Abstract
Abstract Accurate spatio-temporal estimation of evapotranspiration (ET) and surface energy fluxes is crucial for many agro-environmental applications, including the determination of water balance, irrigation scheduling, agro-ecological zoning, simulation of global changes in land use and forecasting crop yields. Remote sensing based energy balance models are presently most suitable for estimating ET at both temporal and spatial scales. This study presents an intercomparison of ET maps over the Habra plain in western Algeria obtained with two different models: Ts/VI trapezoid (Surface temperature/Vegetation Index Trapezoid Model) and SEBAL (Surface Energy Balance Algorithm for Land). Ts/VI trapezoid is the most used model, due to its simplicity, ease of use, few data input requirements and relatively high accuracy. It allows estimating ET directly by using the Priestley-Taylor equation. Whereas SEBAL allows estimating ET as the residual term of the energy balance equation, by using a rather complex hot and cold pixel based contextual approach to internally calibrate sensible heat flux through an iterative approach. The data set consists of four Landsat-8 OLI/TIRS images acquired on 2018-2019 and some ground measurements. In conclusion, the results show that SEBAL and Ts/VI trapezoid models provide comparable outputs and suggest that both the two models are suitable approaches for ET mapping over agricultural areas where ground measurements are scarce or difficult to collect.
Highlights
The spatio-temporal estimation of evapotranspiration (ET) from agricultural regions is important for agriculture water management, especially in arid and semiarid regions where water deficiency is becoming a major constraint for economic development (Yang et al, 2019)
This study evaluates the performance of Ts/VI trapezoid and Surface Energy Balance Algorithm for Land (SEBAL) models for ET estimation by using four images acquired by Landsat-8 OLI/TIRS
Both SEBAL and Ts/VI trapezoid models are developed in C++ code
Summary
The spatio-temporal estimation of evapotranspiration (ET) from agricultural regions is important for agriculture water management, especially in arid and semiarid regions where water deficiency is becoming a major constraint for economic development (Yang et al, 2019). Remote sensing-based energy balance models are presently best alternatives for estimating ET at both field and regional scales where ground measurements are not feasible and reliable estimates of ET are needed (Yang et al, 2019; Zhang et al, 2016). The ET is a major component of the terrestrial hydrological cycle, nearly two-thirds of precipitation over land is returned back to the atmosphere by ET (Brutsaert, 2013) This proportion may be higher in dry regions, such as the Mediterranean basin (Boulet et al, 2020). Inaccurate estimates of ET in these regions can cause large errors in the hydrological components prediction such as runoff and recharge, and in the associated water balance and water resources availability (Zhang et al, 2017)
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