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

Abstract

Abstract.Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil-vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m −2 from values of the order of 50-80 W m −2 ), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution.

Assessment of actual evapotranspiration over a semi-arid heterogeneous land surface by means of coupled low resolution remote sensing data with energy balance model: comparison to extra Large Aperture Scintillometer measurements

“…SEB retrieval of stress is limited by the scale mismatch between the instantaneous estimate of the surface temperature during the satellite overpass (which can be influenced by high frequency turbulence) and the aggregated values of other forcing data which are derived from half hourly averages (Lagouarde et al, 2013;Lagouarde et al, 2015). However, general tendencies are well reproduced, with most points located within a 0.2 confidence interval (illustrated by dotted lines along the 1:1 line) as found by Boulet et al (2015) at plot scale, 525 which is encouraging in a perspective of assimilating ET or SF in a water balance model for example. Moreover, it is noted that results include small LE and LE pot values having the same order of magnitude as the measurement uncertainty itself.…”

“…Consequently, daily available energy was computed for the 10 km × 8 km sub-image at the time of Terra-MODIS ( and Aqua-MODIS ( overpass, and then was weighted by the corresponding daily footprint to get the daily available energy of the upwind area AE day-FP . The SPARSE model system of equations is fully described in Boulet et al(2015). SPARSE is similar to the TSEB model (Kustas and Norman, 1999) but includes classical expressions of the aerodynamic resistances (Choudhury and Monteith, 1988;Shuttleworth and Gurney, 1990) .…”

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

“…The parameters of the f c (NDVI) and LAI(NDVI) relationships (Equations (20) and (21), respectively) were drawn empirically from the in situ measurements performed in [61]. A comparison to the same EC measurements showed an absolute relative bias lower than 1% for SEBS and about 20% for TSEB (see Figure 4).…”

“…TSEB [20] solves two different energy budgets for the soil and the vegetation components. The net radiation flux, estimated as described in Equation (1), is partitioned according to the vegetation and bare soil cover fractions following:…”

Evapotranspiration and Surface Energy Fluxes Estimation Using the Landsat-7 Enhanced Thematic Mapper Plus Image over a Semiarid Agrosystem in the North-West of Algeria

“…They can be divided into two categories: 1) Single-source models, such as SEBAL (Surface Energy Balance Algorithm for Land) (Bastiaanssen et al, 1998;Teixeira et al, 2009), METRIC (Mapping Evapotranspiration with Internalized Calibration) (Allen et al, 2007;Trezza et al, 2013;Hamimed et al, 2014), S-SEBI (Simplified Surface Energy Balance Index) (Roerink et al, 2000), SEBS (Surface Energy Balance System) and TIM model (the Trapezoid Interpolation Model) (Sun and Kafatos, 2007;Stisen et al, 2008), that do not distinguish between soil evaporation and transpiration, but treat the land surface as one homogeneous surface. Their simplicity and yet physically sound basis has made the single-source models widely used; 2) Dual-source models, such as TSEB (Two Source Energy Balance) (French et al, 2015;Boulet et al, 2015;Xin et al, 2010), that discriminate the soil and vegetation component, aiming at a more physical description of heterogeneous surfaces when dealing with radiative and aerodynamic properties. However, they have limitations related to the difficulty of obtaining temperatures for the soil and vegetation.…”