Department of Hydrology and Water Resources Management, Faculty of
Meteorology, Environment & Arid Land Agriculture, King
Abdulaziz University Jeddah, 21589, Saudi Arabia
Received: 28 Nov 2016 – Discussion started: 08 Dec 2016
Abstract. Daily evapotranspiration is a major component of water resources management plans. In arid ecosystems, the quest for an efficient water budget is always hard to achieve due to insufficient irrigational water and high evapotranspiration rates. Therefore, monitoring of daily evapotranspiration is a key practice for sustainable water resources management, especially in arid environments. Remote sensing techniques offered a great help to estimate the daily evapotranspiration on a regional scale. Existing open-source algorithms proved to estimate daily evapotranspiration comprehensively in arid environments. The only deficiency of these algorithms is the course scale of the used remote sensing data. Consequently, the adequate downscaling algorithm is a compulsory step to rationalize an effective water resources management plan. Daily evapotranspiration was estimated fairly well using an Advance Along-Track Scanner Radiometer (AATSR) in conjunction with (MEdium Resolution Imaging Spectrometer) MERIS data acquired in July 2013 with 1 km spatial resolution and 3 days of temporal resolution under a surface energy balance system (SEBS) model. Results were validated against reference evapotranspiration ground truth values using standardized Penman–Monteith method with R2 of 0.879. The findings of the current research successfully monitor turbulent heat fluxes values estimated from AATSR and MERIS data with a temporal resolution of 3 days only in conjunction with reliable meteorological data. Research verdicts are necessary inputs for a well-informed decision-making processes regarding sustainable water resource management.
Revised: 06 Feb 2017 – Accepted: 25 Feb 2017 – Published: 14 Mar 2017
Elhag, M. and Bahrawi, J. A.: Realization of daily evapotranspiration in arid ecosystems based on remote sensing techniques, Geosci. Instrum. Method. Data Syst., 6, 141-147, doi:10.5194/gi-6-141-2017, 2017.