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Derivation of Daily Evaporative Fraction Based on Temporal Variations in Surface Temperature, Air Temperature, and?Net?Radiation

DOI: 10.3390/rs5105369

Keywords: evaporative fraction, temporal variation, remote sensing, SVAT model

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Abstract:

Based on surface energy balance and the assumption of fairly invariant evaporative fraction (EF) during daytime, this study proposes a new parameterization scheme of directly estimating daily EF. Daily EF is parameterized as a function of temporal variations in surface temperature, air temperature, and net radiation. The proposed EF parameterization scheme can well reproduce daily EF estimates from a soil-vegetation-atmosphere transfer (SVAT) model with a root mean square error (RMSE) of 0.13 and a coefficient of determination ( R 2) of 0.719. When input variables from in situ measurements at the Yucheng station in North China are used, daily EF estimated by the proposed method is in good agreement with measurements from the eddy covariance system corrected by the residual energy method with an R 2 of 0.857 and an RMSE of 0.119. MODIS/Aqua remotely sensed data were also applied to estimate daily EF. Though there are some inconsistencies between the remotely sensed daily EF estimates and in situ measurements due to errors in input variables and measurements, the result from the proposed parameterization scheme shows a slight improvement to SEBS-estimated EF with remotely sensed instantaneous inputs.

