## 4 Intercomparison of ET Estimation among the Temperature-Based Models  
Numerous studies have compared the different models that estimate ET with the primary objective of assessing their performance, accuracy, and reliability. Evaluating these models helps to identify the most suitable ones for specific environmental conditions and data availability, allowing for a more precise and consistent ET estimation. Additionally, intercomparisons allow for the refinement and enhancement of the existing models, facilitating advancements and further developments. This work focuses on the studies conducted over the past 15 years, and following a chronological order of development, it provides an up-to-date perspective on the subject. The two models, TSEB and SEBAL, were compared in many studies. Research conducted by [PERSON] et al. (2007) showed reasonable agreement of both models with flux tower observations, whereas significant discrepancies were found in the estimation of H, particularly related to different land cover types [104]. TSEB performed better in areas of bare soil and sparse vegetation, while modifications to SEBAL inputs improved results for certain land cover classes but worsened discrepancies for others. The study highlighted the limitations of SEBAL's simplifying assumptions and identified key inputs that affect the models' performance. In 2008, [PERSON] et al. compared four models that estimate daily ET, more precisely two single source models (SEBAL and SEBS) and two double source models (P-TSEB and S-TSEB), to assess their utility and constraints across diverse land covers and meteorological conditions [105]. S-TSEB outperformed the other models; however, SEBAL and SEBS performed well in areas with higher vegetation cover and soil moisture availability. The two single-source models showed high sensitivity to the KB\({}^{-1}\) parameter, resulting in significant variations in ET estimations due to distinct approaches in handling the roughness length for heat transfer. SSEB was compared to METRIC by [PERSON] et al. in 2008, and both models produced comparable spatial and temporal variability. However, SSEB tended to overestimate ET over water bodies, which could be improved by incorporating a correction coefficient but proved useful for large-scale agro-hydroologic applications, enabling the monitoring and assessment of crop performance and regional water balance dynamics [106]. In 2009, a study evaluated three models, TSEB, METRIC, and TIM, for estimating surface energy balance over an agricultural landscape. TSEB and METRIC performed reasonably well, while TIM exhibited larger errors. Spatial comparisons of the models' outputs revealed significant differences in modeled heat flux patterns associated with varying vegetation densities. Adjusting certain parameters in the METRIC model reduced bias in one variable but increased it in another. The study suggested that further comparisons across different landscapes are needed to improve remote sensing surface energy balance models and develop a hybrid modeling system [107]. In Yucheng, China, a study was conducted to compare three ET models using MODIS/TERRA products and ground measurements. The models under evaluation were SEBS, TSEB, and TVT (for Triangular Vegetation Temperature). TSEB showed the closest agreement with measured heat fluxes, indicating its accuracy in estimating ET. SEBS also performed reasonably well, although it was more sensitive to input errors as compared to TSEB. However, TVT exhibited poor performance due to difficulties in selecting the dry edge, especially in humid regions. This highlights the challenge of accurately estimating ET in such environments using the TVT model [108]. [PERSON] et al., 2011, compared SSEB with the METRIC model, both used for estimating ET from satellite data. The enhanced SSEB model demonstrated good agreement with METRIC when analyzing Landsat images from the 2003 growing season in south-central Idaho. In areas with less complex topography, SSEB exhibited an improved correlation as compared to METRIC.