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Automated evapotranspiration retrieval model with missing soil-related datasets: The proposal of SEBALI

Author

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  • Mhawej, Mario
  • Caiserman, Arnaud
  • Nasrallah, Ali
  • Dawi, Ali
  • Bachour, Roula
  • Faour, Ghaleb

Abstract

Precision Agriculture (PA) has been booming lately in alignment with the proposal of several surface energy balance algorithms. The Surface Energy Balance Algorithm for Land (SEBAL) remains one of the most validated and implemented systems worldwide. This model enables the estimation of Evapotranspiration (ET) in different vegetation settings. In Lebanon, winter cereals, including wheat, are arguably the most important crop types as they enter directly into the Lebanese diet. Yet, no recent studies were produced to estimate their water consumption, particularly with the pressing global warming trend. In this paper, a developed version of the open source SEBAL python script (i.e. Py-SEBAL), surnamed SEBAL-Improved or SEBALI, was proposed to estimate evapotranspiration for winter cereals (i.e. Wheat, Barley, Triticale) in the Bekaa plain of Lebanon with missing soil-related datasets. Main enhancements of SEBALI over py-SEBAL concern the ability to choose a random shape for the study site, the selection of Hot/Cold pixels over agricultural areas only, thus better selection process, as well as the usage of atmospherically corrected satellite images. More importantly, ET rates could be assessed in regions lacking soil-related datasets, due to the usage of the Water stress (Ws) factor.

Suggested Citation

  • Mhawej, Mario & Caiserman, Arnaud & Nasrallah, Ali & Dawi, Ali & Bachour, Roula & Faour, Ghaleb, 2020. "Automated evapotranspiration retrieval model with missing soil-related datasets: The proposal of SEBALI," Agricultural Water Management, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:agiwat:v:229:y:2020:i:c:s0378377419318670
    DOI: 10.1016/j.agwat.2019.105938
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    References listed on IDEAS

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    1. Yuei-An Liou & Sanjib Kumar Kar, 2014. "Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review," Energies, MDPI, vol. 7(5), pages 1-29, April.
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    5. Mhawej, Mario & Elias, Georgie & Nasrallah, Ali & Faour, Ghaleb, 2020. "Dynamic calibration for better SEBALI ET estimations: Validations and recommendations," Agricultural Water Management, Elsevier, vol. 230(C).
    6. Lima, Carlos Eduardo Santos de & Costa, Valéria Sandra de Oliveira & Galvíncio, Josiclêda Domiciano & Silva, Richarde Marques da & Santos, Celso Augusto Guimarães, 2021. "Assessment of automated evapotranspiration estimates obtained using the GP-SEBAL algorithm for dry forest vegetation (Caatinga) and agricultural areas in the Brazilian semiarid region," Agricultural Water Management, Elsevier, vol. 250(C).
    7. Teixeira, Antônio & Leivas, Janice & Struiving, Tiago & Reis, João & Simão, Fúlvio, 2021. "Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images," Agricultural Water Management, Elsevier, vol. 247(C).
    8. Wei, Jun & Cui, Yuanlai & Luo, Yufeng, 2023. "Rice growth period detection and paddy field evapotranspiration estimation based on an improved SEBAL model: Considering the applicable conditions of the advection equation," Agricultural Water Management, Elsevier, vol. 278(C).
    9. Mhawej, Mario & Nasrallah, Ali & Abunnasr, Yaser & Fadel, Ali & Faour, Ghaleb, 2021. "Better irrigation management using the satellite-based adjusted single crop coefficient (aKc) for over sixty crop types in California, USA," Agricultural Water Management, Elsevier, vol. 256(C).

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