Skip to main content

Utility of an Automated Thermal-Based Approach for Monitoring Evapotranspiration

Abstract

A very simple remote sensing-based model for water use monitoring is presented. The model acronym DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature) is a Dutch word which loosely translates as “it’s unbelievable that it works”. DATTUTDUT is fully automated and only requires a surface temperature map, making it simple to use and providing a rapid estimate of spatially-distributed fluxes. The algorithm is first tested over a range of environmental and land-cover conditions using data from four short-term field experiments and then evaluated over a growing season in an agricultural region. Flux model output is in satisfactory agreement with observations and established remote sensing-based models, except under dry and partial canopy cover conditions. This suggests that DATTUTDUT has utility in identifying relative water use and as an operational tool providing initial estimates of ET anomalies in data-poor regions that would be confirmed using more robust modeling techniques.

References

  • Anderson, M.C., J.M. Norman, G.R. Diak, W.P. Kustas, and J.R. Mecikalski (1997), A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing, Remote Sens. Environ. 60, 2, 195–216, DOI: 10.1016/S0034-4257(96)00215-5.

    Article  Google Scholar 

  • Anderson, M.C., J.M. Norman, W.P. Kustas, F. Li, J.H. Prueger, and J.R. Mecikalski (2005), Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape during SMACEX, J. Hydrometeor. 6, 6, 892–909, DOI: 10.1175/JHM465.1.

    Article  Google Scholar 

  • Anderson, M.C., W.P. Kustas, and J.M. Norman (2007), Upscaling flux observations from local to continental scales using thermal remote sensing, Agron. J. 99, 1, 240–254, DOI: 10.2134/agronj2005.0096S.

    Article  Google Scholar 

  • Anderson, M.C., W.P. Kustas, J.M. Norman, C.R. Hain, J.R. Mecikalski, L. Schultz, M.P. González-Dugo, C. Cammalleri, G. d’Urso, A. Pimstein, and F. Gao (2011), Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci. 15, 223–239, DOI: 10.5194/hess-15-223-2011.

    Article  Google Scholar 

  • Andreu, A., W.J. Timmermans, D. Skokovic, and M.P. Gonzalez-Dugo (2015), Influence of component temperature derivation from dual angle thermal infrared observations on TSEB flux estimates over an irrigated vineyard, Acta Geophys. 63, 6, 1540–1570, DOI: 10.1515/acgeo-2015-0037 (this issue).

    Article  Google Scholar 

  • Bastiaanssen, W.G.M. (1995), Regionalization of surface flux densities and moisture indicators in composite terrain: a remote sensing approach under clear skies in Mediterranean climates, Ph.D. Thesis, Wageningen Agricultural University, Wageningen, The Netherlands, 273 pp.

    Google Scholar 

  • Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes, and A.A.M. Holtslag (1998), A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, J. Hydrol. 212-213, 198–212, DOI: 10.1016/S0022-1694 (98)00253-4.

    Article  Google Scholar 

  • Bateni, S.M., D. Entekhabi, S. Margulis, F. Castelli, and L. Kergoat (2014), Coupled estimation of surface heat fluxes and vegetation dynamics from remotely sensed land surface temperature and fraction of photosynthetically active radiation, Water Resour. Res. 50, 11, 8420–8440, DOI: 10.1002/2013WR014573.

    Article  Google Scholar 

  • Brutsaert, W. (1982), Evaporation into the Atmosphere. Theory, History, and Applications, Reidel, Dordrecht, 299 pp.

    Book  Google Scholar 

  • Brutsaert, W., and D. Chen (1996), Diurnal variation of surface fluxes during thorough drying (or severe drought) of natural prairie, Water Resour. Res. 32, 7, 2013–2019, DOI: 10.1029/96WR00995.

    Article  Google Scholar 

  • Burridge, D.M., and A.J. Gadd (1974), The Meteorological Office operational 10-level numerical weather prediction model (December 1974), Tech. Notes 12 and 48, British Meteorological Office, Bracknell, England, 57 pp.

    Google Scholar 

  • Cammalleri, C., M.C. Anderson, and W.P. Kustas (2014), Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications, Hydrol. Earth Syst. Sci. 18, 1885–1894, DOI: 10.5194/hess-18-1885-2014.

    Article  Google Scholar 

  • Campbell, G.S., and J.M. Norman (1998), An Introduction to Environmental Biophysics, 2nd ed., Springer, New York, 286 pp., DOI: 10.1007/978-1-4612-1626-1.

