• Baret, F., , Clevers J. G. P. W. , , and Steven M. D. , 1995: The robustness of canopy gap fraction estimates from red and near-infrared reflectances—A comparison of approaches. Remote Sens. Environ., 54 , 141151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brotzge, J. A., , and Crawford K. C. , 2003: Examination of the surface energy budget: A comparison of eddy correlation and Bowen ratio measurement systems. J. Hydrometeor., 4 , 160178.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., 1999: Aspects of bulk atmospheric boundary layer similarity under free-convective conditions. Rev. Geophys., 37 , 439451.

  • Bugbee, B., , Droter M. , , Monje O. , , and Tanner B. , 1999: Evaluation and modification of commercial infrared-red transducers for leaf temperature measurement. Adv. Space Res., 22 , 14251434.

    • Search Google Scholar
    • Export Citation
  • Chen, J. M., , and Cihlar J. , 1995: Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size methods. IEEE Trans. Geosci. Remote Sens., 33 , 777787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, L. F., , Li Z-L. , , Liu Q. H. , , Chen S. , , Tang Y. , , and Zhong B. , 2004: Definition of component effective emissivity for heterogeneous and non-isothermal surfaces and its approximate calculation. Int. J. Remote Sens., 25 , 231244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Conte, S. D., , Dunsmore H. E. , , and Shen V. Y. , 1986: Software Engineering Metrics and Models. Benjamin-Cummings, 396 pp.

  • Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108 .8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation
  • Diak, G. R., , Bland W. L. , , and Mecikalski J. , 1996: A note on first estimates of surface insolation from GOES-8 visible satellite data. Agric. For. Meteor., 82 , 219226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Famiglietti, J. S., , and Wood E. F. , 1994: Application of multiscale water and energy balance models on a tallgrass prairie. Water Resour. Res., 30 , 30793093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fritschen, L. J., , Qian P. , , Kanemasu E. T. , , Nie D. , , Smith E. A. , , Stewart J. B. , , Verma S. B. , , and Wesely M. L. , 1992: Comparisons of surface flux measurement systems used in FIFE 1989. J. Geophys. Res., 97 , 1869718713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, T., , and Cosh M. , 2003: SMEX02 tower-based radiometric surface temperature, Walnut Creek, Iowa. National Snow and Ice Data Center, Boulder, Colorado, Digital Media. [Available online at http://nsidc.org/data/nsidc-0186.html.].

  • Jacobs, J. M., , Myers D. A. , , Anderson M. C. , , and Diak G. R. , 2002: GOES surface insolation to estimate wetlands evapotranspiration. J. Hydrol., 266 , 5365.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, L., and Coauthors, 2003: Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements. Phys. Chem. Earth, 28 , 7588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanemasu, E. T., and Coauthors, 1992: Surface flux measurements in FIFE: An overview. J. Geophys. Res., 97 , 1854718555.

  • Kustas, W. P., , and Daughtry C. S. T. , 1990: Estimation of the soil heat-flux net-radiation ratio from spectral data. Agric. For. Meteor., 49 , 205223.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , and Norman J. M. , 2000: Evaluating the effects of subpixel heterogeneity on pixel average fluxes. Remote Sens. Environ., 74 , 327342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., , Hatfield J. L. , , and Prueger J. H. , 2005: The Soil Moisture–Atmosphere Coupling Experiment (SMACEX): Background, hydrometeorlogical conditions, and preliminary findings. J. Hydrometeor., 6 , 825839.

    • Search Google Scholar
    • Export Citation
  • Laubach, J., , and Teichmann U. , 1999: Surface energy budget variability: A case study over grass with special regard to minor inhomogeneities in the source area. Theor. Appl. Climatol., 62 , 924.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, X. H., , and Black T. A. , 1994: Relating eddy-correlation sensible heat flux to horizontal sensor separation in the unstable atmospheric surface layer. J. Geophys. Res., 99 , 1854518553.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, F., , Jackson T. J. , , Kustas W. P. , , Schmugge T. J. , , French A. , , Cosh M. H. , , and Bindlish R. , 2004: Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX. Remote Sens. Environ., 42 , 380390.

    • Search Google Scholar
    • Export Citation
  • Lloyd, C. R., and Coauthors, 1997: A comparison of surface fluxes at the HAPEX-Sahel fallow bush sites. J. Hydrol., 189 , 400425.

  • Luo, L. F., and Coauthors, 2003: Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains. J. Geophys. Res., 108 .8843, doi:10.1029/2002JD003246.

    • Search Google Scholar
    • Export Citation
  • Massman, W. J., , and Lee X. , 2002: Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agric. For. Meteor., 113 , 121144.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, D., , Washburne J. , , and Wood E. , 1995: EOS workshop on land surface evaporation and transpiration. The Earth Observer, No. 7, 52–56. [Available online at http://eospso.gsfc.nasa.gov/eos_observ/7_8_95/p52.html.].

  • Monteith, J. L., 1973: Principles of Environmental Physics. Edward Arnold Press, 241 pp.

  • Nie, D., and Coauthors, 1992: An intercomparison of surface-energy flux measurement systems used during FIFE 1987. J. Geophys. Res., 97 , 1871518724.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilson, T., 1971: A theoretical analysis of the frequency of gaps in plant stands. Agric. For. Meteor., 8 , 2538.

  • Prueger, J. H., and Coauthors, 2005: Tower and aircraft eddy covariance measurements of water vapor, energy, and carbon dioxide fluxes during SMACEX. J. Hydrometeor., 6 , 954960.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ., 83 , 135148.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobrino, J. A., , Raissouni N. , , and Li Z. L. , 2001: A comparative study of land surface emissivity retrieval from NOAA data. Remote Sens. Environ., 75 , 256266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, Z., 2002: The surface energy balance system (SEBS) for estimation of the turbulent heat fluxes. Hydrol. Earth Sci., 6 , 8599.

  • Su, Z., , Schmugge T. , , Kustas W. P. , , and Massman W. J. , 2001: An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and the atmosphere. J. Appl. Meteor., 40 , 19331951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Twine, T. E., and Coauthors, 2000: Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteor., 103 , 279300.

  • USDA ARS Hydrology and Remote Sensing Lab, 2002: Soil Moisture Experiments in 2002 (SMEX02): Experiment plan. 192 pp. [Available online at http://hydrolab.arsusda.gov/smex02/smex60302.pdf.].

  • Wood, E. F., , Su H-B. , , McCabe M. , , and Su B. , 2003: Estimating evapotranspiration from satellite remote sensing. IEEE Proc. of IGARSS 03, Vol. 2, Toulouse, France, IEEE, 1163–1165.

    • Search Google Scholar
    • Export Citation
  • Xavier, A. C., , and Vettorazzi C. A. , 2004: Mapping leaf area index through spectral vegetation indices in a subtropical watershed. Int. J. Remote Sens., 25 , 16611672.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction

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  • 1 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
  • | 2 International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, Netherlands
  • | 3 National Soil Tilth Research Laboratory, Ames, Iowa
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Abstract

The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

Corresponding author address: Hongbo Su, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: hongbosu@princeton.edu

Abstract

The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

Corresponding author address: Hongbo Su, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: hongbosu@princeton.edu

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