Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images


  • Meriem Er-Rami | Department of Land and Water Resources Management, CIHEAM-Mediterranean Agronomic Institute of Bari, Valenzano (BA), Italy.
  • Guido D'Urso Department of Agricultural Sciences, University of Naples Federico II, Portici (NA), Italy.
  • Nicola Lamaddalena Department of Land and Water Resources Management, CIHEAM-Mediterranean Agronomic Institute of Bari, Valenzano (BA), Italy.
  • Daniela D'Agostino Department of Land and Water Resources Management, CIHEAM-Mediterranean Agronomic Institute of Bari, Valenzano (BA), Italy.
  • Oscar Rosario Belfiore Department of Agricultural Sciences, University of Naples Federico II, Portici (NA), Italy.


The improvement of performance of irrigation systems plays a fundamental role in increasing their efficiency in order to reach a sound use of irrigation water. The COPAM (Combined Optimization and Performance Analysis Model) has proven its usefulness in performance evaluation of on-demand irrigation systems; however, in many cases, input data, such as water volumes delivered by hydrants, is not readily available. To support a wider application of the COPAM, we tested the possibility of using irrigation volumes estimated by means of space-borne remote sensing. The Sentinel-2 (S2) constellation provides high spatial resolution images with a frequency between 2 and 5 days, which is compatible with COPAM input requirements. In the present work, an irrigation sector in the Capitanata irrigation network (Foggia Province, no. 6 of District 10) in Italy was chosen to assess its performance by using COPAM with volumes estimated from Sentinel-2 data. As an input of COPAM, the upstream discharge was determined after a proper transformation of the estimated irrigation water requirement volumes and the recorded volumes into flowrates. The estimation of the irrigation water requirement volumes was accomplished through the estimation of crop evapotranspiration, Etcrop, and effective precipitation, Pn, by combining crop parameters (leaf area index - LAI, fractional vegetation cover - fc, and Albedo) derived from S2 images and the meteorological data from the ERA5 single levels reanalysis dataset collected for the whole study period, from June 1st to September 30th, 2019. The study comprised a comparison of the estimated irrigation water volumes and the corresponding recorded volumes. The results showed a good agreement between the estimated and the registered volumes in a large time scale for 10 days and a one-month period, while a large difference was observed in a daily time scale. The performance analysis was carried out for the overall system and at hydrant level. The estimated discharge was lower than the registered discharge, indicating better performance. Last but not least, some recommendations were proposed for improving performance in critical zones.



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Allen R.G., Pereira L.S., Raes D., Smith M. 1998. Crop evapotranspiration - guide- lines for computing crop water requirements. FAO irrigation and drainage paper, 56.: FAO, Rome, Italy.

Bacour C., Baret F., Beal D., Weiss M., Pavageau M.K. 2016. Neural network estimation of LAI, fAPAR, fCover and LAIxCab, from top of canopy MERIS reflectance data: Principles and validation. Remote Sens. Environ. 105:313-25. DOI:

Bastiaanssen, W. G. M. 1998. Remote sensing in water resources management: the state of the art. Colombo, Sri Lanka: International Water Management Institute (IWMI). ix, 118p.Available from:

Bastiaanssen W.G., Molden D.J., Makin I.W. 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agric. Water Manage. 46:137-55. DOI:

Braden H. 1985. Ein Energiehaushalts- und Verdunstungsmodell für Wasser- und Stoffhaushaltsuntersuchungen landwirtschaftlich genutzter Einzugsgebiete. Mitteilungen der Deutschen Bodenkundlichen Gesellschaft 42:294-9.

Bréda J.J.N. 2003. Ground-based measurments of leaf area index: a review of methods, instruments and current controversies. J. Exp. Botany 54: 2403-17. DOI:

Brutsaert W.H. 1982. Evaporation into the atmosphere. D. Reidel Publishing Company, Dordrecht, The Netherlands. DOI:

Clevers J.G.P.W. 1989. The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sens. Environ. 29:25-37. DOI:

D’Urso G. 2001. Simulation and management of on-demand irrigation systems: a combined agrohydrological and remote sensing approach. PhD thesis. Wageningen University, Wageningen The Netherlands.

