## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Two-Source Surface Energy Balance Models
#### 3.2.3 The Disaggregated Atmosphere-Land EXchange Inverse (DisALEXI)  
DisALEXI model combines low- and high-resolution remote sensing data to estimate surface energy fluxes. It uses surface brightness-temperature-change measurements from the Geostationary Operational Environmental Satellite (GOES) to estimate average fluxes over a 5 km scale (ALEXI algorithm). These estimates are then disaggregated using high-resolution images of vegetation index and surface temperature, and the model was named DisALEXI. This approach shows potential for estimating field-scale fluxes without local observations and allows for scaling to a surface flux-tower footprint [76].  
Subsequent research explored the effectiveness of the DisALEXI model in downscaling regional-scale flux estimates to micrometeorological scales. Thermal imagery from GOES and Landsat was used, and the accuracy of the approach was evaluated, showing good agreement with observed fluxes at 30 m resolution. Disaggregation validated regional-scale flux predictions over heterogeneous landscapes while thermal sharpening enhanced visual information content and model convergence rate [69].  
The DisALEXI model proved to be capable of integrating data from multiple geostationary meteorological satellite systems for global applications. Ongoing work involves evaluating multi-scale ALEXI implementations across different continents with geostationary satellite coverage [72]. Further studies have demonstrated the application of the ALEXI/DisALEXI model in optimizing irrigation strategies in vineyards, providing high-resolution and daily ET estimates. The model aligns well with flux tower observations and offers valuable information about water use variability within and around the vineyards, aiding in water conservation efforts [77].  
The evaluation of the Ecosystem Spaceborne Thermal Radiometer Experiment on the International Space Station (ECOSTRESS) mission's capability in monitoring ET, particularly using the PT-JPL and DisALEXI algorithms, was lacking. Comparisons between the two products revealed the higher accuracy of DisALEXI in capturing ET patterns, magnitude, and seasonal variability. The study also emphasized the importance of accurately representing local conditions in ET models, given the heterogeneity of ET values in small-scale agroecosystems [78].  
A study performed by [PERSON] et al. (2023) focused on evaluating partitioned evaporation (E), and transpiration (T) estimates using two formulations of DisALEXI: DisALEXI-PM (Penman-Monteith) and DisALEXI-PT (Priestley-Taylor) [79]. Knowing that Priestley-Taylor is a method that was introduced in 1972 to solve the data availability issue [80]. It requires less information for the ET estimation. Priestley-Taylor is considered the most precise among the simplified methods with reduced parameters [81].  
[PERSON] et al. 2023 conducted the validation against partitioned fluxes derived from the conditional eddy covariance approach in vineyards and almond orchards. The results showed that DisALEXI-PT overestimated E and slightly underestimated T, while DisALEXI-PM agreed better with E but overestimated T under non-advective conditions. DisALEXI-PM estimated cooler canopy temperatures and warmer soil temperatures compared to DisALEXI-PT, resulting in differences in E and T. The evaluation highlighted the iterative process of DisALEXI and its potential for improving specific soil and canopy water use information for better irrigation and water use efficiencies in water-limited systems.