SEBS: Surface Energy Balance System
A model for estimating turbulent heat fluxes and surface evapotranspiration from remote sensing data
SEBS is a single-source surface energy balance model, which estimates atmospheric turbulent fluxes and surface evaporative fraction from remote sensing data. The SEBS algorithm, which is described in the article by Z. Su in Hydrology & Earth System Sciences in 2002: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, has been implemented in the PCRaster Python modelling framework by Hans van der Kwast and Derek Karssenberg. The current setup of SEBS requires three sets of input data: (1) Data derived from remote sensing: albedo, emissivity, temperature and Normalized Difference Vegetation Index (NDVI) to derive surface roughness parameters; (2) Meteorological parameters at a reference site (air pressure, temperature, relative humidity, wind speed); (3) Radiation data (downward solar radiation, downward longwave radiation). The model consistis of three modules: (1) submodel to derive energy balance terms; (2) submodel to derive stability parameters and (3) submodel to derive roughness length for heat transfer. Using these three modules, the energy balance for limiting cases (i.e, completely wet or dry pixels) can be resolved. Consequently, the energy balance terms, relative evaporation, evaporative fraction and evaporation flux can be derived for all pixels.
Latent heat flux (W/m2) modelled by SEBS. Rabat region, Morocco.
The PCRaster Python source code of the model is available on GitHub. You can also place your questions, suggestions or corrections there. The repository includes an example dataset from a study in Sehoul, Morocco. Remote sensing data has been derived from an ASTER satellite image and meteorological data comes from a temporary meteorological station. The script also shows how a simple user interface can be used in Python.
Su, Z. (2001). A Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes from point to continental scale. In: Su, Z. & C.E. Jacobs (Eds.) Advanced earth observation – land surface climate, final report. pp. 184: Publications of the National Remote Sensing Board (BCRS). USP-2.
Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology & Earth System Sciences 6, pp. 85-99.
Karssenberg, D., De Jong, K., Van der Kwast, J. (2007). Modelling landscape dynamics with Python. International Journal of Geographical Information Science 5, 483-495.
De Jong, S.M., Van der Kwast, J., Addink, E. & Su, B. (2008). Remote sensing for hydrological studies. In: Bierkens, M.F.P., Dolman, A.J. & Troch, P.A. (Eds.). Climate and the hydrological cycle. IAHS Special Publication, Vol. 8. pp.297-321.
Timmermans, W.J., Van der Kwast, J., Gieske, A.S.M., Su, Z., Olioso, A., Jia, L., Elbers, J. (2005). Intercomparison of energy flux models using ASTER imagery at the SPARC 2004 site (Barrax, Spain), SPARC Final Workshop. ESA Proceedings WPP-250, ITC Enschede, The Netherlands.
Van der Kwast, J. (2009). Quantification of top soil moisture patterns : Evaluation of field methods, process-based modelling, remote sensing and an integrated approach. PhD Thesis. KNAG/Fac. Geowetenschappen, 313 p.
Van der Kwast, J., Timmermans, W., Gieske, A., Su, Z., Olioso, A., Jia, L., Elbers, J., Karssenberg, D. and de Jong, S. (2009). Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER Imagery with Flux-measurements at the SPARC 2004 Site (Barrax, Spain). Hydrol. Earth Syst. Sci., 13, 1337-1347