Quantifying the uncertainty in estimates of surface–atmosphere fluxes through joint evaluation of the SEBS and SCOPE models

Hydrology and Earth System Sciences - Tập 17 Số 4 - Trang 1561-1573
J. Timmermans1, Zhongbo Su1, Christiaan van der Tol1, Anne Verhoef2, W. Verhoef1
1University of Twente, Faculty for Geoinformation Sciences and Earth Observation (ITC), the Netherlands
2Soil Research Centre, Department of Geography and Environmental Science, The University of Reading, UK

Tóm tắt

Abstract. Accurate estimation of global evapotranspiration is considered to be of great importance due to its key role in the terrestrial and atmospheric water budget. Global estimation of evapotranspiration on the basis of observational data can only be achieved by using remote sensing. Several algorithms have been developed that are capable of estimating the daily evapotranspiration from remote sensing data. Evaluation of remote sensing algorithms in general is problematic because of differences in spatial and temporal resolutions between remote sensing observations and field measurements. This problem can be solved in part by using soil-vegetation-atmosphere transfer (SVAT) models, because on the one hand these models provide evapotranspiration estimations also under cloudy conditions and on the other hand can scale between different temporal resolutions. In this paper, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model is used for the evaluation of the Surface Energy Balance System (SEBS) model. The calibrated SCOPE model was employed to simulate remote sensing observations and to act as a validation tool. The advantages of the SCOPE model in this validation are (a) the temporal continuity of the data, and (b) the possibility of comparing different components of the energy balance. The SCOPE model was run using data from a whole growth season of a maize crop. It is shown that the original SEBS algorithm produces large uncertainties in the turbulent flux estimations caused by parameterizations of the ground heat flux and sensible heat flux. In the original SEBS formulation the fractional vegetation cover is used to calculate the ground heat flux. As this variable saturates very fast for increasing leaf area index (LAI), the ground heat flux is underestimated. It is shown that a parameterization based on LAI reduces the estimation error over the season from RMSE = 25 W m−2 to RMSE = 18 W m−2. In the original SEBS formulation the roughness height for heat is only valid for short vegetation. An improved parameterization was implemented in the SEBS algorithm for tall vegetation. This improved the correlation between the latent heat flux predicted by the SEBS and the SCOPE algorithm from −0.05 to 0.69, and led to a decrease in difference from 123 to 94 W m−2 for the latent heat flux, with SEBS latent heat being consistently lower than the SCOPE reference. Lastly the diurnal stability of the evaporative fraction was investigated.

Từ khóa


Tài liệu tham khảo

Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., and Holtslag, A. A. M.: A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, J. Hydrol., 212–213, 198–212, 1998.

Bosveld, F., Holtslag, A. A. M., and Van Den Hurk, B. J. J. M.: Interpretation of Crown Radiation Temperatures of a Dense Douglas fir Forest with Similarity Theory, Bound.-Layer Meteor., 92, 429–451, https://doi.org/10.1023/a:1002087526720, 1999.

Brutsaert, W.: Aspects of Bulk Atmospheric Boundary Layer Similarity Under Free-Convective Conditions, Rev. Geophys., 37, 439–451, https://doi.org/10.1029/1999rg900013, 1999.

Brutsaert, W.: Hydrology, Cambridge University Press, New york, 605 pp., 2005.

Carlson, T.: An Overview of the "Triangle Method" for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery, Sensors, 7, 1612–1629, 2007.

Cowan, I.: Stomatal behaviour and environment, Adv. Bot. Res. 4, 114–228, 1997

Farah, H. O., Bastiaanssen, W. G. M., and Feddes, R. A.: Evaluation of the temporal variability of the evaporative fraction in a tropical watershed, Inte. J. Appl. Earth Obs., 5, 129–140, https://doi.org/10.1016/j.jag.2004.01.003, 2004.

Farquhar, G. D., Caemmerer, S, and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, 1980.

Ghilain, N., Arboleda, A., and Gellens-Meulenberghs, F.: Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data, Hydrol. Earth Syst. Sci., 15, 771–786, https://doi.org/10.5194/hess-15-771-2011, 2011.

