Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements

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.

Section: Introductionmentioning

confidence: 99%

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

Boulet

Mougenot

Lhomme

et al. 2015

“…Solar radiation is shortwave and is the most important supplier of energy. More information on the energy balance is provided in general background material such as Brutsaert (1982), Campbell and Norman (1998) or Allen et al (1998 (Rosema, 1990), SEBAL , TSEB (Norman et al, 1995), SEBS (Su, 2002;Jia et al, 2003), METRIC (Allen et al, 2007), ALEXI (Anderson et al, 1997), and ETWatch (Wu et al, 2012). The differences among these algorithms are often related to the parameterization of H , general model assumptions, and the amount of input data required to operate these models.…”

Section: Evapotranspirationmentioning

confidence: 99%

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Karimi

Bastiaanssen

2015

124

Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95 % (SD 5 %) and rainfall with an overall absolute accuracy of 82 % (SD 15 %). Land use can be identified with an overall accuracy of 85 % (SD 7 %). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

“…The physically based and single source SEBS model (Su, 2002) is one of the surface energy balance models widely used by the scientific community. SEBS estimates actual evapotranspiration (ET) using RS retrievals and meteorology data, and it has been applied in many regional to global studies (Gokmen et al, 2013;Ma et al, 2007Ma et al, , 2012Pan et al, 2008Pan et al, , 2012Vinukollu et al, 2011;Jin et al, 2009;Oku et al, 2007;Jia et al, 2003). The details of SEBS algorithm are provided in Su (2002) and Su et al (2001).…”

Section: Et Datamentioning

confidence: 99%

Satellite-based analysis of recent trends in the ecohydrology of a semi-arid region

Gokmen

Vekerdy

Verhoef

et al. 2013

Abstract. We present a regional framework for an integrated and spatiotemporally distributed assessment of human-induced trends in the hydrology and the associated ecological health of a semi-arid basin where both human activities (i.e. agriculture) and natural ecosystems are highly groundwater dependent. To achieve this, we analysed the recent trends (from year 2000 to 2010) in precipitation, evapotranspiration (actual and potential) and vegetation greenness (i.e. NDVI) using a combination of satellite and groundbased observations. The trend assessment was applied for the semi-arid Konya Basin (Turkey), one of the largest endorheic basins in the world.The results revealed a consistent increasing trend of both yearly evapotranspiration (totally 63 MCM yr −1 from croplands) and mean NDVI (about 0.004 NDVI yr −1 in irrigated croplands), especially concentrating in the plain part of the basin, while no significant trends were observed for the precipitation and potential evapotranspiration variables. On the contrary, a consistent decreasing trend of both yearly evapotranspiration (totally −2.1 MCM yr −1 ) and mean NDVI (−0.001 NDVI yr −1 ) was observed in the wetlands, which also cannot be explained by trends in precipitation and potential evapotranspiration. The emerging picture suggest that the greening trend of the vegetation and increasing of evapotranspiration in the plain are related to land cover changes (i.e. conversion into irrigated croplands) and to the intensification of the supplementary irrigation for agriculture, which in turn caused drying out of some wetlands and the natural vegetation which mostly depend on the groundwater, the main source of irrigation water as well.Our study presented an example of the utility of spatially and temporally continuous RS data in assessing the regional trends in hydrological and ecological variables and their interactions in a spatially distributed manner in a semi-arid region, which can also be adapted to other regions. Such spatiotemporally distributed analysis at the basin level is particularly important considering that most of the water management interventions also take place at this scale.