Skip to main content

Advertisement

SpringerLink
Log in

Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values

  • Original Paper
  • Published:
Irrigation Science Aims and scope Submit manuscript

Abstract

In this study, six extrapolation methods have been compared for their ability to estimate daily crop evapotranspiration (ETd) from instantaneous latent heat flux estimates derived from digital airborne multispectral remote sensing imagery. Data used in this study were collected during an experiment on corn and soybean fields, covering an area of approximately 12 × 22 km, near Ames, Iowa. ETd estimation errors for all six methods and both crops varied from −5.7 ± 4.8% (MBE ± RMSE) to 26.0 ± 15.8%. Extrapolated ETd values based on the evaporative fraction (EF) method better compared to eddy covariance measured ET values. This method reported an average corn ETd estimate error of −0.3 mm day−1, with a corresponding error standard deviation of 0.2 mm day−1, i.e., about 5.7 ± 4.8% average under prediction when compared to average ETd values derived from eddy covariance energy balance systems. A solar radiation-based ET extrapolation method performed relatively well with ETd estimation error of 2.2 ± 10.1% for both crops. An alfalfa reference ET-based extrapolation fraction method (ETrF) yielded an overall ETd overestimation of about 4.0 ± 10.0% for both crops. It is recommended that the average daily soil heat flux not be neglected in the calculation of ETd when utilizing method EF. These results validate the use of the airborne multispectral RS-based ET methodology for the estimation of instantaneous ET and its extrapolation to ETd. In addition, all methods need to be further tested under a variety of vegetation surface homogeneity, crop growth stage, environmental and climatological conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture.

References

  • Allen R (2002) REF-ET: reference evapotranspiration calculator. Software for FAO and ASCE standardized equations. University of Idaho. Available at http://www.kimberly.uidaho.edu/ref-et/. Accessed 15 October 2005

  • Allen R, Pereira L, Raes D, Smith M (1998) Crop evapotranspiration (guidelines for computing crop water requirements). FAO Irrigation and Drainage Paper No. 56. Food and Agriculture Organization of the UN, Italy

  • Allen RG, Tasumi M, Trezza R (2007a) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model. ASCE J Irrig Drain Eng 133(4):380–394. doi:10.1061/(ASCE)0733-9437(2007)133:4(380)

    Article  Google Scholar 

  • Allen RG, Tasumi M, Morse A, Trezza A, Wright JL, Bastiaanssen W, Kramber W, Lorite-Torres I, Robison CW (2007b) Satellite-based energy balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)-Applications. ASCE J Irrig Drain Eng 133(4):395–406

    Article  Google Scholar 

  • ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation. Report by the American Soc. Of Civil Engineers (ASCE) Task Committee on Standardization of Reference Evapotranspiration. In: Allen RG, Walter IA, Elliott RL, Howell TA, Itenfisu D, Jensen ME Snyder RL ASCE, 0-7844-0805-X, Reston, VA, 204 pp

  • Bastiaanssen WGM, Menenti M, Feddes RA, Holtslang AA (1998) A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. J Hydrol 212–213:198–212

    Google Scholar 

  • Brutsaert W (2005) Hydrology: an introduction. Cambridge University Press, London, pp 618

  • Brutsaert W, Sugita M (1992) Application of self-preservation in the diurnal evolution of the surface energy budget to determine daily evaporation. J Geophys Res 97:18377–18382

    Google Scholar 

  • Bullock P, Renwick R, Angadi S, Shaykewich C (2005) Correcting daily maximum and minimum air temperature to improve estimation of reference evapotranspiration. In: Proceedings of the ASA/CSSA/SSSA 97th international annual meeting. November 6–10, Salt Lake City, UT. ASA/CSSA/SSSA

  • Cai B, Neale CMU (1999) A method for constructing three dimensional models from airborne imagery. In: 17th biennial workshop on color photography and videography in resource assessment. May 5–7, 1999. Reno, NV

  • Carlson TN, Capehart WJ, Gillies RR (1995) A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sens Environ 54:161–167

