Irrigation and Drainage Systems

, Volume 15, Issue 1, pp 53–79 | Cite as

Low Cost Satellite Data for Monthly Irrigation Performance Monitoring: Benchmarks from Nilo Coelho, Brazil

  • W.G.M. Bastiaanssen
  • R.A.L. Brito
  • M.G. Bos
  • R.A. Souza
  • E.B. Cavalcanti
  • M.M. Bakker


Irrigation performance indicators can helpwater managers to understand how anirrigation scheme operates under actualcircumstances. The new contribution ofremote sensing data, is the opportunity tostudy the crop growing conditions at scalesranging from individual fields to schemelevel. Public domain internet satellitedata have been used to calculate actual andpotential crop evapotranspiration, soilmoisture and biomass growth on a monthlybasis in the Nilo Coelho irrigation scheme,Pernambuco (Brazil). Satellite interpretedraster maps were merged with vector maps ofthe irrigation water delivery system.Monthly values of a minimum list ofirrigation performance indicators for thevarious service units in the pressurizedNilo Coelho scheme were determined. NiloCoelho is a good performing scheme. Theperformance can be improved further if 25%irrigation water is saved from February toJuly. The benchmark figures from thismodern irrigation system are presented forcomparitive analysis with other systems.The acceptable ranges in space and timeare presented. On average, 65% of thelateral pumping units on a monthly basisfall within the acceptable limits ofirrigation performance. Low cost irrigationperformance data based on low resolutionsatellite images (US$ 1/ha) will help themanagement team to focus on specificpumping units, and discuss alternativeirrigation and farm management strategieswith the stakeholders.

