Skip to main content
SpringerLink
Log in

Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques

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

Abstract

In this work, remote sensing-based assessments of actual evapotranspiration using METRIC integrated with a water balance model provided accurate estimates of irrigation performance. This new methodology was applied and tested in the Genil–Cabra Irrigation Scheme located in southern Spain during the 2004–2005 irrigation season. The performance indicators used, the annual relative irrigation supply (ARIS) and the irrigation water productivity (IWP), required ET input data which were calculated using either METRIC or standard FAO methodology. The new procedure that used METRIC detected overirrigation (ARIS of 1.27) in situations where the ARIS calculated with the standard FAO methodology indicated near-optimal irrigation (ARIS of 0.98). Additionally, the proposed methodology allows the estimation of the volume of applied water at the field scale. Comparisons between the ARIS and IWP values obtained from actual applied water records against those calculated with the new methodology resulted in good agreement. It is concluded that the integration of the METRIC method to calculate actual ET with a water balance model allowed the determination of performance indicators in an irrigation scheme in a reliable and accurate fashion, requiring only very limited information at the field level.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Allen RG (2000) Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J Hydrol 229:27–41

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome

    Google Scholar 

  • Allen RG, Tasumi M, Morse A, Trezza R (2005a) A Landsat-based energy balance and evapotranspiration model in western US water rights regulation and planning. Irrig Drain Syst 19:251–268

    Article  Google Scholar 

  • Allen RG, Pereira LS, Smith M, Raes D, Wright JL (2005b) FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. J Irrig Drain Eng ASCE 131(1):2–13

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Allen RG, Tasumi M, Morse A, Trezza R, Wright JL, Bastiaanssen W, Kramber W, Lorite IJ, Robison CW (2007b) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—applications. J Irrig Drain Eng ASCE 133(4):395–406

    Article  Google Scholar 

  • ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation. Environmental and Water Resources Institute of the ASCE Standardization of Reference Evapotranspiration Task Committee. American Society of Civil Engineers, Reston, p 216

    Google Scholar 

  • Bandara KMPS (2003) Monitoring irrigation performance in Sri Lanka with high-frequency satellite measurements during the dry season. Agric Water Manag 58:159–170

    Article  Google Scholar 

  • Bandara KMPS (2006) Assessing irrigation performance by using remote sensing. Doctoral thesis, Wageningen University, Wageningen

  • Barrett JWH, Skogerboe GV (1978) Effect of irrigation regime on maize yields. J Irrig Drain Eng ASCE 104:179–194

    Google Scholar 

  • Bastiaanssen WGM, Ali S (2003) A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric Ecosyst Environ 94:321–340

    Article  Google Scholar 

  • Bastiaanssen WGM, Bos MG (1999) Irrigation performance indicators based on remotely sensed data: a review of literature. Irrig Drain Syst 13:291–311

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bastiaanssen WGM, Molden DJ, Makin IW (2000) Remote sensing for irrigated agriculture: examples from research and possible applications. Agric Water Manag 46:137–155

    Article  Google Scholar 

  • Bastiaanssen WGM, Brito RAL, Bos MG, Souza RA, Cavalcanti EB, Bakker MM (2001) Low cost satellite data for monthly irrigation performance monitoring: benchmarks from Nilo Coelho, Brazil. Irrig Drain Syst 15:53–79

    Article  Google Scholar 

  • Bastiaanssen WGM, Noordman EJM, Pelgrum H, Davids 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 ASCE 131(1):85–93

    Article  Google Scholar 

  • Bastiaanssen WGM, Allen RG, Droogers P, D’Urso G, Steduto P (2007) Twenty-five years modeling irrigated and drained soils: state of the art. Agric Water Manag 92:111–125

    Article  Google Scholar 

  • Bos MG, Burton MA, Molden DJ (2005) Irrigation and drainage performance assessment. Practical guidelines. CABI, UK

    Google Scholar 

  • Burt CM, Styles SW (1999) Modern water control and management practices in irrigation. Impact on performance. Water Reports 19. FAO, Rome

    Google Scholar 

  • Burt CM, Clemmens AJ, Strelkoff TS, Solomon KH, Bliesner RD, Hardy LA, Howell TA, Eisenhauer DE (1997) Irrigation performance measures: efficiency and uniformity. J Irrig Drain Eng ASCE 123(6):423–442

    Article  Google Scholar 

  • Chavez JL, Neale CMU, Prueger JH, Kustas WP (2008) Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values. Irrig Sci 27:67–82

    Article  Google Scholar 

  • Choi M, Kustas WP, Anderson MC, Allen RG, Li F, Kjaersgaard JH (2009) An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX. Agric For Meteorol 149(12):2082–2097