    Article  Google Scholar 

  • Chávez JL, Neale CMU, Hipps LE, Prueger JH, Kustas WP (2005) Comparing aircraft-based remotely sensed energy balance fluxes with eddy covariance tower data using heat flux source area functions. J Hydrom AMS 6(6):923–940. doi:10.1175/JHM467.1

    Article  Google Scholar 

  • Chávez JL, Gowda PH, Howell TA, Copeland KS (2007) Evaluating three evapotranspiration mapping algorithms with lysimetric data in the semi-arid Texas High Plains. In: Proceedings of the 28th annual international irrigation show, December 9–11, 2007, Irrigation Association CD-ROM, San Diego, pp 268–283

  • Chemin Y, Alexandridis T (2001) Improving spatial resolution of ET seasonal for irrigated rice in Zhanghe, China. Paper presented at the 22nd Asian Conference on remote sensing, 5–9 November, 2001, Singapore

  • Colaizzi PD, Evett SR, Howell TA, Tolk JA (2006) Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Trans ASABE 49(5):1409–1417

    Google Scholar 

  • Courault D, Seguin B, Olioso A (2003) Review of estimate ET from remote sensing data: some examples from the simplified relationship to the use of mesoscale atmospheric models. ICID workshop on remote sensing of ET for large regions, 17 September 2003, Montpellier, France

  • Courault D, Seguin B, Olioso A (2005) Review on estimation of evapotranspiration from remote sensing data: from empirical to numerical modeling approach. Irrig Drain Syst 19:223–249

    Article  Google Scholar 

  • Crago RD (2000) Conservation and variability of the evaporative fraction during the daytime. J Hydrol 180(1–4):173–194

    Google Scholar 

  • Field RT, Heiser M, Strebel DE (1994) Measurements of surface fluxes. The FIFE Information System. April 9, 1994. Available at http://www.esm.versar.com/FIFE/FIFEhome.htm http://www.esm.versar.com/FIFE/Summary/Sur_flux.htm. Accessed 10 September 2004

  • Haffeez MM, Chemin Y, Van De Giesen N, Bouman BAM (2002) Field ET estimation in central Luzon, Philippines, using different sensors: Landsat 7 ETM +, Terra Modis and Aster. Symposium on Geospatial Theory, Processing and Applications, Ottawa 2002, Canada

  • Harrison LP (1963) Fundamentals concepts and definitions relating to humidity. In: Wexler A (ed) Humidity and moisture, vol 3. N.Y. Reinhold Publishing Co, New York

    Google Scholar 

  • Hipps L (2003) Land–atmosphere interactions. Class notes and personal communication. Utah State University, PSB Department, Logan, Utah

    Google Scholar 

  • Howell TA, Schneider AD, Dusek DA, Marek TH, Steiner JL (1995) Calibration and scale performance of Bushland weighing lysimeters. Trans ASAE 38:1019–1024

    Google Scholar 

  • Ibáñez M, Castellví F (2000) Simplifying daily ET estimates over short full-canopy crops. Agron J 92:628–632

    Google Scholar 

  • Irmak S, Howell TA, Allen RA, Payero JO, Martin DL (2005) Standardized ASCE Penman-Monteith: impact of sum-of-hourly vs. 24-hour timestep computations at reference weather station sites. Trans ASAE 48(3):1063–1077

    Google Scholar 

  • Jackson TJ (2002) SMEX02 Soil Climate Analysis Network (SCAN) Station 2031, Ames, Iowa. Boulder, CO: National Snow and Ice Data Center. Digital Media

  • Jackson RD, Reginato RH, Idso SB (1977) Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resour Res 13:651–656

    Article  Google Scholar 

  • Jackson RD, Hatfield JL, Reginato RJ, Idso SB, Pinter PJ (1983) Estimation of daily ET from one-time day measurements. Agric Water Manag 7:351–362

