## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Contexture-Based ET Models  
The application of thermal remotely sensed data has demonstrated its significance in examining the biophysical characteristics and predicting ecological dynamics, as emphasized by [82]. The data embedded within the visible, near-infrared, and thermal infrared spectral bands can be harnessed to extract valuable information such as LST and vegetation indices (VIs) like the NDVI and Enhanced Vegetation Index (EVI). Figure 3 illustrates this concept presenting both the triangle and trapezoidal methods, which will be explained in detail in bellow. The concept of contexture-based models was initially introduced by [83]. The surface temperature over areas of different vegetation fractions has been extensively employed in various research studies, including investigations related to soil water content, surface resistance, land use and land cover change, drought monitoring, and regional ET [84; 85; 86; 87]. The origin of the application of this approach can be attributed to the pioneering work conducted by [83]. In this study, the researchers examined the statistical correlation between thermal emissions and reflected spectral radiance within a heterogeneous vegetated region. The results revealed a robust connection between thermal emissions and the quantity of vegetation, as evidenced by a multispectral index. Conversely, no significant relationship was observed with albedo. This observation can be rationalized by the heightened latent heat flux associated with actively transpiring vegetation. Upon these findings, [PERSON] in 1990 developed a method to estimate evapotranspiration by relating variations in LST and a vegetation index [88]. This approach required independent estimates of evapotranspiration for fully vegetated and non-vegetated areas. In the same study, it was also identified a property that helped discriminate cirrus clouds from areas with varying soil moisture. Building on previous studies, ref. [89] proposed a modification of the \"Simplified Method\" for estimating daily ET by combining the Penman-Monteith equation with the energy balance equation and using linear interpolations between surface temperature values computed for full-cover vegetation and bare soil conditions. Testing the method with ground-based measurements and Landsat Thematic Mapper (TM) images, good agreement with surface evaporation rate measurements was obtained. This modified method provided a way to estimate ET in partially vegetated fields without prior knowledge of percent vegetation cover and canopy resistance. To validate the modified method, [90] used ground-based measurements and Landsat TM images in southeast Arizona. The approach accurately estimated surface evaporation rates based on surface reflectance, temperature, and spectral vegetation index. By applying this method to different environments, regional estimates of evaporation rates for the grassland biome could be produced. [PERSON] 1990 and [PERSON] et al. 1999 mainly addressed the contradictions in the conservation equations of dual-source vegetation models and proposed schemes to estimate surface evaporation over large areas [88; 91]. These studies aimed to enhance the understanding of land surface heat fluxes and provide reliable and easily applicable methods for operational estimation of evapotranspiration. [PERSON] et al. 2003 introduced an algorithm that estimates evaporation fraction (EF) as a combination of EF of bare soil and EF of vegetation that itself estimated using the LST-VI diagram [92]. Additionally, the complementary relationship of the actual and the potential  
Figure 3: Schematic of LST–VIs models (triangle and trapezoid frameworks). ET was used for the formulation of EF. Using NOAA / AVHRR data, estimated EF values were validated with actual observations of EF at AmeriFlux stations, and the algorithm showed promising results. [PERSON] et al. 1996 used the Integral NOAA-imagery processing Chain (iNOAA-Chain) to quantify ET at a regional scale [90].