    Article  Google Scholar 

  • Coelho MB, Villalobos FJ, Mateos L (2003) Modeling root growth and the soil–plant–atmosphere continuum of cotton crops. Agric Water Manag 60:99–118

    Article  Google Scholar 

  • Consoli S, D’Urso G, Toscano A (2006) Remote sensing to estimate ET-fluxes and the performance of an irrigation district in southern Italy. Agric Water Manag 81:295–314

    Article  Google Scholar 

  • Denmead OT, Shaw RH (1960) The effects of soil moisture stress at different stages of growth on the development and yield of corn. Agron J 52:272–274

    Article  Google Scholar 

  • Doorenbos J, Kassam AH (1979) Yield response to water. FAO Irrigation and Drainage Paper 33. FAO, Rome

    Google Scholar 

  • Doorenbos J, Pruit WO (1977) Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper 24. FAO, Rome

    Google Scholar 

  • Droogers P, Bastiaanssen WGM (2002) Irrigation performance using hydrological and remote sensing modelling. J Irrig Drain Eng ASCE 128(1):11–18

    Article  Google Scholar 

  • Droogers P, Kite G (1999) Water productivity from integrated basin modeling. Irrig Drain Syst 13:275–290

    Article  Google Scholar 

  • Droogers P, Kite G, Murray-Rust H (2000) Use of simulation models to evaluate irrigation performance including water productivity, risk and system analyses. Irrig Sci 19:139–145

    Article  Google Scholar 

  • English MJ, Solomon KH, Hoffman GJ (2002) A paradigm shift in irrigation management. J Irrig Drain Eng ASCE 128(5):267–277

    Article  Google Scholar 

  • Er-Raki S, Chehbouni A, Guemouria N, Duchemin B, Ezzahar J, Hadria R (2007) Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agric Water Manag 87:41–54

    Article  Google Scholar 

  • Fereres E, Soriano MA (2007) Deficit irrigation for reducing agricultural water use. J Exp Bot 58:147–159

    Article  CAS  PubMed  Google Scholar 

  • Fernández MD, González AM, Carreño J, Pérez C, Bonachela S (2007) Analysis of on-farm irrigation performance in Mediterranean greenhouses. Agric Water Manag 89:251–260

    Article  Google Scholar 

  • Field CB, Randerson JT, Malmstrom CM (1995) Global net primary production: combining ecology and remote sensing. Remote Sens Environ 51:74–88

    Article  Google Scholar 

  • García-Vila M, Lorite IJ, Soriano MA, Fereres E (2008) Management trends and responses to water scarcity in an irrigation scheme of Southern Spain. Agric Water Manag 95:458–468

    Article  Google Scholar 

  • Gavilán P, Lorite IJ, Tornero S, Berengena J (2006) Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment. Agric Water Manag 81:257–281

    Article  Google Scholar 

  • Gower ST, Kucharik CJ, Norman JM (1999) Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens Environ 70:29–51

    Article  Google Scholar 

  • Hartkamp AD, White JW, Hoogenboom G (1999) Interfacing geographic information systems with agronomic modeling: a review. Agron J 91:761–772

    Article  Google Scholar 

  • Hellegers PJGJ, Soppe R, Perry CJ, Bastiaanssen WGM (2009) Combining remote sensing and economic analysis to support decisions that affect water productivity. Irrig Sci 27:243–251

    Article  Google Scholar 

  • Howell TA (2001) Enhancing water use efficiency in irrigated agriculture. Agron J 93:281–289

    Article  Google Scholar 

  • Hsiao TC, Steduto P, Fereres E (2007) A systematic and quantitative approach to improve water use efficiency in agriculture. Irrig Sci 25:209–231

    Article  Google Scholar 

  • Immerzeel WW, Gaur A, Zwart SJ (2008) Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment. Agric Water Manag 95:11–24

    Article  Google Scholar 

  • Kalu IL, Paudyal GN, Gupta AD (1995) Equity and efficiency issues in irrigation water distribution. Agric Water Manag 28:335–348

    Article  Google Scholar 

  • Karatas BS, Akkuzu E, Unal HB, Asik S, Avci M (2009) Using satellite remote sensing to assess irrigation performance in water user associations in the Lower Gediz Basin, Turkey. Agric Water Manag 96:981–990

    Article  Google Scholar 

  • Kite G (2000) Using a basin-scale hydrological model to estimate crop transpiration and soil evaporation. J Hydrol 229:59–69

    Article  Google Scholar 

  • Kite G, Droogers P (2000) Comparing ET estimates from satellites, hydrological models and field data. J Hydrol 229:3–18

    Article  Google Scholar 

  • Kloezen WH, Garcés-Restrepo C (1998) Assessing irrigation performance with comparative indicators: the case of the Alto Rio Lerma Irrigation District, Mexico. Research Report 22. Irrigation Water Management Institute, Colombo