    Article  Google Scholar 

  • Jackson RD, Moran MS, Gay LW, Raymond LH (1987) Evaluating evaporation from field crops using airborne radiometry and ground-based meteorological data. Irrig Sci 8:81–90

    Article  Google Scholar 

  • Kustas WP, Hatfield JL, Prueger JH (2005) The soil moisture-atmosphere coupling experiment (SMACEX): background, hydrometeorological conditions and preliminary findings. J Hydromet AMS 6:791–804

    Article  Google Scholar 

  • Kustas WP, Perry EM, Doraiswamy PC, Moran MS (1994) Using satellite remote sensing to extrapolate evapotranspiration estimates in time and space over a semiarid Rangeland basin. Remote Sens Environ 49(3):275–286

    Article  Google Scholar 

  • Lascano RJ, Van Bavel CHM (2007) Explicit and recursive calculation of potential and actual evapotranspiration. Agron J 99:585–590

    Article  Google Scholar 

  • Lascano RJ, Evett SR (2007) Experimental verification of a recursive method to calculate evapotranspiration. In: Proceedings of the 28th annual international irrigation show, December 9–11, 2007, San Diego, Irrigation Association CD-ROM, 687–705

  • Narasimhan B, Srinivasan R (2002) Determination of regional scale evapotranspiration of Texas from NOAA-AVHRR satellite. Final report to the Texas Water Resources Institute March 5, 2002

  • Neale CMU, Crowther B (1994) An airborne multispectral video/radiometer remote sensing system: development and calibration. Remote Sens Environ 49(3):187–194

    Article  Google Scholar 

  • Romero MG (2004) Daily Evapotranspiration estimation by means of evaporative fraction and reference evapotranspiration fraction. PhD Dissertation, Biological and Irrigation Engineering Department. Utah State University. Logan–UT, 190 pp

  • Rouse, JW, Hass RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP–351 I, pp 309–317

  • Seguin B, Courault D, Guerif M (1994) Surface temperature and evapotranspiration: Application of local scale methods to regional scales using satellite data. Remote Sens Environ 49:287–295

    Article  Google Scholar 

  • Shuttleworth WJ, Gurney RJ, Hsu AY, Ormsby JP (1989) FIFE: the variation in energy partition at surface flux sites. Int Assoc Hydrol Sci (IAHS) Publication 186: 67–74

    Google Scholar 

  • Simmers I (1977) Effects of soil heat flux on the water balance of a small catchment. Hydrol Sci 22(3):433–445

    Article  Google Scholar 

  • Suleiman A, Crago R (2004) Hourly and daytime ET from grassland using radiometric surface temperatures. Agron J 96:384–390

    Google Scholar 

  • Thunnissen HAM, Nieuwenhuis GJA (1990) A simplified method to estimate regional 24-hr evapotranspiration from thermal infrared data. Remote Sens Environ 31:211–225

    Article  Google Scholar 

  • Tolk JA, Howell TA, Evett SR (2006a) Nighttime evapotranspiration from alfalfa and cotton in a semiarid climate. Agron J 98:730–736

    Article  Google Scholar 

  • Tolk JA, Evett SR, Howell TA (2006b) Advection influences on evapotranspiration of alfalfa in a semiarid climate. Agron J 98:1646–1654

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150

    Article  Google Scholar 

  • Trezza R (2002) Evapotranspiration using a satellite-based surface energy balance with standardized ground control. Ph.D. dissertation, USU, Logan, UT, 339 pp

  • Twine TE, Kustas WP, Norman JM, Cook DR, Houser PR, Meyers TP, Prueger JH, Starks PJ, Wesely ML (2000) Correcting eddy-covariance flux underestimates over a grassland. Agric For Meteorol J 103(3):229–317

    Article  Google Scholar 

  • USDA (2006) USDA, NRCS. National Water and Climatic Center. Available at http://www.wcc.nrcs.usda.gov/scan/. Accessed 20 July 2006

  • Vogt VJ, Niemege S, Viau AA (2001) Monitoring water stress at regional scales. In: Proceedings of the 23rd Canadian symposium on remote sensing, 21–24 August 2001, Laval University, Sainte Foy, pp 315–321

