## 4 Intercomparison of ET Estimation among the Temperature-Based Models
Similarly to the study mentioned here above, ref. [113] evaluated the performance of SEBAL, METRIC, SEBS, S-SEBI, and SSEBop in estimating ET in a high biomass sorghum field. The researchers compared the models against measurements from an eddy covariance system. The findings indicated that S-SEBI, SEBAL, and SEBS performed reasonably well overall, while METRIC and SSEBop had a poorer performance. All SEB models tended to overestimate ET during extremely dry conditions and underestimated sensible heat flux. METRIC, SEBAL, and SEBS consistently overestimated ET regardless of wet or dry periods, while S-SEBI and SSEBop significantly underestimated ET under wet conditions. The study highlighted the importance of incorporating soil moisture or plant water stress components into SEB models to improve their performance, particularly in extremely dry or wet environments. [PERSON] et al. conducted a study in 2017 aiming to estimate ET in an arid region for effective water resource management [114]. They compared METRIC and TTME for estimating \(LE\) in a specific sub-region of the Heihe River Basin in northwest China. The models were applied to Landsat-8 and Landsat-7 data during the crop growth stage from April to September 2013. The researchers evaluated the derived \(LE\) values from METRIC and TTME by comparing them with observed \(LE\) data. The results indicated that the spatial distributions of \(LE\) from both models were similar, with relative deviation values within 7%. This suggests that both METRIC and TTME models are suitable for retrieving \(LE\) in the study area. However, the precision of \(LE\) retrieval was influenced by land use/cover types. METRIC provided slightly higher accuracy than TTME in desert areas, while TTME achieved notably higher precision than METRIC in farmland/wetland areas. In a data-limited region in the Kilomberg Valley river basin, three SEB models, SEBAL, SSEBop, and S-SEBI, were compared. SEBAL and SSEBop showed high and low ET, respectively, while S-SEBI had similar ET to the ensemble mean of all models. The models provided statistically comparable ET estimates among different land cover classes and spatial scales [115]. In the same year, a study addressed the uncertainty and weight determination issues in ET estimation by adopting the Bayesian model averaging (BMA) method. Four SEB models, SEBAL, SSEB, S-SEBI, and SEBS, were combined using Landsat-8 images. BMA methods provided more accurate and reliable results compared to individual models [116]. Similarly, BMA was used by [PERSON] et al. in 2021 to improve daily ET estimation. Eight models, including SEBS, SEBAL, and machine learning algorithms, were combined, resulting in a more reliable estimate of ET [117]. The BMA method,incorporating machine learning, significantly enhanced daily ET accuracy and reduced uncertainties among models. In more recent work, the performance of the new mission ECOSTRESS thermal radiometer experiment was assessed. The PT-JPL algorithm captured the diurnal ET pattern but tended to overestimate instantaneous ET in the morning; on the other hand, the DisALEXI algorithm demonstrated higher accuracy [78]. In 2022, [PERSON] et al. compared two algorithms, SEBAL and METRIC/Earth Engine Evapotranspiration Flux (EEFLux), using Landsat-8 images during the maize growth period. The results showed that ET values varied throughout the season due to temperature changes and variations in maize cover. The EEFLux algorithm outperformed SEBAL in terms of accuracy, providing closer estimations to measured ET values with higher correlation and lower standard deviation [118]. One of the latest studies compared the SEBS and SEBAL algorithms for estimating daily wheat evapotranspiration in central Kluzestan province. Both algorithms showed high compatibility with lysimeter data, outperforming other methods and suggesting their suitability for accurate evapotranspiration estimation in the region [119].