    Book  Google Scholar 

  • Carlson, T.N., and D.A. Ripley (1997), On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sens. Environ. 62, 3, 241–252, DOI: 10.1016/S0034-4257(97)00104-1.

    Article  Google Scholar 

  • Chehbouni, A., J.C.B. Hoedjes, J.-C. Rodriguez, C.J. Watts, J. Garatuza, F. Jacob, and Y.H. Kerr (2008), Using remotely sensed data to estimate areaaveraged daily surface fluxes over a semi-arid mixed agricultural land, Agr. Forest Meteorol. 148, 3, 330–342, DOI: 10.1016/j.agrformet.2007.09.014.

    Article  Google Scholar 

  • Choi, M., W.P. Kustas, M.C. Anderson, R.G. Allen, F. Li, and J.H. Kjaersgaard (2009), An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX, Agr. Forest Meteorol. 149, 12, 2082–2097, DOI: 10.1016/j.agrformet.2009.07.002.

    Article  Google Scholar 

  • Choudhury, B.J. (1987), Relationships between vegetation indices, radiation absorption, and net photosynthesis evaluated by a sensitivity analysis, Remote Sens. Environ. 22, 2, 209–233, DOI: 10.1016/0034-4257(87)90059-9.

    Article  Google Scholar 

  • Choudhury, B.J., N.U. Ahmed, S.B. Idso, R.J. Reginato, and C.S.T. Daughtry (1994), Relations between evaporation coefficients and vegetation indices studied by model simulations, Remote. Sens. Environ. 50, 1, 1–17, DOI: 10.1016/0034-4257(94)90090-6.

    Article  Google Scholar 

  • Crago, R.D. (1996), Conservation and variability of the evaporative fraction during the daytime, J. Hydrol. 180, 1–4, 173–194, DOI: 10.1016/0022-1694(95)02903-6.

    Article  Google Scholar 

  • de Bruin, H.A.R. (1987), From Penman to Makkink. In: J.C. Hooghart (ed.), “Evaporation and Weather” Proceedings and Information, 25 March 1987, Hague, Netherlands, TNO Committee on Hydrological Research, Vol. 39, 5–31.

    Google Scholar 

  • de Bruin, H.A.R. (1994), Analytic solutions of the equations governing the temperature fluctuation method, Bound.-Lay. Meteorol. 68, 4, 427–432, DOI: 10.1007/BF00706800.

    Article  Google Scholar 

  • de Miguel, E., M. Jiménez, I. Pérez, Ó.G. de la Cámara, F. Muñoz, and J.A. Gómez-Sánchez (2015), AHS and CASI processing for the REFLEX remote sensing campaign: methods and results, Acta Geophys. 63, 6, 1485–1498, DOI: 10.1515/acgeo-2015-0031 (this issue).

    Article  Google Scholar 

  • Delogu, E., G. Boulet, A. Olioso, B. Coudert, J. Chirouze, E. Ceschia, V. le Dantec, O. Marloie, G. Chehbouni, and J.-P. Lagouarde (2012), Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass, Hydrol. Earth Syst Sci. 16, 2995–3010, DOI: 10.5194/hess-16-2995-2012.

    Article  Google Scholar 

  • Droogers, P., and W. Bastiaanssen (2002), Irrigation performance using hydrological and remote sensing modeling, J. Irrig. Drain. Eng. ASCE 128, 1, 11–18, DOI: 10.1061/(ASCE)0733-9437(2002)128:1(11).

    Article  Google Scholar 

  • Duffie, J.A., and W.A. Beckman (1991), Solar Engineering of Thermal Processes, 2nd ed., John Wiley & Sons, New York, 944 pp.

    Google Scholar 

  • Foken, T., and M.Y. Leclerc (2004), Methods and limitations in validation of footprint models, Agr. Forest Meteorol. 127, 3-4, 223–234, DOI: 10.1016/j.agrformet.2004.07.015.

    Article  Google Scholar 

  • French, A.N., T.J. Schmugge, W.P. Kustas, K.L. Brubaker, and J. Prueger (2003), Surface energy fluxes over El Reno, Oklahoma, using high-resolution remotely sensed data, Water Resour. Res. 39, 6, 1164, DOI: 10.1029/2002WR001734.

