Advertisement

Water Resources Management

, Volume 27, Issue 9, pp 3493–3506 | Cite as

A Modified SEBAL Modeling Approach for Estimating Crop Evapotranspiration in Semi-arid Conditions

  • Giorgos Papadavid
  • Diofantos G. Hadjimitsis
  • Leonidas Toulios
  • Silas Michaelides
Article

Abstract

Remote sensing methods are becoming attractive to estimate crop evapotranspiration, as they cover large areas and can provide accurate and reliable estimations; intensive field monitoring is also not required, although some ground-truth measurements can be helpful in interpreting satellite images. For the purposes of this paper, modeling and remote sensing techniques were integrated for estimating actual evapotranspiration of groundnuts (Arachishypogaea, L.) that is cultivated near Mandria Village in Paphos District of Cyprus. The Surface Energy Balance Algorithm for Land (SEBAL) was adopted for the first time in Cyprus, employing the essential adaptations for local soil and meteorological conditions. Landsat-5 TM and 7 ETM+ images were used to retrieve the needed spectral data. The SEBAL model is enhanced with empirical equations determined as part of the present study, regarding crop canopy factors, in order to increase its accuracy. Maps of ETa were created using the SEBAL modified model (CYSEBAL) for the area of interest. The results have been compared to the measurements from an evaporation pan (which was used as a reference) and those of the original SEBAL model. The statistical comparison has shown that the modified SEBAL yields results that are comparable to those of the evaporation pan. T-test application has revealed that the statistical difference between SEBAL and CYSEBAL is significant and quite crucial, especially in a place with limited surface and underground water resources.

