## 1 Introduction  
Evapotranspiration (ET) is an essential component of the global water cycle and the land surface energy balance [1], with transpiration (Tr) predominating in the evapotranspiration of terrestrial ecosystems [2]. Therefore, accurately estimating vegetation transpiration not only helps in understanding plant water use strategies and guiding forest management [3], but is also crucial for understanding the exchange of carbon, water, and energy between terrestrial ecosystems and the atmosphere [4]. At present, techniques for measuring leaf and individual tree scale transpiration are relatively well-developed, but with the scale up to regional or even global levels, the estimation of transpiration will exhibit uncertainty [5]. At the leaf and individual tree scales, common methods for measuring/estimating transpiration include the gas exchange chamber method, the sap flow method, and the lysimeter method [6]. At the ecosystem scale, the eddy covariance method based on micrometeorology can be used to reliably observe evapotranspiration. The methods for estimating large-scale evapotranspiration using remote sensing techniques are mainly divided into three categories: models based on land surface water or energy balance [7; 8], models based on meteorological simplification and mechanistic process [9], and empirical or machine learning models driven by meteorological/optical remote sensing parameters or vegetation indices (VIs) [10]. With the development of spectral observation techniques and retrieval algorithms, it has been found that sun-induced chlorophyll fluorescence (SIF) is directly related to plant physiology. Compared with conventional VIs, SIF contains information on both canopy structure and photosynthetic physiology, potentially enabling a novel approach for Tr estimation [11; 12]. SIF is the light signal re-emitted by vegetation after absorbing photosynthetic active radiation (APAR) and is a by-product of photosynthesis. Compared with active fluorescence, SIF is easier to obtain at canopy and larger scales, and has been widely used to indicate early stress of vegetation [13], observe phenology [14] and estimate gross primary productivity [15]. Stomata are the channels through which leaves control the exchange of gas. The physiological relationship between photosynthesis and fluorescence, and the coupling of photosynthesis and transpiration through stomatal behavior, provide a theoretical basis for the relationship between SIF and Tr. At present, research based on different types of terrestrial ecosystems has found correlations between SIF and Tr at multiple temporal and spatial scales [16; 17], which have utilized different approaches to retrieve Tr from SIF. The main approaches include linear empirical relationship between SIF and Tr or canopy conductance [18]; machine learning or other statistical regression models with SIF and environmental factors as independent variables [19]; SIF-based estimates of Tr through land surface, meteorological, hydrological and photosynthesis models [20; 21; 22; 23; 24]; and the establishment of mechanistic or semi-mechanistic models based on SIF that consider stomatal behavior and carbon-water coupling relationships [25; 26; 27]. Mechanistic models typically involve significant assumptions and simplifications, with certain parameters being inherently difficult to quantify. Machine learning methods can achieve precise ET inversion without requiring explicit consideration of mechanistic relationships between variables [28]. [PERSON] et al. retrieved latent heat flux using SIF data through an artificial neural network (ANN) approach [19]. [PERSON] et al. achieved canopy conductance retrieval by combining SIF and photochemical reflectance index using a random forest model [29]. However, the mismatch in spatial and temporal scales of data sources and the variability in the relationship between SIF and Tr will limit the estimation accuracy.