## 1 Introduction  
Soil, as an essential component of Earth's ecosystems, serves not only as the foundational medium for agricultural production [1], but also as a critical mediator sustaining biodiversity and facilitating carbon cycling processes [2]. Under the dual pressures of climate change and human activities, global soil degradation has exhibited an alarming trend [1, 3, 4, 5, 6]. This situation highlights the urgent need for precise and efficient soil monitoring technologies to achieve the \"Zero Hunger\" and \"Life on Land\" objectives outlined in the United Nations Sustainable Development Goals (SDGs) [7, 8, 9, 10, 11, 12]. Visible and near-infrared (Vis-NIR) spectroscopy, characterized by its rapid and non-destructive analytical capabilities, has become an integral tool in contemporary soil analysis. Utilizing spectral response information within the wavelength range of 400-2500 nm, Vis-NIR spectroscopy enables the effective assessment of critical soil parameters, such as organic matter and heavy metals, thus offering technological feasibility for large-scale soil surveys [13; 14; 15; 16; 17; 18]. In terms of methodological frameworks for soil spectral modeling, traditional machine learning techniques have established diversified approaches including partial least squares regression (PLSR), support vector machine regression (SVR), random forest (RF), ridge regression, and gradient boosting trees (XGBoost). Numerous scholars have conducted extensive research in the field of soil hyperspectral inversion. For instance, [PERSON] et al. utilized the extremely randomized tree (ERT) model to predict soil electrical conductivity in northwestern China [19] while [PERSON] et al. employed the marine predators algorithm to optimize random forest models for predicting the soil organic matter content [20]. [PERSON] et al. applied an optimized XGBoost model to retrieve the soil copper content [21], while [PERSON] et al. compared linear models (GWR, PLSR) with nonlinear models (RF, SVM) to predict the arsenic concentration in soils from Pingtan Island [22]. [PERSON] et al. inverted the total nitrogen content in apple orchard soils during fertilization using hyperspectral data and various machine learning regression methods [23]. [PERSON] et al. leveraged UAV hyperspectral data and compared PLSR with ensemble learning models for the inversion of soil textures (sand, silt, clay) [24]. [PERSON] et al. combined laboratory-based spectral data with random forest and Bayesian data fusion methods to estimate the soil organic carbon in the Three-River Headwater Region [25]. [PERSON] et al. utilized hyperspectral data in conjunction with extreme learning machine (ELM) and support vector machine (SVM) for urban soil nickel concentration inversion [26]. [PERSON] et al. developed hyperspectral models for soil organic matter (SOM) in arid regions of northwest China, comparing multiple linear regression and machine learning approaches [27]. [PERSON] et al. applied continuous wavelet transform (CWT) coupled with extreme learning machine (ELM) for the rapid inversion of soil moisture content [28]. While the above studies have demonstrated considerable success in soil hyperspectral inversion using machine learning, three major issues remain. (1) Existing machine learning models often rely on principal component analysis (PCA) or manual band selection for dimensionality reduction when dealing with large-scale datasets such as the LUCAS2009. However, linear dimensionality reduction methods compromise spectral continuity (e.g., absorption peak shapes and adjacent-band relationships), leading to diminished sensitivity to subtle spectral signals (e.g., heavy metal feature peaks). (2) Even when utilizing nonlinear models (e.g., RF, XGBoost), their tree-based feature splitting mechanisms essentially represent piecewise linear approximations, thus failing to adequately characterize complex nonlinear coupling between soil elements and spectral features.