    Google Scholar 

  • Lobell D, Asner GP, Ortiz-Monasterio JI, Benning TL (2003) Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties. Agric Ecosyst Environ 94:205–220

    Article  Google Scholar 

  • Lorite IJ, Mateos L, Fereres E (2004a) Evaluating irrigation performance in a Mediterranean environment. I. Model and general assessment of an irrigation scheme. Irrig Sci 23:77–84

    Article  Google Scholar 

  • Lorite IJ, Mateos L, Fereres E (2004b) Evaluating irrigation performance in a Mediterranean environment. II. Variability among crops and farmers. Irrig Sci 23:85–92

    Article  Google Scholar 

  • Lorite IJ, Mateos L, Fereres E (2005) Impact of spatial and temporal aggregation of input parameters on the assessment of irrigation scheme performance. J Hydrol 300:286–299

    Article  Google Scholar 

  • Lorite IJ, Mateos L, Orgaz F, Fereres E (2007) Assessing deficit irrigation strategies at the level of an irrigation district. Agric Water Manag 91:51–60

    Article  Google Scholar 

  • Maas SJ (1988) Use of remotely sensed information in agricultural crop growth models. Ecol Model 41:247–268

    Article  Google Scholar 

  • Malano H, Burton M (2001) Guidelines for benchmarking performance in the irrigation and drainage sector. International Programme for Technology and Research in Irrigation and Drainage. FAO, Rome

    Google Scholar 

  • Mantovani EC, Villalobos FJ, Orgaz F, Fereres E (1995) Modelling the effects of sprinkler irrigation uniformity on crop yield. Agric Water Manag 27:243–257

    Article  Google Scholar 

  • MAPA (2002) Plan Nacional de Regadíos. Real Decreto 329/2002

  • MIMAM (1998) El libro blanco del agua en España. Ministerio de Medio Ambiente, Madrid

    Google Scholar 

  • Mo X, Liu S, Lin Z, Xu Y, Xiang Y, McVicar TR (2005) Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecol Model 183:301–322

    Article  Google Scholar 

  • Molden DJ, Gates TK (1990) Performance measures for evaluation of irrigation water delivery systems. J Irrig Drain Eng ASCE 116:804–823

    Article  Google Scholar 

  • Molden DJ, Sakthivadivel R, Perry CJ, de Fraiture C, Kloezen WH (1998) Indicators for comparing performance of irrigated agricultural systems. Research Report 20. International Water Management Institute, Colombo

    Google Scholar 

  • Monteith JL (1972) Solar radiation and productivity in tropical ecosystems. J Appl Ecol 9:747–766

    Article  Google Scholar 

  • Moriondo M, Maselli F, Hindi M (2007) A simple model of regional wheat yield based on NDVI data. Eur J Agron 26:266–274

    Article  Google Scholar 

  • Olioso A, Chauki H, Courault D, Wigneron J (1999) Estimation of evapotranspiration and photosynthesis by assimilation of remote sensing data into SVAT models. Remote Sens Environ 68:341–356

    Article  Google Scholar 

  • Panigrahy S, Manjunath KR, Ray SS (2005) Deriving cropping system performance indices using remote sensing data and GIS. Int J Remote Sens 26(12):2595–2606

    Article  Google Scholar 

  • Patel NR, Bhattacharjee B, Mohammed AJ, Tanupriya B, Saha SK (2006) Remote sensing of regional yield assessment of wheat in Haryana, India. Int J Remote Sens 27(19):4071–4090

    Article  Google Scholar 

  • Plummer SE (2000) Perspectives on combining ecological process models and remotely sensed data. Ecol Model 129:169–186

    Article  Google Scholar 

  • Ray SS, Dadhwal VK, Navalgund RR (2002) Performance evaluation of an irrigation command area using remote sensing: a case study of Mahi command, Gujarat, India. Agric Water Manag 56:81–91

    Article  Google Scholar 

  • Robins JS, Domingo CE (1953) Some effects of severe soil moisture deficits at specific growth stages in corn. Agron J 45:618–621

    Article  Google Scholar 

  • Rodríguez-Díaz JA, Camacho-Poyato E, López-Luque R, Pérez-Urrestarazu L (2008) Benchmarking and multivariate data analysis techniques for improving the efficiency of irrigation districts. An application in Spain. Agric Syst 96:250–259

    Article  Google Scholar 

  • Roebeling RA, Van Putten E, Genovese G, Rosema A (2004) Application of Meteosat derived meteorological information for crop yield predictions in Europe. Int J Remote Sens 25(23):5389–5401