    Article  Google Scholar 

  • French, A.N., F. Jacob, M.C. Anderson, W.P. Kustas, W. Timmermans, A. Gieske, Z. Su, H. Su, M.F. McCabe, F. Li, J. Prueger, and N. Brunsell (2005a), Corrigendum to “Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA)” [Remote Sensing of Environment 2005 99/1–2; 55–65], Remote Sens. Environ. 99, 4, 471, DOI: 10.1016/j.rse.2005.10.001.

    Article  Google Scholar 

  • French, A.N., F. Jacob, M.C. Anderson, W.P. Kustas, W. Timmermans, A. Gieske, Z. Su, H. Su, M.F. McCabe, F. Li, J. Prueger, and N. Brunsell (2005b), Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USa), Remote Sens. Environ. 99, 1-2, 55–65, DOI: 10.1016/j.rse.2005. 05.015.

    Article  Google Scholar 

  • Garratt, J.R. (1992), The Atmospheric Boundary Layer, Cambridge University Press, Cambridge.

    Google Scholar 

  • Gentine, P., D. Entekhabi, A. Chehbouni, G. Boulet, and B. Duchemin (2007), Analysis of evaporative fraction diurnal behaviour, Agr. Forest Meteorol. 143, 1–2, 13–29, DOI: 10.1016/j.agrformet.2006.11.002.

    Article  Google Scholar 

  • Gieske, A., and W. Meijninger (2005), High density NOAA time series of ET in the Gediz Basin, Turkey, Irrig. Drain. Syst. 19, 3–4, 285–299, DOI: 10.1007/s10795-005-5191-3.

    Article  Google Scholar 

  • Goetz, S.J., S.D. Prince, S.N. Goward, M.M. Thawley, and J. Small (1999), Satellite remote sensing of primary production: an improved production efficiency modeling approach, Ecol. Model. 122, 3, 239–255, DOI: 10.1016/S0304-3800(99)00140-4.

    Article  Google Scholar 

  • Hanna, S.R., and J.C. Chang (1992), Boundary-layer parameterizations for applied dispersion modeling over urban areas, Bound.-Lay. Meteorol. 58, 3, 229–259, DOI: 10.1007/BF02033826.

    Article  Google Scholar 

  • Hoedjes, J.C.B., A. Chehbouni, J. Ezzahar, R. Escadafal, and H.A.R. de Bruin (2007), Comparison of large aperture scintillometer and eddy covariance measurements: Can thermal infrared data be used to capture footprintinduced differences? J. Hydrometeorol. 8, 2, 144–159, DOI: 10.1175/JHM561.1.

    Article  Google Scholar 

  • Humes, K.S., W.P. Kustas, and M.S. Moran (1994), Use of remote sensing and reference site measurements to estimate instantaneous surface energy balance components over a semiarid rangeland watershed, Water Resour. Res. 30, 5, 1363–1373, DOI: 10.1029/93WR03082.

    Article  Google Scholar 

  • Jackson, R.D., S.B. Idso, R.J. Reginato, and P.J. Pinter Jr. (1981), Canopy temperature as a crop water stress indicator, Water Resour. Res. 17, 4, 1133–1138, DOI: 10.1029/WR017i004p01133.

    Article  Google Scholar 

  • Jackson, T.J., D.M. le Vine, A.Y. Hsu, A. Oldak, P.J. Starks, C.T. Swift, J.D. Isham, and M. Haken (1999), Soil moisture mapping at regional scales using microwave radiometry: the Southern Great Plains hydrology experiment, IEEE Trans. Geosci. Remote. Sens. 37, 5, 2136–2151, DOI: 10.1109/36.789610.

    Article  Google Scholar 

  • Jacob, F., A. Olioso, X.F. Gu, Z. Su, and B. Seguin (2002), Mapping surface fluxes using airborne visible, near infrared, thermal infrared remote sensing data and a spatialized surface energy balance model, Agronomie 22, 6, 669–680, DOI: 10.1051/agro:2002053.

    Article  Google Scholar 

  • Jiang, L., and S. Islam (2001), Estimation of surface evaporation map over Southern Great Plains using remote sensing data, Water Resour. Res. 37, 2, 329–340, DOI: 10.1029/2000WR900255.

    Article  Google Scholar 

  • Kalma, J.D., T.R. McVicar, and M.F. McCabe (2008), Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surv. Geophys. 29, 4–5, 421–469, DOI: 10.1007/s10712-008-9037-z.

