## 1 Introduction  
Evapotranspiration (ET), the combined process of evaporation and transpiration, is an essential component of the water cycle [1]. Therefore, an accurate estimation of ET is crucial to achieving efficient and sustainable water resource management [2, 3]. ET refers to the process by which water changes from a liquid to a gaseous state from different surfaces, including plants, soil, and open water bodies. Transpiration, on the other hand, refers to the water released mainly by the plant leaves after being extracted by roots [4, 5]. However, according to [6], quantification of ET has always been difficult due to an imperceptible process exclusively in an ecosystem or a watershed spatial scale with the desired level of accuracy. Estimating evapotranspiration involves direct and indirect methods. The most accurate methods are the direct or on-site measurements (i.e., lysimeter, eddy covariance, sap flow) [7]. For large-scale areas, hence, over a non-homogeneous area, a variety of surface accessory measurements are needed. On-site measurements are expensive and are mainly used to calibrate other ET estimation methods [8]. Remote sensing (RS) is recognized as the only reliable, cost-effective, and practical technology for mapping regional and mesoscale ET using surface radiances and surface energy balance components [9; 10]. The significant advantages of this technology compared to conventional methods can be summarized in three main points: (1) vast and continuous spatial coverage, (2) cost-effectiveness, and (3) monitoring where man-made measurements are difficult or unavailable [11]. Relying on RS, many models were developed for ET estimation. They range from simple models requiring data about the average temperature of air, length of the day, and development stage of the crop to complex models requiring more data such as daily radiation, temperature, vapor pressure, wind velocity, etc. There is not a distinctive model that can be used in all the conditions and not all of them are similarly reliable and accurate for different regions, thus, different climatological conditions. Solar radiation plays a key role in the field of remote sensing. It consists of various components, and understanding these components is important for accurately estimating the energy inputs that drive evapotranspiration. The partitioning of net solar radiation at the land surface includes sensible, latent, and ground heat fluxes (\(H,LE\), and \(G\), respectively). The ET process involving phase changing of water from liquid or solid to vapor, absorbs energy and cools the land surface. The accompanying energy to this process in known as latent heat. It is important to mention that conventional techniques like eddy covariance (EC) have often been used to measure the ecosystem-atmosphere exchange of water vapor and sensible heat, but they do not offer spatial distribution on a regional scale and are primarily employed for validation purposes [12; 13; 14]. Remote sensing models for ET estimation are varied. Classifying them enables us to address their complexity and diversity and consequently select the most suitable for the specific environmental conditions, considering the data availability. Classification also supports the comparative analysis of the models, allows for the evaluation of their performance, identifies advantages and drawbacks, promotes advanced research in specific categories, simplifies communication, and enhances understanding among a broader audience. However, it remains a challenging task, as the complexity is determined by the interplay of their empirical and physical components [15]. This study considers two main categories of models: temperature-based and conductance-based models. Temperature-based ET models use land surface temperature (LST) from thermal remote sensing to calculate the sensible heat flux and estimate the latent heat flux as a residual of the surface energy balance from which ET is derived.