## 1 Introduction
This limitation has prompted the hydrological science community to continuously explore more detailed and comprehensive ET estimation methods. Therefore, the ETWatch model, generalized complementary relationship, TSEB, and some models created by team independent innovation came into being. They can use remote sensing products to achieve long-term and high-spatial-resolution evapotranspiration estimation: [PERSON] et al. [13] utilized the ETWatch model to estimate the actual ET in the Loess Plateau in China from 2000 to 2020 and employed the generalized additive model to quantitatively assess the contributions of climate factors and human activities to changes in ETa. Using the ETWatch method and the Penman-Monteith equation, [PERSON] et al. [14] estimated the daily ET over the Loess Plateau from 2000 to 2015. [PERSON] et al. [15] utilized the generalized complementary relationship to quantitatively assess the influence of climate change and vegetation restoration on the increase in ET across the Loess Plateau by establishing the relationship between AC and climatic vegetation factors in 14 watersheds in the region from 1981 to 2014. [PERSON] et al. [16] used the Zhang model to predict the actual ET on the Loess Plateau from 1986 to 2016, established a water balance equation, and quantitatively distinguished the response of ET to climate change and land use. The National Aeronautics and Space Administration (NASA) tested three mainstream evapotranspiration algorithms (the PT-JPL algorithm, PM-MOD16 algorithm, and SEBS algorithm). The PT-JPL algorithm, which had the highest Nash efficiency coefficient and the lowest root mean square error (RMSE), was chosen as the global evapotranspiration retrieval algorithm for the ECOSTRESS experiment on the space station [17]. In addition, the PT-JPL model requires the least local measurement parameters and has been widely used in ET and its component simulation [11; 17]. Our study area is located in arid and semi-arid regions. Due to the lack of detailed ground observation data, conventional ET and GPP estimation models are often limited. In this context, the PT-JPL model has shown significant advantages in a data-scarce environment. Using widely available remote sensing data and improving algorithms and integrating other estimation methods will greatly improve the accuracy of ET [11]. The model inherits the accuracy of the PT algorithm in estimating potential ET and further improves the accuracy and applicability of ET estimation by introducing advanced data processing technology and a model optimization strategy [18]. While maintaining high precision, the dependence on ground-measured data is relatively low, which greatly reduces the threshold and cost of model application, making it possible to promote its use in a wide range of areas [19]. In addition, the model also has strong prediction ability. Based on current and historical data, combined with the prediction of future climate change, the model can simulate the trend in ET in the next period of time and provide a scientific basis for water resource management, agricultural irrigation, ecological environment protection, and other strategies [20]. As an indispensable key river section in the middle and upper reaches of the Yellow River, the HLS is famous for its diverse geographical forms and complex hydrological ecosystems [21]. The HLS is a crucial component of the global and regional water cycle, serving as a significant source of water resources for the Yellow River. However, it has been affected by human activities such as natural erosion, overgrazing, and unreasonable water and soil resource development for a long time, resulting in intensified soil erosion and serious vegetation degradation. This poses a direct threat to regional ecological security and significantly affects the ecological quality of the lower Yellow River and the broader area [22].