Glenn, E. P., Huete, A. R., Nagler, P. L., Hirschboeck, K. K., and Brown, P.: Integrating remote sensing and ground methods to estimate evapotranspiration, CRC Cr. Rev. Plant Sci., 26, 139–168, https://doi.org/10.1080/07352680701402503, 2007.

Hartogensis, O.: Exploring Scintillometry in the Stable Atmospheric Surface Layer, PhD, Meteorologie en Luchtkwaliteit, Wageningen Universiteit, Wageningen, 240 pp., 2006.

Jacobs, A. F. G., Halbersma, J., and Przybula, C.: Behaviour of Crop resistance of maize during a growing season, Estimation of Areal Evapotranspiration, Vancouver, Canada, 1989.

Jia, L.: Modeling heat exchanges at the land-atmosphere interface using multi-angular thermal infrared measurements, Ph.D, Wageningen University, Wageningen, 199 pp., 2004.

Jia, L., Su, Z. B., van den Hurk, B., Menenti, M., Moene, A., De Bruin, H. A. R., Yrisarry, J. J. B., Ibanez, M., and Cuesta, A.: Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements, Phys. Chem. Earth, 28, 75–88, https://doi.org/10.1016/s1474-7065(03)00009-3, 2003.

Jiang, L., Islam, S., and Carlson, T. N.: Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data, Can. J. Remote Sens., 30, 769–787, https://doi.org/10.5589/m04-038, 2004.

Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S. I., McCabe, M. F., Wood, E. F., Rossow, W. B., Balsamo, G., Betts, A. K., Dirmeyer, P. A., Fisher, J. B., Jung, M., Kanamitsu, M., Reichle, R. H., Reichstein, M., Rodell, M., Sheffield, J., Tu, K., and Wang, K.: Global intercomparison of 12 land surface heat flux estimates, J. Geophys. Res., 116, D02102, https://doi.org/10.1029/2010jd014545, 2011.

Kalma, J. D., McVicar, T. R., and McCabe, M. F.: Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data, Surv. Geophys., 29, 421–469, https://doi.org/10.1007/s10712-008-9037-z, 2008.

Kite, G. W. and Droogers, P.: Comparing evapotranspiration estimates from satellites, hydrological models and field data, J. Hydrol., 229, 3–18, 2000.

Kljun, N., Calanca, P., Rotach, M., and Schmid, H.: A Simple Parameterisation for Flux Footprint Predictions, Bound.-Lay. Meteorol., 112, 503–523, https://doi.org/10.1023/b:boun.0000030653.71031.96, 2004.

Kustas, W. P. and Daughtry, C. S. T.: Estimation of the soil heat flux/ net radiation ratio from spectral data, Agr. Forest Meteorol., 49, 205–223, 1989a.

Kustas, W. P. and Daughtry, C. S. T.: Estimation of the soil heat flux/net radiation from spectral data, Agric. For. Meteorol., 49, 205–223, 1989b.

Kustas, W. P. and Norman, J. M.: A Two-Source Energy Balance Approach Using Directional Radiometric Temperature Observations for Sparse Canopy Covered Surfaces, Agron. J., 92, 847–854, 2000.

Kustas, W. P., Daughtry, C. S. T., and Oevelen, P.: Analytical treatment of the relationships between soil heat flux/net radiation ratio and vegetation indices, Remote sensing of environment, 46, 319–330, 1993.

Li, S., Kang, S., Li, F., Zhang, L., and Zhang, B.: Vineyard evaporative fraction based on eddy covariance in an arid desert region of Northwest China, Agr. Water Manage., 95, 937–948, 2008.

Liu, Q. H., Huang, H. G., Qin, W. H., Fu, K. H., and Li, X. W.: An extended 3-D radiosity-graphics combined model for studying thermal-emission directionality of crop canopy, IEEE Trans. Geosci. Remote Sens., 45, 2900–2918, https://doi.org/10.1109/tgrs.2007.902272, 2007a.

