## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Two-Source Surface Energy Balance Models
#### 3.2.2 The Atmosphere-Land EXchange Inverse (ALEXI)  
ALEXI is an operational two-source model for evaluating surface energy balance using measurements of the rate of change in radiometric surface temperature. It eliminates the need for ancillary measurements of near-surface air temperature and accurately decomposes surface radiometric temperature into soil and vegetation components. It predicts angular dependence from a single view angle and has been evaluated against field experiment data with comparable uncertainties to models requiring air temperature inputs. A strategy for applying the ALEXI model on a larger scale is briefly outlined by [69]. Among many studies reporting on model validation, [PERSON] et al. compared its outputs to ground-based observations [69].  
In 2007, further improvements were made to the ALEXI model, including an algorithm for estimating fluxes during cloudy periods [70]. This allowed for the estimation of surface fluxes across the entire US in near real-time, highlighting the model's practical applicability. Moreover, in the same year, the ALEXI model was recognized for its value in monitoring troughs, utilizing thermal infrared imagery and vegetation data [71]. The model's evaporative stress index (ESI) correlated well with the Palmer drought index, providing accurate results and indicating its potential as a reliable drought monitoring tool. In 2011, [72] stated that the ALEXI model's physically-based interpretation of land-surface temperature and vegetation data allowed for daily flux mapping at large scales.  
The practical application of the ALEXI model for optimizing irrigation management in California's Central Valley was demonstrated in 2019 [73]. The model accurately reflected irrigation practices, offering valuable insights into water use and crop stress, thereby contributing to operational irrigation management.  
Recently, a study focused on calibrating the SWAT model using remotely sensed hydrologic variables and ALEXI ET, MODIS ET, and SMERGE soil moisture, in addition to streamflow data. Results show that incorporating remotely sensed ET and soil moisture affects sensitive model parameters but may not improve model performance. Using only remotely sensed ET or soil moisture data leads to worse performance compared to using streamflow alone. Different combinations of streamflow, ET, and soil moisture data affect simulated hydrologic processes. Comparisons with the SWAT-Carbon model reveal significant performance differences. The study suggests evaluating multiple remote sensing options for calibration and considering different model structures for robust hydrologic modeling [74].  
Using the TSEB concept, ALEXI introduced an additional element that leverages the accumulated thermal energy within the planetary boundary layer over time. This element serves to refine the estimation of regional sensible heat flux and to enhance the accuracy of ET estimation. Moreover, recent advancements in technology have ushered in sophisticated instruments that offer significant potential in addressing the challenging gaps that exist within the realm of remote sensing. An important example is the use by [75] of passive microwave sensors, capable of penetrating non-precipitating cloud cover, thereby enabling observations. The obtained information can derive LST, which is not possible to estimate from thermal RS in the presence of clouds. This capability remains relevant, even when dealing with datasets characterized by relatively coarse spatial resolutions.