## 1 Introduction  
The tea plant (_Camelia sinensis_) is one of the most important beverage crops in the world, covering a total area of 21.1 million ha in 2016 [1]. In China, tea plants are planted on about 3.02 million ha of land, accounting for 14.3% of the global planting area [2]. The tea plant is tolerant of shade and humidity but sensitive to water stress [3]. A lack of water, even for a short time, will affect the growth and production of tea plants, resulting in a significant yield reduction [4]. Tea plantations in China are generally located in humid subtropical and tropical regions, where the rainfall is sufficient to meet the demands of the tea plants [5]. Tea is a rain-fed crop, and thus, refined irrigation scheduling is rarely practiced in tea plantations in China [6; 7]. However, the frequency of drought and extreme precipitation events has increased considerably with global climate warming, and tea plantations in China are facing major challenges, such as crop health and a decrease in agricultural yield, due to droughs and water shortages [8; 9; 10]. The actual evapotranspiration (ET\({}_{\text{a}}\)) is an essential variable in the hydrological cycle, particularly for the optimization of water use and the management of tea plantations. Therefore, the accurate estimation of tea plantation ET\({}_{\text{a}}\) is essential for improving water management, crop health, and agricultural yield in tea plantations. Different methods and techniques have been used to measure or estimate ET\({}_{\text{a}}\). Measurement techniques for ET\({}_{\text{a}}\) include the use of weighing lysimeters, soil water measurement, eddy covariance, the Bowen ratio, and large-aperture scintillometers [11; 12; 13; 14]. Among them, the weighing lysimeters and soil water measurements for obtaining the ET\({}_{\text{a}}\) of ecosystems are based on the water balance principle [15; 16]. The eddy covariance, Bowen ratio, and large-aperture scintillometer are based on the surface energy balance and turbulent flux exchange in the atmosphere and the vegetation interface [17; 18; 19; 20; 21]. These methods have been proven effective for ET\({}_{\text{a}}\) measurement. However, the installation and maintenance of these instruments and equipment are expensive. Meanwhile, these methods also exhibit significant spatial and temporal limitations, with multiple sources of errors arising from the extensive measurements and data elaboration, which limit the reliability and transportability of the ET\({}_{\text{a}}\) measurement results [6; 22]. Various models have also been proposed for ET\({}_{\text{a}}\) estimation. The Penman-Monteith (P-M) equation is one of the most commonly used methods for evaluating ET\({}_{\text{a}}\) on the basis of meteorological and biological variables [23; 24]. Compared with other models, the P-M equation, which considers the energy balance and aerodynamic principles, exhibits high superiority in different ecosystems and under varying climatic conditions [13; 25]. However, massive meteorological variables including air temperature, wind speed, surface heat flux, solar radiation, and relative humidity are required for ET\({}_{\text{a}}\) estimation based on the P-M equation. This condition restricts the application of this model due to the availability and/or questionable quality of data from weather stations. As alternatives to the aforementioned methods, various machine learning algorithms have been applied to the estimation of ET\({}_{\text{a}}\). These algorithms are more economical and require fewer variables as input. [PERSON] et al. (2020) employed random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor (KNN) to forecast the ET\({}_{\text{a}}\) for a subtropical wetland. Artificial neural network (ANN) and support vector machine (SVM) models were implemented by [26] for estimating rain-fed maize field evapotranspiration. [PERSON] et al. (2021) used a deep neural network, RF, and symbolic regression to estimate evapotranspiration using meteorological and plant data.