## 1 Introduction  
Water is essential for human survival. Water transparency is not only a key water quality indicator that regulates aquatic ecosystem processes and reflects watershed characteristics [1] but also is a sentinel to environmental change and a useful tool for resource managers to monitor intra- and inter-annual changes in lakes. In general, the transparency of lake water is measured by manual sampling, and the Secchi depth (SD) is obtained by measuring the depth of a Secchi disk when the ship reaches the pre-arranged sampling point [2]. However, this method for water transparency monitoring only applies to specific sampling points of the waters, which is insufficient for comprehensively and accurately estimating water transparency at each location of a lake. The solution is to increase the number of sampling points; however, this would require substantial human and material resources. Hence, manual sampling and measurements are not an effective approach for monitoring lake water transparency over a large area and the results does not fully represent the transparency of the entire lake. With the emergence of remote-sensing satellites, the use of image data to evaluate lake water quality has gained traction [3; 4; 5; 6]. In addition, the Google Earth Engine (GEE) can process and analyze remote-sensing images based on Google's high-performance cluster servers, which overcomes the limitations of traditional image processing technologies [7; 8]. However, as the atmospheric correction algorithm may not be specifically designed for typical radiation in water, the direct use of Landsat 8 surface reflection data extracted from GEE may lead to aquatic remote-sensing errors [9]. Therefore, the combination of the original images extracted from GEE and an atmospheric correction algorithm suited for water bodies provides a highly efficient method to calculate water quality for the ocean and inland water bodies [10]. Remote-sensing inversion methods for water bodies are mainly categorized as semi-analytical [11; 12; 13; 14], empirical [15; 16; 17; 18], and machine learning-based [19; 20; 21; 22] methods. Semi-analytical methods use diffusion attenuation coefficients in the transparent window of water bodies in the visible domain to calculate SD. These methods involve complex parameterization processes and require hyperspectral resolution, which impedes their ability to determine the transparency of lake water [15; 16; 17; 18; 23]. Empirical methods establish the relationship between single bands (or band combinations) and lake water transparency with measured datasets by fitting different input parameters with water transparency data, and optimal regression functions are selected based on the goodness of fit to establish inversion models. These methods are simple and convenient and have achieved satisfactory results for several water bodies [16]. However, generating a unified water transparency model for different water body types from a limited dataset is challenging because band selection usually varies with the optical properties of the water environment [24]. Machine learning is another option that uses computing power to learn and identify potential patterns in datasets [19] and has been implemented successfully in the prediction of lake water quality and transparency [19; 20; 21; 22; 25]. For example, [PERSON] et al. used the multi-layer perception (MLP) method to achieve high-precision predictions of lake water quality in 2021 [25], and [PERSON] et al. successfully predicted the SD value using the support vector machines (SVM) algorithm in 2021 [21]. Owing to the complex optical characteristics and high geographic heterogeneity of lakes, it is difficult to ascertain an algorithm or a series of algorithms effective for the inversion of lake water quality globally [26; 27; 28]. Moreover, there is a scarcity of in situ monitoring data from lake-rich regions in China.