Keywords

SEBAL model Evapotranspiration Crop canopy factors Remote sensing 

References

  1. Alexandridis T (2003) Scale effect on determination of hydrological and vegetation parameters using remote sensing techniques and GIS. PhD Thesis. Aristotle University of Thessaloniki, GreeceGoogle Scholar
  2. Alexandridis T, Chemin Y (2001) Irrigation water consumption through remote sensing. Comparison at different scales in Zhanghe irrigation system, China. Presented in First International Conference on Water Resources Management, Halkidiki, Greece, 10ppGoogle Scholar
  3. Bandara KMPS (2006)Assessing irrigation performance by using remote sensing. Doctoral thesis, Wageningen University, Wageningen, The NetherlandsGoogle Scholar
  4. Bannari A, Morin D, Huette AR, Bonn F (1995) A review of vegetation indices. Remote Sens Rev 13:95–120CrossRefGoogle Scholar
  5. Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Nino F, Weiss M, Samain O, Roujean JL, Leroy M (2007) LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: principles of the algorithm. Remote Sens Environ 110:275–286CrossRefGoogle Scholar
  6. Bastiaanssen WGM (2000) SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J Hydrol 229:87–100CrossRefGoogle Scholar
  7. Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL), part 1: formulation. J Hydrol 212-213:198–212Google Scholar
  8. Bastiaanssen WGM, Noordman EJM, Pelgrum H, David G, Thoreson BP, Allen RG (2005) SEBAL model with remotely sensed data to improve water resources management under actual field conditions. J Irrig Drain Eng 131:85–93CrossRefGoogle Scholar
  9. Clevers JGPW (1989) The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sens Envir 29:25–37CrossRefGoogle Scholar
  10. D’Urso G, Menenti M (1995) Mapping crop coefficients in irrigated areas from Landsat TM images. In: Proceedings European Symposium on Satellite Remote Sensing II, Europto, Paris, September 1995,SPIE (International Society of Optical Engineering) 2585, 41–47Google Scholar
  11. Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8:2136–2160CrossRefGoogle Scholar
  12. Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agricultural. Remote Sens Envir 90:337–352CrossRefGoogle Scholar
  13. Hadjimitsis DG, Papadavid G, Themistocleous K, Kounoudes A, Toulios L (2008) Estimating irrigation demand using satellite remote sensing: a case study of Paphos District area in Cyprus. Proc SPIE Eur Remote Sens, 15–18 September 2008 University of Wales Institute, Cardiff, UK,Proc. SPIE 7104, 71040I (2008);doi: 10.1117/12.800366
  14. Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Envir 25:295–309CrossRefGoogle Scholar
  15. Huete AR (1989) Soil influences in remotely sensed vegetation-canopy spectra. In: Asrar G (ed) Theory and application of optical remote sensing. Wiley, New York, pp 107–141Google Scholar
  16. Kite GW, Droogers P (2000) Comparing evapotranspiration estimates from satellites, hydrological models and field data. J Hydrol 229:3–18CrossRefGoogle Scholar
  17. Lang A, McMutrie R, Benson M (1991) Validity of surface area indices of PinusRadiata estimated from transmittance of the sun’s beam. Agric Forest Meteorol 57:157–170CrossRefGoogle Scholar
  18. Markou M, Papadavid G (2007) Norm input output data for the main crop and livestock enterprises of Cyprus. Agricultural Economics Report 46, ISSN 0379-0827, p 196–199Google Scholar
  19. Metochis C (1997) Assessment of irrigation water needs of main crops of Cyprus, Cyprus Agricultural Research Institute Series. Ministry of Agriculture, Natural Resources and Environment, Cyprus, NicosiaGoogle Scholar
  20. Oetter DR, Warren BC, Berterretche M, Maiersperger TK, Kennedy RE (2000) Land cover mapping in an agricultural setting using multiseasonal thematic mapper data. Remote Sens Envir 76:139–155CrossRefGoogle Scholar
  21. Papadavid G, Hadjimitsis DG (2009)Spectral signature measurements during the whole life cycle of annual crops and sustainable irrigation management over Cyprus using remote sensing and spectroradiometric data: the cases of spring potatoes and peas. Proc. of SPIE, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, Vol 7472, 747215. doi: 10.1117/12.830552
  22. Papastergiadou ES, Retalis A, Apostolakis A, Georgiadis T (2008) Environmental monitoring of spatio-temporal changes using remote sensing and GIS in a Mediterranean Wetland of Northern Greece. Water Resour Manag 22:579–594CrossRefGoogle Scholar
  23. Pereira LS, Perrier A, Allen RG, Alves I (1999) Evapotranspiration: concepts and future trends. J Irrig Drain Eng 125:45–51CrossRefGoogle Scholar
  24. Petra JG, Hellegers J, Soppe R, Perry CJ, Bastiaanssen WGM (2010) Remote Sensing and Economic Indicators for Supporting Water Resources Management Decisions. Water Resour Manag 24:2419–2436CrossRefGoogle Scholar
  25. Qi J, Kerr Y, Chehbouni A (1994) External factor consideration in vegetation index development. Proc. Phys Meas Signature Remote Sens ISPRS, pp 723–730Google Scholar
  26. Qi J, Kerr YH, Moran MS, Weltz M, Huete AR, Sorooshian S, Bryant R (2000) Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. Remote Sens Envir: 73:18–30CrossRefGoogle Scholar
  27. Roerink G, Bastiaanssen WGM, Chambouleyron J, Menenti M (1997) Relating crop water consumption to irrigation water supply by remote sensing. Water Resour Manag 11:445–465CrossRefGoogle Scholar
  28. Rogers JS, Allen LH Jr, Calvert DV (1983) Evapotranspiration from a humid-region developing citrus grove with a grass cover. Trans ASAE 26:1778–1792Google Scholar
  29. Running SW, Coughlan JC (1988) A general model of forest ecosystem processes for regional applications. I. Hydrological balance, canopy gas exchange and primary production processes. Ecol Model 42:125–154CrossRefGoogle Scholar
  30. Santos C, Lorite IJ, Allen RG, Tasumi M (2012) Aerodynamic parameterization of the satellite-based energy balance (METRIC) model for ET estimation in rainfed olive orchards of Andalusia, Spain. Water Resour Manag 26:3267–3283CrossRefGoogle Scholar
  31. Souch C, Wolfe CP, Grimmond CSB (1996) Wetland evaporation and energy partitioning: Indiana Dunes National Lakeshore. J Hydrol 184:189–208CrossRefGoogle Scholar
  32. Spiliotopoulos M, Loukas A, Vasiliades L (2008)Actual evapotranspiration estimation from satellite-based surface energy balance model in Thessaly, Greece. EGU General Assembly, 13-18 April 2008, Vienna, Austria, Geophysical Research Abstracts: Vol 10Google Scholar
  33. Tasumi M, Bastiaanssen WGM, Allen RG (2000) 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. EOSDIS Project Report, Raytheon Systems Company and the University of Idaho, USAGoogle Scholar
  34. Tiktak A, Van Grinsven HJM (1995) Review of sixteen forest–soil–atmosphere models. Ecol Model 83:35–53CrossRefGoogle Scholar
  35. Watson DJ (1947) Comparative physiological studies on growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann Bot-London 11:41–76Google Scholar
  36. Welles JM, Norman JM (1991) Instrument for indirect measurement of canopy architecture. Agron J 83:818–825CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Giorgos Papadavid
    • 1
  • Diofantos G. Hadjimitsis
    • 2
  • Leonidas Toulios
    • 3
  • Silas Michaelides
    • 4
  1. 1.Agricultural Research InstituteNicosiaCyprus
  2. 2.Department of Civil Engineering & GeomaticsCyprus University of TechnologyLemesosCyprus
  3. 3.National Agricultural Research Foundation (NAGREF)LarissaGreece
  4. 4.Cyprus Meteorological ServiceNicosiaCyprus

Personalised recommendations