A Review of Evapotranspiration Estimation Models: Advances and Future Development

Evapotranspiration (ET) is a critical component of both the water and carbon cycles and plays an essential role in understanding the energy exchange between terrestrial ecosystems and the atmosphere. It encompasses processes such as soil evaporation, plant transpiration, and canopy interception and significantly influences the sustainability of terrestrial ecosystems. This review discusses various methods for estimating ET, including traditional empirical approaches, remote sensing methods, and machine learning techniques. Traditional methods, which are computationally simple and data demanding, may fail to accurately capture small-scale ET variations. Remote sensing methods provide continuous environmental data for large-scale ET estimation but are constrained by the resolution and quality of remote sensing data. Machine learning methods, by extracting features from big data, increase the precision of ET estimation, particularly in data-rich regions. However, the performance of these methods may be limited in data-scarce areas, and model complexity can lead to difficulties in interpretation. Collectively, future research should aim to improve data quality, optimize model generalizability, and explore methods that integrate physical processes with data-driven models. When selecting an ET estimation method, considerations should include model adaptability, data availability, estimation accuracy requirements, and technical operational complexity to meet the needs of specific research areas and applications.