    Article  Google Scholar 

  • Kite, G.W., and P. Droogers (2000), Comparing evapotranspiration estimates from satellites, hydrological models and field data, J. Hydrol. 229, 1-2, 3–18, DOI: 10.1016/S0022-1694(99)00195-X.

    Article  Google Scholar 

  • Kustas, W.P., and J.M. Norman (1997), A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations, Water Resour. Res. 33, 6, 1495–1508, DOI: 10.1029/97WR00704.

    Article  Google Scholar 

  • Kustas, W.P., and J.M. Norman (1999), Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover, Agr. Forest Meteorol. 94, 1, 13–29, DOI: 10.1016/S0168-1923(99)00005-2.

    Article  Google Scholar 

  • Kustas, W.P., and J.M. Norman (2000), Evaluating the effects of subpixel heterogeneity on pixel average fluxes, Remote Sens. Environ. 74, 3, 327–342, DOI: 10.1016/S0034-4257(99)00081-4.

    Article  Google Scholar 

  • Kustas, W.P., M.S. Moran, K.S. Humes, D.I. Stannard, P.J. Pinter Jr., L.E. Hipps, E. Swiatek, and D.C. Goodrich (1994a), Surface energy balance estimates at local and regional scales using optical remote sensing from an aircraft platform and atmospheric data collected over semiarid rangelands, Water Resour. Res. 30, 5, 1241–1259, DOI: 10.1029/93WR03038.

    Article  Google Scholar 

  • Kustas, W.P., E.M. Perry, P.C. Doraiswamy, and M.S. Moran (1994b), Using satellite remote sensing (to extrapolate evapotranspiration estimates in time and space over a semiarid Rangeland basin), Remote Sens. Environ. 49, 3, 275–286, DOI: 10.1016/0034-4257(94)90022-1.

    Article  Google Scholar 

  • Kustas, W.P., X. Zhan, and T.J. Schmugge (1998), Combining optical and microwave remote sensing for mapping energy fluxes in a semiarid watershed, Remote Sens. Environ. 64, 2, 116–131, DOI: 10.1016/S0034-4257(97)00176-4.

    Article  Google Scholar 

  • Kustas, W.P., J.L. Hatfield, and J.H. Prueger (2005), The Soil-Moisture-Atmosphere Couopling Experiment (SMACEX): background, hydrometeorological conditions, and preliminary findings, J. Hydrometeorol. 6, 6, 791–804, DOI: 10.1175/JHM456.1.

    Article  Google Scholar 

  • Mecikalski, J.R., G.R. Diak, M.C. Anderson, and J.M. Norman (1999), Estimating fluxes on continental scales using remotely sensed data in an atmosphericland exchange model, J. Appl. Meteorol. 38, 9, 1352–1369, DOI: 10.1175/1520-0450(1999)038<1352:EFOCSU>2.0.CO;2.

    Article  Google Scholar 

  • Meijninger, W.M.L. (2003), Surface fluxes over natural landscapes using scintillometry, Ph.D. Thesis, Wageningen University, Wageningen, The Netherlands.

    Google Scholar 

  • Meijninger, W.M.L., and H.A.R. de Bruin (2000), The sensible heat fluxes over irrigated areas in western Turkey determined with a large aperture scintillometer, J. Hydrol. 229, 1-2, 42–49, DOI: 10.1016/S0022-1694(99) 00197-3.

    Article  Google Scholar 

  • Menenti, M., and B.J. Choudhury (1993), Parameterization of land surface evaporation by means of location dependant potential evaporation and surface temperature range. In: H.J. Bolle, R.A. Feddes, and J.D. Kalma (eds.), Proc. Int. Symp. “Exchange Processes at the Land surface for a range of space and time scales”, 13–16 July 1993, Yokohama, Japan.

  • Menenti, M., W.G.M. Bastiaanssen, and D. van Eick (1989), Determination of surface hemispherical reflectance with Thematic Mapper data, Remote Sens. Environ. 28, 327–337, DOI: 10.1016/0034-4257(89)90124-7.

    Article  Google Scholar 

  • Monteith, J.L., and M.H. Unsworth (1990), Principles of Environmental Physics, Edward Arnold Publishers, London, 291 pp.

    Google Scholar 

  • Nichols, W.E., and R.H. Cuenca (1993), Evaluation of the evaporative fraction for parameterization of the surface energy balance, Water Resour. Res. 29, 11, 3681–3690, DOI: 10.1029/93WR01958.

