Automated evapotranspiration retrieval model with missing soil-related datasets: The proposal of SEBALI
Résumé
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. Between November 2017 and June 2018, results show that the highest ET values were in May (i.e. flowering and grain filling growth stage), with seasonal ET values significantly varying (e.g. more than 600 mm in some regions) irrespective of the spatial location of the parcels. These results were explained by the different agricultural practices, and, to a lower extent, the varied precipitations within the study area. These outputs coupled with the produced ET cereal trend could assist decision makers as well as farmers and land owners to forecast their water consumptions and to increase their yields while conserving water resources, thus enhancing the water usage efficiency. The proposed surface energy balance algorithm (i.e. SEBALI) could be portable to other climatic regions, particularly when soil-related datasets are lacking.