Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate

Abstract

Abstract. Instantaneous evapotranspiration rates and surface water stress levels can be deduced from remotely sensed surface temperature data through the surface energy budget. Two families of methods can be defined: the contextual methods, where stress levels are scaled on a given image between hot/dry and cool/wet pixels for a particular vegetation cover, and single-pixel methods, which evaluate latent heat as the residual of the surface energy balance for one pixel independently from the others. Four models, two contextual (S-SEBI and a modified triangle method, named VIT) and two single-pixel (TSEB, SEBS) are applied over one growing season (December-May) for a 4 km × 4 km irrigated agricultural area in the semi-arid northern Mexico. Their performance, both at local and spatial standpoints, are compared relatively to energy balance data acquired at seven locations within the area, as well as an uncalibrated soilvegetation-atmosphere transfer (SVAT) model forced with local in situ data including observed irrigation and rainfall amounts. Stress levels are not always well retrieved by most models, but S-SEBI as well as TSEB, although slightly biased, show good performance. The drop in model performance is observed for all models when vegetation is senescent, mostly due to a poor partitioning both between turbulent fluxes and between the soil/plant components of the latent heat flux and the available energy. As expected, contextual methods perform well when contrasted soil moisture and vegetation conditions are encountered in the same image (therefore, especially in spring and early summer) while they tend to exaggerate the spread in water status in more homogeneous conditions (especially in winter). Surface energy balance models run with available remotely sensed products prove to be nearly as accurate as the uncalibrated SVAT model forced with in situ data.

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

“…Information on the magnitude of evapotranspiration (ET) from land surfaces, along with its variability in space and time, is essential for effectively managing water resources in agricultural systems (Anderson et al 2011; Bastiaanssen et al 2005; Cammalleri et al 2014; Carlson 2007; Chirouze et al 2014; Cleugh et al 2007; Jiang and Islam 2001; Kustas et al 2012; McMahon et al 2013; Morillas et al 2013; Tang et al 2011). However, the regional quantification of ET remains difficult, in particular when spatially distributed information with high resolution is sought.…”

“…One way of classifying approaches for the estimation of ET from remotely sensed TIR imagery is to divide them into two groups, contextual and single-pixel methods (Chirouze et al 2014). Contextual methods make use of the heterogeneity within an LST image and use the information of the whole image for the estimation of ET at each single pixel.…”

“…The performance of simple contextual and more complex two-source models has been studied in detail in model inter-comparisons over a wide range of landscapes mostly based on medium-resolution imagery such as Landsat 7, 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer with spatial resolutions of 60, 100, and 90 m, respectively (French, Hunsaker, and Thorp 2015; Timmermans et al 2007; Choi et al 2009; Chirouze et al 2014). These studies concluded that both contextual modelling schemes and the TSEB model perform similarly well in reproducing observed tower-based energy fluxes even though the spatial distribution and patterns in ET showed significant discrepancies.…”

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

“…This assumption is not critical at the daily time step on homogeneous bare soil and short crops [46,47]. Daily latent heat fluxes were divided by the latent heat of vaporization to obtain evapotranspiration in mm·day −1 (more details can be found in [48]). Soil moisture was measured every 30 min by water content reflectometers (CS616 probes, Campbell Scientific Inc., Logan, UT, USA), which were set up within soil pits at 5-and 30-cm depths.…”

Evaluation and Aggregation Properties of Thermal Infra-Red-Based Evapotranspiration Algorithms from 100 m to the km Scale over a Semi-Arid Irrigated Agricultural Area

“…TSEB appears to provide good ET estimates and to be less sensitive to roughness length parameters [22]. One expects flux conservation between HR and LR.…”