###### Abstract
Therefore, to achieve high-precision inversion of total phosphorus, it is necessary to develop an advanced inversion method that can thoroughly explore the spectral characteristics related to total phosphorus while possessing strong capabilities to capture nonlinear relationships. The extraction of water body water quality using remote sensing data mainly includes empirical methods, analytical methods, and data-driven methods [18]. Empirical methods utilize the statistical relationship between the spectral values of water bodies in remote sensing data and water quality to establish statistical regression models for water quality inversion [19]. Its advantage lies in the simplicity of the model construction process, but its drawback is the relatively low accuracy of water quality estimation. Analytical methods [20] use bio-optical models and radiative transfer models to simulate the propagation of light in the atmosphere and water, describing the relationship between water quality components and the radiance or reflectance spectra of waterborne radiation. However, due to the complexity of the aquatic environment and radiative transfer processes, this method encounters difficulty in achieving good estimation results in practical applications. Data-driven methods involve the utilization of artificial intelligence models such as artificial neural networks and random forests. These models take the reflectance of various bands as inputs and water quality concentration as outputs, enabling precise water quality inversion. Deep learning models like CNN and LSTM are often employed due to their ability to effectively capture linear and nonlinear relationships. [PERSON] et al. [21] successfully applied a CNN model for chlorophyll-a water quality inversion, yielding favorable results. [PERSON] [22] utilized Landsat spectral data to establish water quality inversion models for chlorophyll-a, turbidity, and phosphorus, demonstrating that the established neural network model provided a good relationship between observed and simulated water quality parameters. However, deep learning models require a large number of samples for training and the selection of appropriate network structures; otherwise, inadequate learning or overfitting may occur, significantly limiting the accuracy of neural network model inversion [23]. Meanwhile, due to the complexity of the relationship between water quality indicators and spectral reflectance, a single machine learning model cannot learn the intricate relationship between them. Ensemble learning, by integrating multiple machine learning models to reduce the variance or bias of model predictions [24; 25; 26], can achieve higher prediction performance. Single machine learning algorithms have some drawbacks, including overfitting and slow convergence [27]. Ensemble learning models have gained more attention in the field of remote sensing big data due to their outstanding fitting ability [28; 29]. [PERSON] [30] employed stacked ML algorithms using drone multispectralimages for river water quality monitoring, demonstrating that the inversion performance of stacked ensemble learning models is superior to that of single machine learning models. For data-driven models, selecting appropriate input features is crucial to ensure the accuracy of the results. Using correlation coefficients to select bands or combinations of bands is a common method. [PERSON] et al. [PERSON] et al. (2019) employed Pearson correlation coefficients to select bands, using the selected bands as inputs for SVR, pixel_DNNR, and patch_DNNR models to invert non-optically active water quality parameters. However, correlation coefficients can only reflect the linear relationship between bands and water quality indicators, failing to capture the nonlinear relationship between them. Additionally, remote sensing data often contain a large amount of noise [PERSON] et al. (2019), which can severely impact the accuracy of models. Research by [PERSON] et al. [PERSON] et al.