    Article  Google Scholar 

  • Roerink GJ, Bastiaanssen WGM, Chambouleyron J, Menenti M (1997) Relating crop water consumption to irrigation water supply by remote sensing. Water Resour Manag 11:445–465

    Article  Google Scholar 

  • Sanaee-Jahromi S, Feyen J (2001) Spatial and temporal variability performance of the water delivery in irrigation schemes. Irrig Drain Syst 15:215–233

    Article  Google Scholar 

  • Santos C, Lorite IJ, Tasumi M, Allen RG, Fereres E (2008) Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level. Irrig Sci 26:277–288

    Article  Google Scholar 

  • Sarma PBS, Rao VV (1997) Evaluation of an irrigation water management scheme—a case study. Agric Water Manag 32:181–195

    Article  Google Scholar 

  • Sepulcre-Cantó G, Zarco-Tejada PJ, Jiménez-Muñoz JC, Sobrino JA, de Miguel E, Villalobos FJ (2006) Detection of water stress in an olive orchard with thermal remote sensing imagery. Agric For Meteorol 136:31–44

    Article  Google Scholar 

  • Soil Conservation Service (1972) National engineering handbook. USDA-Soil Conservation Service, Washington, DC

    Google Scholar 

  • Stewart JI, Hagan RM (1973) Functions to predict effects of crop water deficits. J Irrig Drain Div ASCE 99:421–439

    Google Scholar 

  • Stockle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping system simulation model. Eur J Agron 18:239–307

    Article  Google Scholar 

  • Tasumi M, Allen RG (2007) Satellite-based ET mapping to assess variation in ET with timing of crop development. Agric Water Manag 88:54–62

    Article  Google Scholar 

  • Tasumi M, Trezza R, Allen RG, Wright J (2005) Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid US. Irrig Drain Syst 19:355–376

    Article  Google Scholar 

  • Teixeira AHC, Bastiaanssen WGM, Moura MSB, Soares JM, Ahmad MD, Bos MG (2008) Energy and water balance measurements for water productivity analysis in irrigated mango trees, Northeast Brazil. Agric For Meteorol 148:1524–1537

    Article  Google Scholar 

  • Teixeira AHC, Bastiaanssen WGM, Ahmadd MD, Bos MG (2009a) Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Sao Francisco River basin, Brazil. Part A: calibration and validation. Agric For Meteorol 149:462–476

    Article  Google Scholar 

  • Teixeira AHC, Bastiaanssen WGM, Ahmadd MD, Bos MG (2009b) Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Sao Francisco River basin, Brazil. Part B: application to the regional scale. Agric For Meteorol 149:477–490

    Article  Google Scholar 

  • Vicente-Serrano SM, Cuadrat-Prats JM, Romo A (2006) Early prediction of crop production using drought indices at different time-scales and remote sensing data: application in the Ebro Valley (north-east Spain). Int J Remote Sens 27(3):511–518

    Article  Google Scholar 

  • Williams JR (1991) Runoff and water erosion. In: Hanks J, Ritchie JT (eds) Modeling plant and soil systems. ASA/CSSA/SSSA, Madison, pp 439–455

    Google Scholar 

  • Zwart SJ, Bastiaanssen WGM (2004) Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric Water Manag 69:115–133

    Article  Google Scholar 

  • Zwart SJ, Bastiaanssen WGM (2007) SEBAL for detecting spatial variation of water productivity and scope for improvement in eight irrigated wheat systems. Agric Water Manag 89:287–296

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the support provided by the technicians and farmers of the Genil–Cabra Irrigation Scheme and INIA. The study was supported by grants INIA-RTA05-0025 and INIA-TRT06-0014 of the Spanish Ministry of Education and Science. Development of the METRIC processing algorithms was supported by funding from the Idaho Agricultural Experiment Station, Idaho Department of Water Resources, NASA and Raytheon Company.

Author information

Authors and Affiliations

  1. IFAPA - Centro “Alameda del Obispo”, Junta de Andalucía, Alameda del Obispo s/n, Post office box: 3092, 14080, Córdoba, Spain

    C. Santos & Ignacio J. Lorite

  2. University of Miyazaki, Miyazaki, Japan

    M. Tasumi

  3. University of Idaho, Kimberly, ID, USA

    R. G. Allen

  4. IAS-CSIC and University of Córdoba, Córdoba, Spain

    E. Fereres

Authors
  1. C. Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ignacio J. Lorite
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Tasumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. G. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Fereres
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ignacio J. Lorite.

Additional information

Communicated by J. Kijne.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Santos, C., Lorite, I.J., Tasumi, M. et al. Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques. Irrig Sci 28, 461–477 (2010). https://doi.org/10.1007/s00271-010-0207-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00271-010-0207-7

Keywords

Search

Navigation