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

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

Agricultural Water Management, 2020, vol. 229, issue C

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.

Keywords: Evapotranspiration; Remote sensing; SEBAL; Winter cereal; Missing data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:229:y:2020:i:c:s0378377419318670

DOI: 10.1016/j.agwat.2019.105938

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