D’Urso G., Calera Belmonte A. 2006. Operative approaches to determine crop water requirements from Earth Observation data: methodologies and applications. AIP Conf. Proc. 852:14-25. DOI:

D’Urso G., Richter K., Calera A., Osann M.A., Escadafal R., Garatuza-Pajan J. Hanich L., Perdigão A., Tapia J.B., Vuolo F. 2010. Earth Observation products for operational irrigation management in the context of the PLEIADeS project. Agric. Water Manage. 98:271-82. DOI:

Estrada C., González C., Aliod R., Paño J. 2009. Improved pressurized pipe network hydraulic solver for applications in irrigation systems. J. Irrig. Drain. Engine. 135(4). DOI:

Hersbach H., Bell B., Berrisford P., Biavati G., Horányi A., Muñoz Sabater, J., Nicolas J., Peubey C., Radu R., Rozum I., Schepers D., Simmons A., Soci C., Dee D., Thépaut J-N. 2018. ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi:10.24381/cds.adbb2d47

Kurtulmus E., Büyükcangaz H., Kusçu H. and Demir O.A. 2018. The hydraulic and economic performance analysis of on-demand pressurized irrigation systems: a case study in Turkey. J. Agric. Sci. 24:42-9. DOI:

Lamaddalena N. 1997. Integrated simulation modeling for design and performance analysis of on-demand pressurized irrigation systems. PhD Thesis. Technical University of Lisbon, Lisbon, Portugal.

Lamaddalena N., Pereira L.S. 2007. Pressure-driven modeling for performance analysis of irrigation systems operating on demand. Agric. Water Manag. 90:36-44. DOI:

Lamaddalena N., Sagardoy J.A. 2000. Performance analysis of on-demand pressurized irrigation systems. FAO irrigation and drainage paper, 59. FAO, Rome, Italy.

Ozdogan M., Yang Y., Allez G., Cervantes C. 2010. Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sens. 2:2274-304. DOI:

Pelosi A., Terribile F., D’Urso G., Chirico B.G. 2020. Comparison of ERA5-Land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for regional assessment of reference evapotranspiration. Water 12:1669. DOI:

Rolim J., Navarro A., Vilar P., Saraiva C., Catalao J. 2019. Crop data retrieval using earth observation data to support agricultural water management. Eng. Agríc. 39:380-90. DOI:

Rossman L.A. 2000. EPANET 2. Users Manual. Water supply and Water Resources Division National Risk Management Research Laboratory. U.S. Environmental Protection Agency, Cincinnati, OH, USA.

UNWAP (United Nations World Water Assessment Programme). 2016. Water and jobs. World Water Development Report 2016. UNESCO, Paris, France.

Vanino S., Pulighe G., Nino P., De Michele C., Bolognesi S.F., D’Urso G. 2015. Estimation of evapotranspiration and crop coefficients of tendone vineyards using multi-sensor remote sensing data in a Mediterranean environment. Remote Sens. 7:14708-30. DOI:

Vuolo F., D’Urso G., De Michele C., Bianchi B., Cutting M. 2015. Satellite-based irrigation advisory services: a common tool for different experiences from Europe to Australia. Agric. Water Manage. 147:82-95. DOI:


Original Articles
COPAM, crop evapotranspiration, irrigation water requirement, on-demand irrigation system, performance analysis, Sentinel-2 images.
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How to Cite
Er-Rami, M., D’Urso, G. ., Lamaddalena, N., D’Agostino, D. ., & Belfiore, O. R. (2021). Analysis of irrigation system performance based on an integrated approach with Sentinel-2 satellite images. Journal of Agricultural Engineering, 52(2).