References

[1]  Vinukollu, R.K.V.R.K.; Wood, E.F.; Ferguson, C.R.; 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 2011, 115, 801–823.
[2]  Anderson, M.C.; Allen, R.G.; Morse, A.; Kustas, W.P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ 2012, 122, 50–65.
[3]  Tang, R.L.; Li, Z.-L.; Jia, Y.; Li, C.; Sun, X.; Kustas, W.P.; Anderson, M.C. An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat-corn production region. Remote Sens. Environ 2011, 115, 3187–3202.
[4]  Teixeira, A.H. de C.; Bastiaanssen, W.G.M.; Ahmad, M.D.; Bos, M.G. Determining regional actual evapotranspiration of irrigated crops and natural vegetation in the S?o Francisco River Basin (Brazil) using remote sensing and penman-monteith equation. Remote Sens 2010, 2, 1287–1319.
[5]  Ruhoff, A.L.; Paz, A.R.; Collischonn, W.; Aragao, L.E.O.C.; Rocha, H.R.; Malhi, Y.S. A MODIS-based energy balance to estimate evapotranspiration for clear-sky days in Brazilian Tropical Savannas. Remote Sens 2012, 4, 703–725.
[6]  Johnson, L.F.; Trout, T.J. Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California’s San Joaquin Valley. Remote Sens 2012, 4, 439–455.
[7]  Long, D.; Singh, V.P. A two-source trapezoid model for evapotranspiration (TTME) from satellite imagery. Remote Sens. Environ 2012, 121, 370–388.
[8]  Bastiaanssen, W.; Menenti, M.; Feddes, R.; Holtslag, A. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol 1998, 212, 198–212.
[9]  Su, Z. The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci 2002, 6, 85–99.
[10]  Norman, J.M.; Kustas, W.P.; Humes, K.S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface-temperature. Agric. For. Meteorol 1995, 77, 263–293.
[11]  Li, Z.-L.; Tang, B.H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ 2013, 131, 14–37.
[12]  Wu, H.; Li, Z.-L. Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors 2009, 9, 1768–1793.
[13]  Li, Z.-L.; Wu, H.; Wang, N.; Qiu, S.; Sobrino, J.A.; Wan, Z.; Tang, B.H.; Yan, G. Land surface emissivity retrieval from satellite data. Int. J. Remote Sens 2013, 34, 3084–3127.
[14]  Li, Z.-L.; Tang, R.L.; Wan, Z.; Bi, Y.; Zhou, C.; Tang, B.H.; Yan, G.; Zhang, X. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 2009, 9, 3801–3853.
[15]  Kalma, J.D.; McVicar, T.R.; McCabe, M.F. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys 2008, 29, 421–469.
[16]  Wang, K.C.; Dickinson, R.E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 2012, doi:10.1029/2011RG000373.
[17]  Crago, R.D. Conservation and variability of the evaporative fraction during the daytime. J. Hydrol 1996, 180, 173–194.
[18]  Lhomme, J.P.; Elguero, E. Examination of evaporative fraction diurnal behaviour using a soil-vegetation model coupled with a mixed-layer model. Hydrol. Earth Syst. Sci 1999, 3, 259–270.
[19]  Gentine, P.; Entekhabi, D.; Polcher, J. The diurnal behavior of evaporative fraction in the soil-vegetation-atmospheric boundary layer continuum. J. Hydrometeorol 2011, 12, 1530–1546.
[20]  Lu, J.; Li, Z.-L.; Tang, R.L.; Tang, B.H.; Wu, H.; Yang, F.; Labed, J.; Zhou, G. Evaluating the SEBS-estimated evaporative fraction from MODIS data for a complex underlying surface. Hydrol. Process., 2012, doi:10.1002/hyp.9440.
[21]  Nichols, W.E.; Cuenca, R.H. Evaluation of the evaporative fraction for parameterization of the surface energy balance. Water Resour. Res 1993, 29, 3681–3690.
[22]  Colaizzi, P.; Evett, S.; Howell, T.; Tolk, J. Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Trans. ASAE 2006, 49, 1409–1417.
[23]  Sugita, M.; Brutsaert, W. Daily evaporation over a region from lower boundary layer profiles measured with radiosondes. Water Resour. Res 1991, 27, 747–752.
[24]  Tang, R.L.; Li, Z.-L.; Sun, X. Temporal upscaling of instantaneous evapotranspiration: An intercomparison of fourmethods using eddy covariance measurements and MODIS data. Remote Sens. Environ., 2013, doi:10.1016/j.rse.2013.07.001.
[25]  Cammalleri, C.; Anderson, M.; Kustas, W. Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrol. Earth Syst. Sci 2013, 10, 7325–7350.
[26]  Jiang, L.; Islam, S. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophys. Res. Lett 1999, 26, 2773–2776.
[27]  Roerink, G.; Su, Z.; Menenti, M. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth Part. B Hydrol. Oceans Atmos 2000, 25, 147–157.
[28]  Tang, R.L; Li, Z.-L.; Tang, B.H. An application of the Ts-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation. Remote Sens. Environ 2010, 114, 540–551.
[29]  Anderson, M.C.; Norman, J.M.; Diak, G.R.; Kustas, W.P.; Mecikalski, J.R. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ 1997, 60, 195–216.