  • Walter IA, Allen RG, Elliot R, Jensen ME, Itenfisu D, Mecham B, Howell TA, Snyder R, Brown P, Echings S, Spofford T, Hattendorf M, Cuenca RH, Wright JL, Martin D (2000) ASCE’s standardized reference evapotranspiration equation. In: Evans RG, Benham BL, Trooien TP (eds). Proceedings of the 4th decennial symposium, National Irrigation Symposium, Phoenix, AZ, November, 2000. ASAE, St Joseph, pp 209–215

  • Weaver HL (1990) Temperature and humidity flux-variance relations determined by one-dimensional eddy correlation. Bound Layer Meteorol 53:77–91

    Article  Google Scholar 

  • Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C, Grelle A, Ibrom A, Law BE, Kowalski A, Meyers T, Moncrieff J, Monson R, Oechel W, Tenhunen J, Valentini R, Verma S (2002) Energy balance closure at FLUXNET sites. Agric For Meteorol 113(2002):223–243

    Article  Google Scholar 

  • Zhang L, Lemeur R (1995) Evaluation of daily ET estimates from instantaneous measurements. Agric For Meteorol 74:139–154

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to NASA’s Global Water and Energy Cycle Program, Utah Agricultural Experiment Station, to the Remote Sensing Services Laboratory-BIE-Utah State University and to the USDA-ARS, Conservation and Production Research Laboratory for their support. We also wish to thank Drs. Prasanna H. Gowda, Harikishan Jayanthi, and Jairo E. Hernandez for their constructive suggestions to improve the quality of this article.

Author information

Authors and Affiliations

  1. Conservation and Production Research Laboratory, USDA-ARS, P.O. Drawer 10, Bushland, TX, 79012, USA

    José L. Chávez

  2. Biological and Irrigation Engineering Department, Utah State University, 4105 Old Main Hill, Logan, UT, 84322, USA

    Christopher M. U. Neale

  3. National Soil Tilth Laboratory, USDA-ARS, 2150 Pammel DR, Ames, IA, 50011-3120, USA

    John H. Prueger

  4. Hydrology and Remote Sensing Laboratory, USDA-ARS, Bldg. 007, BARC-WEST, Beltsville, MD, 20705, USA

    William P. Kustas

Authors
  1. José L. Chávez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher M. U. Neale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John H. Prueger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William P. Kustas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José L. Chávez.

Additional information

Communicated by J. Ayars.

Appendix

Appendix

Table 2 EC measured daily and 30-min R n and heat fluxes over corn fields
Full size table
Table 3 EC measured daily and 30-min R n and heat fluxes over soybean fields
Full size table
Table 4 RS-ETd estimation errors including corn and soybean fields together
Full size table
Table 5 Daily ET estimation errors in “mm day−1” for corn fields
Full size table
Table 6 Daily ET estimation errors in percent (%) for corn fields
Full size table
Table 7 Daily ET estimation errors in “mm day−1” for soybean fields
Full size table
Table 8 Daily ET estimation errors in percent (%) for soybean fields
Full size table
Table 9 RS-based ETd estimation errors for corn resulting from extrapolating around noon ETi values only and when compared to ECn-based ETd values
Full size table
Table 10 RS-based ETd estimation errors for soybean resulting from extrapolating around noon ETi values only and when compared to ECn-based ETd values
Full size table
Table 11 RS-based ETd estimation errors for corn resulting from extrapolating around noon ETi values only, excluding DOY 189 and when compared to ECn-based ETd values
Full size table
Table 12 RS-based ETd estimation errors for soybean resulting from extrapolating around noon ETi values only, excluding DOY 189 and when compared to ECn-based ETd values
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chávez, J.L., Neale, C.M.U., Prueger, J.H. et al. Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values. Irrig Sci 27, 67–81 (2008). https://doi.org/10.1007/s00271-008-0122-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00271-008-0122-3

Keywords

Search

Navigation