irrigation performance water management remote sensing benchmark figures 


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  1. Allen, R.G., Pruitt, W.O., Businger, J.A., Fritschen, L.J., Jensen, M.E. and Quinn, F.H. 1996. Evapotranspiration and transpiration, Hydrology Handbook, 2nd edition, ASCE Manuals and Reports on Engineering Practice no. 28, New York, USA.Google Scholar
  2. Asrar, G., Kanemu, E., Jackson, R.D. and Pinter, P.J. 1985. Estimation of total above-ground phytomass production using remotely sensed data. Remote Sensing of Environment 17: 211–220Google Scholar
  3. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A. and Holtslag, A.A.M. 1998. A Surface Energy Balance Algorithm for Land (SEBAL), part 1: formulation. J. of Hydr. 212–213: 198–212Google Scholar
  4. Bastiaanssen, W.G.M. and Bos, M.G. 1999. Irrigation performance indicators based on remotely sensed data: a review of literature. Irrigation and Drainage Systems 13(4): 291–311Google Scholar
  5. Bastiaanssen, W.G.M., 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J. of Hydr. 229: 87–100Google Scholar
  6. Bos, M.G. and Nugteren, J. 1974. On irrigation efficiencies, ILRI Publication no. 19, International Institute for Land Reclamation and Improvement (ILRI), Wageningen, The Netherlands: 138 ppGoogle Scholar
  7. Bos, M.G., Wolters, W., Drovandi, A. and Morabito, J.A. 1991. The Viejo Retamo secondary canal, performance evaluation case study: Mendoza, Argentina. Irrigation and Drainage Systems 5: 77–88Google Scholar
  8. Bos, M.G., Murray-Rust, D.H., Merrey, D.J., Johnson, H.G. and Snellen,W.B. 1994. Methodologies for assessing performance of irrigation and drainage management. Irrigation and Drainage Systems 5: 231–261Google Scholar
  9. Bos, M.G., 1997. Performance indicators for irrigation and drainage. Irrigation and Drainage Systems 11: 119–137Google Scholar
  10. Brito, R.A.L., Soares, J.M., Cavalcanti, E.B. and Bos, M.G. 1998. Irrigation performance assessment for Nilo Coelho scheme in Northeastern Brazil, ICID Proc., 10th Afro-Asian Regional Conference, Bali, Indonesia, Volume II-A, A13.1–7: 1–7Google Scholar
  11. Brito, R.A.L., Souza, G.H.F., Dantas Neto, J. and Azevedo, C.A.V. 2000. Performance indicators for evaluation of irrigation districts, paper no. 002106, ASAE meeting presentation, Milwaukee, Wisconsin, July 9–12, 2000: 9 pp.Google Scholar
  12. Carslon, T.N. and Ripley, D.A. 1997. On the relation between NDVI, fractional vegetation cover and leaf area index. Rem. Sens. of Env. 62: 241–252Google Scholar
  13. Castro, A.H., Azvedo, P.V. de, Silva, B.B. da and Soares, J.M. 1999. Water consumption and crop coefficient of grape vine in the region of Petrolina, Pernambuco State, Brazil, Revista Brasileira de Engenharia Agricola e Ambiental 3(3): 413–416 (in Portugese)Google Scholar
  14. Choudhury, B.J. and DiGirolamo, 1998. A biophysical process-based estimate of global land surface evapotranspiration using satellite and ancillary data, 1. model description and comparison with observations. J. of Hydr. 205: 164–185Google Scholar
  15. Cordeiro, G.C., 2000, Personal communication, Monitoring of water table fluctuations in sandy soil under irrigation, EMBRAPA, Petrolina, PE, BrasilGoogle Scholar
  16. Donald, C.M. and Hamblin, J. 1976. The biological yields and harvest index of cereals as agronomic and plant breeding criteria. Adv. Agron. 28(1): 361–405Google Scholar
  17. Doorenbos, J. and Pruitt,W.O. 1977. Crop water requirements, Irrigation and Drainage Paper no. 24, FAO, Rome, Italy: 144 ppGoogle Scholar
  18. Doorenbos, J. and Kassam, A.H. 1979. Yield response to water, Irrigation and Drainage Paper no. 33, FAO, Rome, Italy: 193 ppGoogle Scholar
  19. Fraiture, C. de and Garces-Restrepo, C. 1997. Assessing trends and changes in irrigation performance, the case of Samaca irrigation scheme, Colombia, paper presented at the International Workshop on Irrigation Performance related to RPIP, 3–7 November 1997, Mendoza, ArgentinaGoogle Scholar
  20. Gallaghar, J.N. and Biscoe, P.V. 1978. Radiation absorption, growth and yield of cereals. J. Agric. Sciences 91: 47–60Google Scholar
  21. Gieske, A., 1999. NPR software, personal communication,, International Institute of Aerospace Survey and Earth Sciences, Enschede, The NetherlandsGoogle Scholar
  22. Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Phil. Trans. Roy. Soc. London, B273: 593–610Google Scholar
  23. Jensen, M.E., 1972. Programming irrigation for greater efficiency, in (ed.) D. Hillel, Optimizing the soil physical environment toward greater crop yields, Academic Press, New York, N.Y.: 133–161Google Scholar
  24. Kloezen, W.H., 1999. Measuring land and water productivity in a Mexican irrigation district. Int. J. of Water Resources Development 14(2): 231–247Google Scholar
  25. Malano, H.M and Hofwegen, P.J.M. van, 1999. Management of irrigation and drainage systems: a service approach, IHE Nomograph 3, Balkema, Rotterdam, The Netherlands: 149 pp.Google Scholar
  26. Mekonnen, M.G. and Bastiaanssen, W.G.M. 2000. A new simple method to determine crop coefficients for water allocation planning from satellites; results from Kenya. Irrigation and Drainage Systems 14(3): 237–256Google Scholar
  27. Molden, D.J., 1997. Accounting for water use and productivity, SWIM paper 1, International Water Management Institute (IWMI), Colombo, Sri Lanka: 16 pp.Google Scholar
  28. Molden, D.J. and Sakthivadivel, R. 1999. Water accounting to assess use and productivity of water. Int. J. of Water Resources Development 15(1/2): 55–72Google Scholar
  29. Morabito, J.A., Bos, M.G., Vos, S. and Brouwer, R. 1998. The quality of service provided by the irrigation department to the users associations, Tunuyan System, Mendoza, Argentina. Irrigation and Drainage Systems 12: 49–65Google Scholar
  30. Poifo, E., 2000. Personal communication, EMBRAPA, Petrolina, PE, BrazilGoogle Scholar
  31. Priestley, C.H.B. and Taylor, R.J. 1972. On the assessment of surface flux and evapotranspiration using large-scale parameters. Mon. Weather Rev. 100: 81–92Google Scholar
  32. Sarwar, A., Bastiaanssen, W.G.M., Dam, J.C. van and Boers, Th.M. 2000. Re-evaluating drainage design criteria for the Fourth Drainage Project, Pakistan, Part 1: Results of model calibration. Irrigation and Drainage Systems 14: 257–280Google Scholar
  33. Silva, V. de Paulo Rodrigues, 2000. Estimation of irrigation needs in mango, Ph.D. thesis, Department of agro-meteorology, Campina Grande, PB, Brazil: 140 pp (in portugese).Google Scholar
  34. Todd, R.W, Evett, S.R. and Howell, T.A. 2000. The Bowen-ratio energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment. Agriculture and Forest Meteorology 103: 335–348Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • W.G.M. Bastiaanssen
    • 1
  • R.A.L. Brito
    • 2
  • M.G. Bos
    • 3
  • R.A. Souza
    • 4
  • E.B. Cavalcanti
    • 5
  • M.M. Bakker
    • 6
  1. 1.WaterWatchWageningenThe Netherlands
  2. 2.EMBRAPASete Lagoas, MGBrazil
  3. 3.International Institute for Land Reclamation and Improvement (ILRI)WageningenThe Netherlands
  4. 4.EMBRAPAPetrolina, PEBrazil
  5. 5.Nilo Coelho Irrigation District-CODEVASFPetrolina, PEBrazil
  6. 6.DHV ConsultantsAmersfoortThe Netherlands

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