## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Single-Source Surface Energy Balance Models
#### 3.1.4 The Surface Energy Balance Algorithm for Land (SEBAL)  
SEBAL, an approach based on the relationship between visible and thermal infrared (TIR) spectral radiances, estimates hydro-meteorological parameters empirically, demonstrating accurate surface flux ratios compared to instrumental inaccuracies. It operates independently of land cover and can handle TIR images at various resolutions [44]. Validation studies conducted in Spain, Niger, and China confirm SEBAL's performance in diverse regions [44]. The method was further calibrated and validated in the Low-Middle SaoFrancisco River basin, providing improved coefficients for local conditions and accurate daily ET estimates for mixed agricultural and natural ecosystems [45].  
Sensitivity analysis reveals the importance of considering input variables, domain size, and sensor resolution in SEBAL's estimation of \(H\) for large heterogeneous areas [46]. Moreover, a modified SEBAL model demonstrates applicability in the humid south-eastern United States, providing accurate estimates of consumptive water use using remote sensing methods [47]. Understanding the impact of changes in SEBAL, such as incorporating realistic heat transfer resistance, further refines ET estimation for different hydrological regimes [48].  
Several studies have focused on enhancing the SEBAL model for improved ET estimation in various regions and conditions. A study introduced SEBAL-Advection (SEBAL-A), incorporating a component to account for advective conditions affecting ET. SEBAL-A demonstrated improved performance compared to the original model, particularly in arid and semi-arid regions [49]. In South Korea, a modified SEBAL model was calibrated using measured ET data from flux towers, showing a correlation between SEBAL and flux tower ET values with higher accuracy in rice paddy areas [50].  
To automate the calibration process, an exhaustive search algorithm was introduced, enabling the identification of endmember pixels in SEBAL. The automated approach outperformed manual methods, reducing the time required for SEBAL application and allowing for wider usage [51]. Additionally, a SEBAL and Backtracking Search Algorithm-based model (SEBAL-BSA) addressed challenges with Landsat-8 data and hot-wet pixel selection, demonstrating high accuracy in mapping ET [52]. Moreover, some studies focused on specific aspects of SEBAL, such as incorporating soil moisture as an additional parameter for anchor pixel selection, which significantly improved ET estimation accuracy [53].  
Overall, these studies collectively contribute to the refinement and applicability of the SEBAL model for accurate ET estimation in diverse regions and conditions. Modifications to the model, such as SEBAL-A, have improved ET estimation under advective conditions, making it particularly valuable in arid and semi-arid regions. Furthermore, SEBAL-A's ability to account for advection using minimal weather data enhances its utility in such areas, even when hourly weather data are lacking. The model has also benefited from optimization techniques like the exhaustive search algorithm (ESA), which automates the selection of endmember pixels based on land surface temperature and vegetation index histograms. Compared to existing automated approaches, the ESA-based method demonstrates superior performance, offering full automation of SEBAL/METIC models and reducing potential bias in ET estimates for inexperienced users. However, it is essential to adapt SEBAL to changes in satellite image characteristics in current systems.