    Article  Google Scholar 

  • Norman, J.M., W.P. Kustas, and K.S. Humes (1995), Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agr. Forest Meteorol. 77, 3–4, 263–293, DOI: 10.1016/0168-1923(95)02265-Y.

    Article  Google Scholar 

  • Norman, J.M., W.P. Kustas, J.H. Prueger, and G.R. Diak (2000), Surface flux estimation using radiometric temperature: A dual-temperature-difference method to minimize measurement errors, Water Resour. Res. 36, 8, 2263–2274, DOI: 10.1029/2000WR900033.

    Article  Google Scholar 

  • Norman, J.M., M.C. Anderson, and W.P. Kustas (2006), Are single-source, remotesensing surface-flux models too simple?, AIP Conf. Proc. 852, 170, DOI: 10.1063/1.2349341.

    Google Scholar 

  • Oncley, S.P., T. Foken, R. Vogt, C. Bernhofer, W. Kohsiek, H. Liu, A. Pitacco, D. Grantz, L. Ribeiro, and T. Weidinger (2002), The energy balance experiment EBEX-2000. In: Proc. 15th Conf. on Boundary Layers and Turbulence, American Meteorological Society (AMS), 15–19 July 2002, Wageningen University, Wageningen, The Netherlands.

    Google Scholar 

  • Parlange, M.B., W.E. Eichinger, and J.D. Albertson (1995), Regional scale evaporation and the atmospheric boundary layer, Rev. Geophys. 33, 1, 99–124, DOI: 10.1029/94RG03112.

    Article  Google Scholar 

  • Pelgrum, H., and W.G.M. Bastiaanssen (1996), An intercomparison of techniques to determine the area-averaged latent heat flux from individual in situ observations: A remote sensing approach using the European Field Experiment in a Desertification-Threatened Area data, Water Resour. Res. 32, 9, 2775–2786, DOI: 10.1029/96WR01396.

    Article  Google Scholar 

  • Prihodko, L., and S.N. Goward (1997), Estimation of air temperature from remotely sensed surface observations, Remote Sens. Environ. 60, 3, 335–346, DOI: 10.1016/S0034-4257(96)00216-7.

    Article  Google Scholar 

  • Prince, S.D., S.J. Goetz, R.O. Dubayah, K.P. Czajkowski, and M. Thawley (1998), Inference of surface and air temperature, atmospheric precipitable water and vapor pressure deficit using Advanced Very High-Resolution Radiometer satellite observations: comparison with field observations, J. Hydrol. 212-213, 230–249, DOI: 10.1016/S0022-1694(98)00210-8.

    Article  Google Scholar 

  • Prueger, J.H., J.L. Hatfield, T.B. Parkin, W.P. Kustas, L.E. Hipps, C.M.U. Neale, J.I. MacPherson, W.E. Eichinger, and D.I. Cooper (2005), Tower and aircraft eddy covariance measurements of water vapor, energy, and carbon dioxide fluxes during SMACEX, J. Hydrometeorol. 6, 6, 954–960, DOI: 10.1175/JHM457.1.

    Article  Google Scholar 

  • Roerink, G.J., Z. Su, and M. Menenti (2000), S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance, Phys. Chem. Earth B 25, 2, 147–157, DOI: 10.1016/S1464-1909(99)00128-8.

    Article  Google Scholar 

  • Santanello, J.A., and M.A. Friedl (2003), Diurnal covariation in soil heat flux and net radiation, J. Appl. Meteorol. 42, 6, 851–862, DOI: 10.1175/1520-0450(2003)042<0851:DCISHFs 2.0.CO;2.

    Article  Google Scholar 

  • Schmid, H.P. (1994), Source areas for scalars and scalar fluxes, Bound.-Lay. Meteorol. 67, 3, 293–318, DOI: 10.1007/BF00713146.

    Article  Google Scholar 

  • Senay, G.B., S. Bohms, R.K. Singh, P.H. Gowda, N.M. Velpuri, H. Alemu, and J.P. Verdin (2013), Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach, J. Am. Water Resour. Assoc. 49, 3, 577–591, DOI: 10.1111/jawr. 12057.

    Article  Google Scholar 

  • Shuttleworth, W.J., R.J. Gurney, A.Y. Hsu, and J.P. Ormsby (1989), FIFE: The variation in energy partition at surface flux sites, IAHS Publ. 186, 67–74.

