Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures

Abstract

The two source energy balance model (TSEB) can estimate evaporation (E), transpiration (T), and evapotranspiration (ET) of vegetated surfaces, which has important applications in water resources management for irrigated crops. The TSEB requires soil (T S) and canopy (T C) surface temperatures to solve the energy budgets of these layers separately. Operationally, usually only composite surface temperature (T R) measurements are available at a single view angle. For surfaces with nonrandom spatial distribution of vegetation such as row crops, T R often includes both soil and vegetation, which may have vastly different temperatures. Therefore, T S and T C must be derived from a single T R measurement using simple linear mixing, where an initial estimate of T C is calculated, and the temperature-resistance network is solved iteratively until energy balance closure is reached. Two versions of the TSEB were evaluated, where a single T R measurement was used (TSEB-T R) and separate measurements of T S and T C were used (TSEB-T C-T S). All surface temperatures (T S , T C , and T R) were measured by stationary infrared thermometers that viewed an irrigated cotton (Gossypium hirsutum L.) crop. The TSEB-T R version used a Penman-Monteith approximation for T C , rather than the Priestley-Taylor-based formulation used in the original TSEB version, because this has been found to result in more accurate partitioning of E and T under conditions of strong advection. Calculations of E, T, and ET by both model versions were compared with measurements using microlysimeters, sap flow gauges, and large monolythic weighing lysimeters, respectively. The TSEB-T R version resulted in similar overall agreement with the TSEB-T C-T S version for calculated and measured E (RMSE = 0.7 mm d À1) and better overall agreement for T (RMSE = 0.9 vs. 1.9 mm d À1), and ET (RMSE = 0.6 vs. 1.1 mm d À1). The TSEB-T C-T S version calculated daily ET up to 1.6 mm d À1 (15%) less early in the season and up to 2.0 mm d À1 (44%) greater later in the season compared with lysimeter measurements. The TSEB-T R also calculated larger ET later in the season but only up to 1.4 mm d À1 (20%). ET underestimates by the TSEB-T C-T S version may have been related to limitations in measuring T C early in the season when the canopy was sparse. ET overestimates later in the season by both versions may have been related to a greater proportion of non-transpiring canopy elements (flowers, bolls, and senesced leaves) being out of the T C and T R measurement view.

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

“…Prior inter-comparisons showed that in general flux estimates by the TSEB model are in better agreement with EC measurements over a wide range of environmental conditions and over homogeneous landscapes compared to contextual models (Choi et al 2009; French, Hunsaker, and Thorp 2015; Timmermans et al 2007; Xia et al 2015; Colaizzi et al 2012; Li et al 2005). The slightly higher error statistics of the TSEB model in this study compared to the contextual models are a consequence of the larger deviations of the model results from the observations for the two flights on 6 July, a day with scattered clouds, as discussed above in detail.…”

“…The roughness lengths for momentum and heat required for the estimation of the resistances were set to 0.125 of the vegetation height. The series resistance network is described in Colaizzi et al (2012), Kustas and Norman (1999), and Timmermans et al (2007). …”

“…The TSEB model requires information on canopy characteristics (like the Triangle Method) and ancillary meteorological data including air temperature, wind speed, vapour pressure, and atmospheric pressure. However, the TSEB model has proven to be fairly robust for a variety of landscapes and weather conditions and, compared to contextual models, also when applied over homogeneous landscapes (Li et al 2005; Colaizzi et al 2012; Kustas and Anderson 2009). Typically TIR-based ET models are applied using moderate- to medium-resolution satellite imagery such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat for which pixels represent a mixed thermal signal of various sources, which is a key assumption in the parameterization of the TSEB model.…”

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

“…Such frameworks use as input data either the component surface temperatures (e.g. soil and vegetation components retrieved from directional surface temperature data; Jia et al, 2003, or Colaizzi et al, 2012 or a single soilvegetation composite surface skin temperature. For the former, there is no current operational satellite that offers estimates of temperatures at two contrasted view angles with a very small interval between both acquisitions, even though the soon to be launched Sentinel-3 mission will have such capability (Donlon et al, 2012).…”

“…Amongst them, one can mention the development of a more complex net radiation scheme, with an initialization of soil and vegetation temperatures in separate formulations of the net radiation of the soil and the canopy or the use of an incremental decrease in a transpiration efficiency (Kustas and Norman, 1999; it corresponds roughly to the ratio between the actual and potential transpiration rates and matches the definition of the efficiency used in the present work). The TSEB rationale has been translated into several algorithms, with the possibility of using directional radiative temperatures (Kustas and Norman, 1997), day-night temperature difference (Guzinski et al, 2013; Nor- , correcting for clumping effects in sparsely vegetated areas (Anderson et al, 2005), and finally by taking into account a Penman-Monteith formulation for potential transpiration (Colaizzi et al, 2012).…”