Available from
Jun 4, 2014