Liu, Shaomin, Lu, L., Mao, D., and Jia, L.: Evaluating parameterizations of aerodynamic resistance to heat transfer using field measurements, Hydrol. Earth Syst. Sci., 11, 769–783, https://doi.org/10.5194/hess-11-769-2007, 2007b.

Lu, X. and Zhuang, Q. L.: evaluating evapotranspiration and water use efficiency of terrestrial ecosystems in the conterminous united states using modis and ameriflux, Remote Sens. Environ., 114, 1924–1939, 2010.

Massman, W. J.: A model study of kBH-1 for vegetated surfaces using ["]localized near-field" Lagrangian theory, J. Hydrol., 223, 27–43, 1999.

McCabe, M. F. and Wood, E. F.: Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors, Remote Sens. Environ., 105, 271–285, https://doi.org/10.1016/j.rse.2006.07.006, 2006.

McNaughton, K. G. and van den Hurk, B. J. J. M.: A "Lagrangian" revision of the resistors in the two-layer model for calculating the energy budget of a plant canopy, Bound.-Layer Meteor., 74, 261–288, 1995.

Monin, A. S. and Obukhov, A. M.: Osnovnye zakonomernosti turbulentnogo peremesivanija v prizemnom sloe atmosfery, Trudy geofiz. inst. AN SSSR, 24, 163–187, citeulike-article-id:3716139, 1954.

Monteith, J. L.: Principles of environmental physics, edited by: Press, E. A., 1973.

Mu, Q., Heinsch, F. A., Zhao, M., and Running, S. W.: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sens. Environ., 111, 519–536, https://doi.org/10.1016/j.rse.2007.04.015, 2007.

Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.

Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M., Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J. B., Guo, Z., Jung, M., Maignan, F., McCabe, M. F., Reichle, R. H., Reichstein, M., Rodell, M., Sheffield, J., Teuling, A. J., Wang, K., Wood, E. F., and Zhang, Y.: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations, Geophys. Res. Lett., https://doi.org/10.1029/2010GL046230, 38, L06402, 2011.

Norman, J. M.: Modeling the complete crop canopy, in: Modification of the aerial environment of plants, edited by: Barfield, B. J. and Gerber, J. F., ASAE Monogr. Am. Soc. Agric. Eng., St. Joseph, MI., 249–277, 1979.

Obukhov, A. M.: Turbulence in an atmosphere with a non-uniform temperature, Bound.-Lay. Meteorol., 2, 7–29, 1971.

Olioso, A., Chauki, H., Wigneron, J., Bergaoui, K., Bertuzzi, P., Chanzy, A., Bessemoulin, P., and Clavet, J. C.: Estimation of energy fluxes from thermal infrared, spectral reflectances, microwave data and SVAT modeling, Phys. Chem. Earth B, 24, 829–836, 1999.

Pauwels, V. R. N. and Samson, R.: Comparison of different methods to measure and model actual evapotranspiration rates for a wet sloping grassland, Agr. Water Manage., 82, 1–24, https://doi.org/10.1016/j.agwat.2005.06.001, 2006.

Pauwels, V. R. N., Timmermans, W., and Loew, A.: Comparison of the estimated water and energy budgets of a large winter wheat field during AgriSAR 2006 by multiple sensors and models, J. Hydrol., 349, 425–440, https://doi.org/10.1016/j.jhydrol.2007.11.016, 2008.

Petropoulos, G., Carlson, T. N., Wooster, M. J., and Islam, S.: A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture, Progr. Phys. Geogr., 33, 224–250, https://doi.org/10.1177/0309133309338997, 2009.

Rauwerda, J., Roerink, G. J., and Su, Z.: Estimation of evaporative fractions by the use of vegetation and soil component temperatures determined by means of dual-looking remote sensing, Wageningen, Alterra, Green World Research, 149, 2002.

Schmid, H. P.: Experimental design for flux measurements: matching scales of observations and fluxes, Agric. For. Meteorol., 87, 179–200, https://doi.org/10.1016/S0168-1923(97)00011-7, 1997.