    Google Scholar 

  • Su, Z. (2002), The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sci. 6, 1, 85–100, DOI: 10.5194/hess-6-85-2002.

    Article  Google Scholar 

  • Tasumi, M., R.G. Allen, and W.G.M. Bastiaanssen (2000), The theoretical basis of SEBAL. In: A. Morse, T. Tasumi, G.A. Richard, and J.K. William (eds.), Application of the SEBAL Methodology for Estimating Consumptive Use of Water and Stream flow Depletion in the Bear River Basin of Idaho through Remote Sensing, Final report, Department of Biological and Agriculture Engineering, University of Idaho, Moscow, USA, 46–69.

    Google Scholar 

  • Timmermans, W.J., W.P. Kustas, M.C. Anderson, and A.N. French (2007), An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes, Remote Sens. Environ. 108, 4, 369–384, DOI: 10.1016/j.rse.2006.11.028.

    Article  Google Scholar 

  • Timmermans, W.J., G. Bertoldi, J.D. Albertson, A. Olioso, Z. Su, and A.S.M. Gieske (2008), Accounting for atmospheric boundary layer variability on flux estimation from RS observations, Int. J. Remote Sens. 29, 17–18, 5275–5290, DOI: 10.1080/01431160802036383.

    Article  Google Scholar 

  • Timmermans, W.J., Z. Su, and A. Olioso (2009), Footprint issues in scintillometry over heterogeneous landscapes, Hydrol. Earth Syst. Sci. 13, 2179–2190, DOI: 10.5194/hess-13-2179-2009.

    Article  Google Scholar 

  • Timmermans, W.J., C. van der Tol, J. Timmermans, M. Ucer, X. Chen, L. Alonso, J. Moreno, A. Carrara, R. Lopez, F. de la Cruz Tercero, H.L. Corcoles, E. de Miguel, J.A.G. Sanchez, I. Pérez, B. Franch, J.-C.J. Munoz, D. Skokovic, J. Sobrino, G. Soria, A. MacArthur, L. Vescovo, I. Reusen, A. Andreu, A. Burkart, C. Cilia, S. Contreras, C. Corbari, J.F. Calleja, R. Guzinski, C. Hellmann, I. Herrmann, G. Kerr, A.-L. Lazar, B. Leutner, G. Mendiguren, S. Nasilowska, H. Nieto, J. Pachego-Labrador, S. Pulanekar, R. Raj, A. Schikling, B. Siegmann, S. von Bueren, and Z.B. Su (2015), An overview of the Regional Experiments For Land-atmosphere Exchanges 2012 (REFLEX 2012) campaign, Acta Geophys. 63, 6, 1465–1484, DOI: 10.2478/s11600-014-0254-1 (this issue).

    Google Scholar 

  • Twine, T.E., W.P. Kustas, J.M. Norman, D.R. Cook, P.R. Houser, T.P. Meyers, J.H. Prueger, P.J. Starks, and M.L. Wesely (2000), Correcting eddycovariance flux underestimates over a grassland, Agr. Forest Meteorol. 103, 3, 279–300, DOI: 10.1016/S0168-1923(00)00123-4.

    Article  Google Scholar 

  • van der Tol, C., W.J. Timmermans, C. Corbari, A. Carrara, J. Timmermans, and Z. Su (2014), An analysis of turbulent heat fluxes and the energy balance during the REFLEX campaign, Acta Geophys. 63, 6, 1516–1539, DOI: 10.1515/acgeo-2015-0061 (this issue).

    Google Scholar 

  • Willmott, C.J. (1984), On the evaluation of model performance in physical geography. In: G.L. Gaile and C.J. Willmott (eds.), Spatial Statistics and Models, Theory and Decision Library, Vol. 40, Reidel Publ., Boston, 443–460.

    Article  Google Scholar 

  • Zhan, X., W.P. Kustas, and K.S. Humes (1996), An intercomparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature, Remote Sens. Environ. 58, 3, 242–256, DOI: 10.1016/S0034-4257(96)00049-1.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Wim J. Timmermans.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Timmermans, W.J., Kustas, W.P. & Andreu, A. Utility of an Automated Thermal-Based Approach for Monitoring Evapotranspiration. Acta Geophys. 63, 1571–1608 (2015). https://doi.org/10.1515/acgeo-2015-0016

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1515/acgeo-2015-0016

Key words

  • remote sensing
  • water use monitoring
  • temperature index scheme
  • automated
  • operational