Abstract
Evapotranspiration (ET) is a key component of the hydrological cycle, encompassing evaporation processes from soil and water surfaces and plant transpiration (Sun et al., 2017). Accurate estimation of ET is vital for effective water resource management, agricultural planning, and environmental monitoring (Gowda et al., 2008). However, the complex interactions between land surface conditions, vegetation, and atmospheric factors make direct measurement of ET challenging, leading to the development of various estimation methods. Remote sensing has become a widely used approach for estimating ET over large areas because it provides spatially comprehensive data (Xiao et al., 2024). Methods like the Surface Energy Balance Algorithm for Land and the Surface Energy Balance System utilise satellite-derived thermal imagery and meteorological inputs to calculate ET by analysing the energy exchanges between the land surface and the atmosphere. These methods are advantageous for their broad spatial coverage, making them particularly useful for regional to global scale studies. However, they require careful calibration and validation, and their accuracy can be affected by the spatial resolution of the satellite data and the quality of meteorological inputs. In addition to remote sensing, several other ET estimation methods are commonly employed.
The Penman-Monteith equation is one of the most widely accepted methods, integrating meteorological data—such as air temperature, humidity, wind speed, and solar radiation— with biophysical properties of vegetation to estimate ET. This method has been validated extensively, making it a standard reference in ET studies. Empirical methods like the Hargreaves-Samani equation provide simpler alternatives that require fewer data inputs, making them suitable for regions with limited meteorological information but with a trade-off in accuracy. Direct measurement techniques offer highly accurate ET data, including lysimeters and eddy covariance systems. Lysimeters measure water loss directly from a soil column, while eddy covariance systems assess the exchange of water vapour and energy between the surface and the atmosphere. Despite their precision, these methods are limited by high costs, maintenance requirements, and their applicability to small-scale, homogeneous areas (Howell, 2005). Choosing the appropriate ET estimation method depends on the scale of the study, data availability, and the specific application. Remote sensing and models like Penman-Monteith offer scalability and broad applicability, while direct measurements provide precise data at localised scales. Integrating these methods can improve the reliability of ET estimates, enhance water resource management, and aid in climate adaptation efforts.