Shan, X., van de Velde, R., Wen, J., He, Y., Verhoef, W., and Su, Z.: Regional Evapotranspiration over the arid inland heihe river basin in northwest China, Dragon 1 Programme Final Results, Beijing, 2008.

Sobrino, J. A., Soria, G., and Prata, A. J.: Surface temperature retrieval from Along Track Scanning Radiometer 2 data: Algorithms and validation, J. Geophys. Res.-Atmos., 109, D11101, https://doi.org/10.1029/2003jd004212, 2004.

Song, J., Wang, J., Xiao, Z., and Xiao, Y.: The method on generating LAI production by fusing BJ-1 remote sensing data and modis LAI product, Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009, IV-825-IV-828, 2009.

Su, H., McCABE, M. F., and Wood, E. F.: Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction, J. Hydrometeorol., 6, 910–922, 2005.

Su, Z.: Remote Sensing Applied to Hydrology: The Sauer River Basin Study, Ph.D, Hydrologie/Wasserwirtschaft, Faculty of Civil Engineering, Ruhr Univesity, Bochum, 1996.

Su, Z.: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sci., 6, 85–100, https://doi.org/10.5194/hess-6-85-2002, 2002.

Su, Z., Pelgrum, H., and Menenti, M.: Aggregation effects of surface heterogeneity in land surface processes, Hydrol. Earth Syst. Sci., 3, 549–563, https://doi.org/10.5194/hess-3-549-1999, 1999.

Su, Z., Schmugge, T., Kustas, W. P., and Massman, W. J.: An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and the atmosphere, J. Appl. Meteorol., 40, 1933–1951, 2001.

Timmermans, J., Verhoef, W., van der Tol, C., and Su, Z.: Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements, Hydrol. Earth Syst. Sci., 13, 1249–1260, https://doi.org/10.5194/hess-13-1249-2009, 2009.

Timmermans, W. J., van der Kwast, J., Gieske, A. S. M., Su, Z., Olioso, A., Jia, L., and Elbers, J. A.: Intercomparison of Energy Flux Models using Aster Imagery at the SPARC 2004 site (Barrax, Spain), SPARC final workshop, Enschede, 2005.

Timmermans, W. J., Kustas, W. P., Anderson, M. C., and French, A. N.: An intercomparison of the surface energy balance algorithm for land (SEBAL) and the two-source energy balance (TSEB) modeling schemes, Remote Sens. Environ., 108, 369–384, https://doi.org/10.1016/j.rse.2006.11.028, 2007.

van der Kwast, J., Timmermans, W., Gieske, A., Su, Z., Olioso, A., Jia, L., Elbers, J., Karssenberg, D., and de Jong, S.: 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, https://doi.org/10.5194/hess-13-1337-2009, 2009.

van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., and Su, Z.: An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance, Biogeosciences, 6, 3109–3129, https://doi.org/10.5194/bg-6-3109-2009, 2009.

Verhoef, A., McNaughton, K. G., and Jacobs, A. F. G.: A parameterization of momentum roughness length and displacement height for a wide range of canopy densities, Hydrol. Earth Syst. Sci., 1, 81–91, https://doi.org/10.5194/hess-1-81-1997, 1997.

Verhoef, W.: A Bayesian optimisation approach for model inversion of hyperspectral – multidirectional observations: the balance with A Priori information, 10th international symposium on physical measurements and spectral signatures in remote sensing, Davos, Switserland, 208–213, 2008.

Verhoef, W. and Bach, H.: Coupled soil-leaf-canopy and atmosphere radiative transfier modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data, Remote Sens. Environ., 109, 166–182, https://doi.org/10.1016/j.rse.2006.12.013, 2007.

Verhoef, W., Jia, L., Xiao, Q., and Su, Z.: Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies, IEEE Trans. Geosci. Remote Sens., 45, 1808–1822, https://doi.org/10.1109/tgrs.2007.895844, 2007.

Vinukollu, R. K., Wood, E. F., Ferguson, C. R., and Fisher, J. B.: Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches, Remote Sens. Environ., 115, 801–823, 2011.