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WATPRO: A remote sensing based model for mapping water productivity of wheat

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  • Zwart, Sander J.
  • Bastiaanssen, Wim G.M.
  • de Fraiture, Charlotte
  • Molden, David J.

Abstract

Water productivity in agriculture needs to be improved significantly in the coming decades to secure food supply to a growing world population. To assess on a global scale where water productivity can be improved and what the causes are for not reaching its potential, the current levels must be understood. This paper describes the development and validation of a WATer PROductivity (WATPRO) model for wheat that is based on remote sensing-derived input data sets, and that can be applied at local to global scales. The model is a combination of Monteith's theoretical framework for dry matter production in plants and an energy balance model to assess actual evapotranspiration. It is shown that by combining both approaches, the evaporative fraction and the atmospheric transmissivity, two parameters which are usually difficult to estimate spatially, can be omitted. Water productivity can then be assessed from four spatial variables: broadband surface albedo, the vegetation index NDVI, the extraterrestrial radiation and air temperature. A sensitivity analysis revealed that WATPRO is most sensitive to changes in NDVI and least sensitive to changes in air temperature. The WATPRO model was applied at 39 locations where water productivity was measured under experimental conditions. The correlation between measured and modelled water productivity was low, and this can be mainly attributed to differences in scales and in the experimental and modelling periods. A comparison with measurements from farmer's fields in areas surrounded by other wheat fields located in Sirsa District, NW India, showed an improved correlation. Although not a validation, a comparison with SEBAL-derived water productivity in the same region in India proved that WATPRO can spatially predict water productivity with the same spatial variation.

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  • Zwart, Sander J. & Bastiaanssen, Wim G.M. & de Fraiture, Charlotte & Molden, David J., 2010. "WATPRO: A remote sensing based model for mapping water productivity of wheat," Agricultural Water Management, Elsevier, vol. 97(10), pages 1628-1636, October.
    • Handle: RePEc:eee:agiwat:v:97:y:2010:i:10:p:1628-1636
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    2. Sabzchi-Dehkharghani, Hamed & Nazemi, Amir Hossein & Sadraddini, Ali Ashraf & Majnooni-Heris, Abolfazl & Biswas, Asim, 2021. "Recognition of different yield potentials among rain-fed wheat fields before harvest using remote sensing," Agricultural Water Management, Elsevier, vol. 245(C).
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    5. Muhammad Mohsin Waqas & Muhammad Waseem & Sikandar Ali & Megersa Kebede Leta & Adnan Noor Shah & Usman Khalid Awan & Syed Hamid Hussain Shah & Tao Yang & Sami Ullah, 2021. "Evaluating the Spatio-Temporal Distribution of Irrigation Water Components for Water Resources Management Using Geo-Informatics Approach," Sustainability, MDPI, vol. 13(15), pages 1-20, August.
    6. Safi, Abdur Rahim & Karimi, Poolad & Mul, Marloes & Chukalla, Abebe & de Fraiture, Charlotte, 2022. "Translating open-source remote sensing data to crop water productivity improvement actions," Agricultural Water Management, Elsevier, vol. 261(C).
    7. Battude, Marjorie & Al Bitar, Ahmad & Brut, Aurore & Tallec, Tiphaine & Huc, Mireille & Cros, Jérôme & Weber, Jean-Jacques & Lhuissier, Ludovic & Simonneaux, Vincent & Demarez, Valérie, 2017. "Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery," Agricultural Water Management, Elsevier, vol. 189(C), pages 123-136.
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    9. Mercedeh Taheri & Abdolmajid Mohammadian & Fatemeh Ganji & Mostafa Bigdeli & Mohsen Nasseri, 2022. "Energy-Based Approaches in Estimating Actual Evapotranspiration Focusing on Land Surface Temperature: A Review of Methods, Concepts, and Challenges," Energies, MDPI, vol. 15(4), pages 1-57, February.
    10. Hamze, Mohamad & Cheviron, Bruno & Baghdadi, Nicolas & Lo, Madiop & Courault, Dominique & Zribi, Mehrez, 2023. "Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model," Agricultural Water Management, Elsevier, vol. 283(C).
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