[30]  Norman, J.M.; Kustas, W.P.; Prueger, J.H.; Diak, G.R. Surface flux estimation using radiometric temperature: A dual temperature-difference method to minimize measurement errors. Water Resour. Res 2000, 36, 2263–2274.
[31]  Wang, K.C.; Li, Z.Q.; Cribb, M. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sens. Environ 2006, 102, 293–305.
[32]  Stisen, S.; Sandholt, I.; Norgaard, A.; Fensholt, R.; Jensen, K.H. Combining the triangle method with thermal inertia to estimate regional evapotranspiration—Applied to MSG-SEVIRI data in the Senegal River basin. Remote Sens. Environ 2008, 112, 1242–1255.
[33]  Shu, Y.Q.; Stisen, S.; Jensen, K.H.; Sandholt, I. Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data. Int. J. Appl. Earth Obs 2011, 13, 192–206.
[34]  Long, D.; Singh, V.P.; Scanlon, B.R. Deriving theoretical boundaries to address scale dependencies of triangle models for evapotranspiration estimation. J. Geophys. Res 2012, 117, D05113.
[35]  Long, D.; Singh, V.P. Assessing the impact of end-member selection on the accuracy of satellite-based spatial variability models for actual evapotranspiration estimation. Water Resour. Res 2013, 49, 2601–2618.
[36]  Anderson, M.C.; Norman, J.M.; Meyers, T.P.; Diak, G.R. An analytical model for estimating canopy transpiration and carbon assimilation fluxes based on canopy light-use efficiency. Agric. For. Meteorol 2000, 101, 265–289.
[37]  Brutsaert, W. Evaporation into the Atmosphere: Theory, History, and Applications; D. Reidel: Dordrecht, The Netherlands, 1982.
[38]  Kalma, J.; Jupp, D. Estimating evaporation from pasture using infrared thermometry: Evaluation of a one-layer resistance model. Agric. For. Meteorol 1990, 51, 223–246.
[39]  Chehbouni, A.; Lo Seen, D.; Njoku, E.; Monteny, B. Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sens. Environ 1996, 58, 177–186.
[40]  Lhomme, J.; Chehbouni, A.; Monteny, B. Sensible heat flux-radiometric surface temperature relationship over sparse vegetation: Parameterizing B-1. Bound.-Lay. Meteorol 2000, 97, 431–457.
[41]  Sun, J.; Mahrt, L. Determination of surface fluxes from the surface radiative temperature. J. Atmos. Sci 1995, 52, 1096–1106.
[42]  Carlson, T.N.; Buffum, M.J. On estimating total daily evapotranspiration from remote surface temperature measurements. Remote Sens. Environ 1989, 29, 197–207.
[43]  Lagouarde, J.-P.; McAneney, K. Daily sensible heat flux estimation from a single measurement of surface temperature and maximum air temperature. Bound.-Lay. Meteorol 1992, 59, 341–362.
[44]  Carlson, T.N.; Capehart, W.J.; Gillies, R.R. A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sens. Environ 1995, 54, 161–167.
[45]  Brutsaert, W.; Sugita, M. Application of self-preservation in the diurnal evolution of the surface energy budget to determine daily evaporation. J. Geophys. Res 1992, 97, 18377–18382.
[46]  Daughtry, C.; Kustas, W.; Moran, M.; Pinter, P.; Jackson, R.; Brown, P.; Nichols, W.; Gay, L. Spectral estimates of net radiation and soil heat flux. Remote Sens. Environ 1990, 32, 111–124.
[47]  Choudhury, B.J.; Idso, S.B.; Reginato, R.J. Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation. Agric. For. Meteorol 1987, 39, 283–297.
[48]  Van de Griend, A.A.; Camillo, P.J.; Gurney, R.J. Discrimination of soil physical parameters, thermal inertia, and soil moisture from diurnal surface temperature fluctuations. Water Resour. Res 1985, 21, 997–1009.
[49]  Tang, B.H.; Li, Z.-L.; Zhang, R. A direct method for estimating net surface shortwave radiation from MODIS data. Remote Sens. Environ 2006, 103, 115–126.
[50]  Tang, B.H.; Li, Z.-L. Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data. Remote Sens. Environ 2008, 112, 3482–3492.
[51]  Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ 1997, 62, 241–252.
[52]  Prihodko, L.; Goward, S.N. Estimation of air temperature from remotely sensed surface observations. Remote Sens. Environ 1997, 60, 335–346.
[53]  Twine, T.; Kustas, W.; Norman, J.; Cook, D.; Houser, P.; Meyers, T.; Prueger, J.; Starks, P.; Wesely, M. Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol 2000, 103, 279–300.
[54]  McCabe, M.F.; Wood, E.F. Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sens. Environ 2006, 105, 271–285.
[55]  Wan, Z.; Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE T. Geosci. Remot 1996, 34, 892–905.
[56]  Campbell, G.S.; Norman, J.M. Introduction to Environmental Biophysics; Springer Verlag: New York, NY, USA, 1998.

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