Evapotranspiration estimation using remote sensing data

Evapotranspiration is a combined term for evaporation which is the direct removal of water from open water bodies, soil and vegetation surfaces in the form of vapour, and transpiration through vegetation. The above processes are very difficult to quantify

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separately, and as such they are collectively called evapotranspiration (ET). Energy supply, vapour transport and water availability (open water source or soil moisture) affect the rate of ET (Chow et al., 1988). The energy available, the carrying capacity of air (Bastiaanssen et al., 1998a; Su, 2002) and the amount of soil moisture control transpiration, and as such control ET (Biggs et al., 2008).

ET is the second largest component of the terrestrial water balance. Water that is removed in the process of ET contributes to atmospheric water vapour and cloud formation. These clouds precipitate on the same or different areas. In this way, ET plays an important role in the water balance and in the energy cycle for the maintenance of maintain the atmospheric temperature. Knowledge of ET is essential for policy makers and managers to make out decisions, and conduct technical and management tasks such as watershed management and hydrological modelling, irrigation scheduling, and weather forecasting. Moreover, this knowledge is important to understand the long term effect of landuse/landcover changes and the effect of climate change on catchment water budget (Glenn et al., 2007). In brief, the quantification of ET is essential for better management of water resources.

The quantification of ET has been undertaken in two ways. First, ET has been derived from a range of measurement systems including lysimeter, eddy covariance, Bowen ratio, water balance (gravimetric, neutron meter, other soil water sensing), sap flow and scintillometer. Second, ET has been estimated through the use of modelling techniques with hydrometeorological variables as inputs (Allen et al., 2011). The Lysimeter method is the oldest and the most direct method of deriving ET from measurement systems. In this method, ET is calculated as a residual of the water balance equation. However, the available number of lysimeters is not sufficient for the water management decision making process. Moreover, many of these lysimeters are located within cropping areas, and thus do not represent other LULCs within a catchment. In general, this situation is common to all methods that derive ET through measurement systems. The second is the modelling techniques, and this included catchment water balance, hydrometeorological equations and the energy balance method.

Several modelling techniques have been developed in the absence of derived ET from measurement systems (Thornthwaite, 1948; Penman, 1948; Monteith, 1965; Priestley and Taylor, 1972). Thornthwaite (1948) explained a way to estimate potential

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evapotranspiration (i.e. the maximum amount of water removed as a result of ET, when there is no limitation to water availability) using surface temperature as input data. Potential evapotranspiration (PET) was computed as a function of monthly average temperature. Later various correction factors were introduced to Thornthwaite’s method to improve the accuracy of estimates (Willmott et al., 1985; Camargo et al., 1999; Pereira and Pruitt, 2004). However, the Thornthwaite’s method does not account for the thermodynamic effect. As such, the accuracy of PET remains low when compared to other methods such as the Penman-Monteith and the Priestley-Taylor methods (PT) (Malek, 1987). Moreover, the Thornthwaite’s method estimates PET on a monthly basis. Thus, this information is insufficient to make decisions in certain applications like irrigation scheduling.

The above mentioned disadvantages are partially addressed by the Penman ET estimation method (Penman, 1948). The Penman ET estimation method which is essentially based on the energy used to evaporate water, was further enhanced by introducing the ‘advection effect’ into the process (Monteith, 1965). The Penman-Monteith (PM) formula is a combination of both the energy term and this advection term, and is widely used in PET estimation (Allen et al., 1998). In addition, Priestley and Taylor (1972) introduced a simplified ET estimation procedure over uniform wet surfaces. Considering that dry air moving over a uniform wet surface comes to a level of equilibrium, they simplified the energy balance by inserting a constant.

The above discussed methods are based on several meteorological variables. However, many catchments do not have sufficient ground measured data to estimate PET. Whilst heterogeneity of the surface vegetation makes estimation of ET difficult, partial canopies create further problems. Su (2002) highlighted that PET estimation procedures such as PM and PT estimate PET accurately over homogenous small areas. Nevertheless, the accuracy of estimates are reduced as the area becomes larger, in which case PET estimation procedures become insensitive due to the lack of representative hydrometeorological variables.

Over large areas, RS based ET estimation has numerous advantages (Bastiaanssen and Chandrapala, 2003; Bandara, 2003; Bos, 2004; Ahmad et al., 2005). RS gives a better representation of the ground heterogeneity, and its temporal changes. The accessibility and

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the quality of RS data are superior compared with ground measured data, and data are available to the research community on a near-real time basis. Additionally, RS data are available at zero or minimum costs. In the light of all these advantages, Bastiaanssen et al. (1998a) and Su (2002) advocated the use of surface energy balance based ET estimation method with RS data as inputs as an alternative to the above mentioned estimation methods.

The Surface Energy Balance Algorithm for Land (SEBAL) method which was proposed by Bastiaanssen et al. (1998a), uses the surface energy balance equation to estimate ET. In this method, ET is calculated as the residual of the difference between the net radiation to the surface and losses due to the ground heat flux (energy stored in the soil and vegetation) and the sensible heat flux (energy used to heat the air) (Senay et al., 2007b).

The net radiation of the above method is the difference between incoming and outgoing radiation, and RS based surface albedo and emissivity are used as inputs to calculate it. The ground heat flux is estimated using surface temperature, albedo, and NDVI. The sensible heat flux is estimated as a function of the temperature gradient above the surface. Surface roughness and wind speed are required for this, and surface roughness is calculated as a function of the NDVI in SEBAL (Bastiaanssen et al., 2002; Bastiaanssen and Chandrapala, 2003; Ahmad et al., 2005; Ahmad et al., 2009; Gamage et al., 2009a). The Surface Energy Balance Systems (SEBS) method (Su, 2002) also used the surface energy balance equation to estimate ET. This method uses the same inputs computed from RS data as the SEBAL. However, SEBS uses a numerical simulation model to estimate ET, instead of SEBAL’s hot and cold pixel approach.

The Surface energy balance method used in SEBAL and SEBS was further simplified by Senay et al. (2007b) with their proposed methodology of Simplified Surface Energy Balance (SSEB). They introduced the fraction of surface temperature instead of the hot and cold pixel approach of SEBAL and the numerical simulation approach of SEBS. Initially, evapotranspiration computed from the surface energy balance method was used to calculate water efficiency, agricultural water requirement (Ahmad et al., 2009), and water resources assessment (Muthuwatta et al., 2010). These calculations require computing daily, monthly and annual ET values. However, non-cloudy RS images are

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essential to estimate ET using the above described methods. Therefore, calculating ET continually on a daily basis is virtually impossible. This has slightly been overcome to a certain extent in monthly and annual estimation of ET by introducing a temporal integration mechanism (Ahmad et al., 2009). Temporally integrated ET can be used in water resources assessments and planning, but not for daily streamflow estimation purposes.

The above mentioned RS based ET estimation methods require a few ground measured meteorological data, with wind speed being the most important input. Unfortunately, obtaining wind speed data in data scarce catchments represent a challenging if not impossible task. Therefore, a method is proposed in this study to estimate PET, and then use it for streamflow estimation. The surface energy balance method has been modified under the proposed method, so that minimum ground measured data and maximum RS data are used.