## 1 Introduction  
Soil Organic Matter (SOM) refers to a complex mixture of organic compounds at various stages of decomposition, including plant residues, microbial products, and rhizosphere inputs [1]. It also contains fixed proportions of nitrogen (N), phosphorus (P), and sulfur (S) [2]. SOM plays multiple roles, such as storing water, retaining nutrients, improving soil physical properties, and reducing pollution, making it an important indicator of soil fertility [3]. Rapid and accurate determination of SOM content and monitoring its dynamic changes are crucial for the development of forestry and agriculture. However, traditional SOM measurement methods are costly, time-consuming, and complex, making it difficult to accurately reflect soil changes [4, 5]. Additionally, the use of large amounts of chemical solvents and analytical reagents poses risks to personnel and the environment [6]. In contrast, using hyperspectral reflectance in the visible (350-700 nm)and near-infrared (700-2500 nm) ranges to predict SOM content offers advantages such as shorter cycles, cost-effectiveness, and no pollution [7; 8]. This method has been applied to measure various soil parameters and is widely used in ecology, agriculture, medicine, and food fields [9; 10; 11]. Hyperspectral technology has demonstrated its broad effectiveness in monitoring Soil Organic Matter (SOM) content. The vast amount of spectral band information enriches the dimensions and information content of soil spectral data, providing a solid foundation for precise analysis [8]. However, this also brings challenges in data processing, including increased information redundancy and the emergence of multicollinearity issues [12]. These phenomena increase the complexity of data processing and analysis. To effectively utilize this data for predicting soil properties, it is crucial to identify appropriate data preprocessing and modeling methods [13; 14]. The main goal of data preprocessing is to improve the accuracy and robustness of models. Common mathematical transformations, Savitzky-Golay (SG) smoothing, Discrete Wavelet Transform (DWT) smoothing, Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), can effectively improve prediction models [15; 16; 17]. Mathematical transformations can effectively highlight characteristic bands of the spectrum, smoothing operations can suppress spectral noise and reduce random noise impact, MSC adjusts spectral skew by correcting scattering effects, and SNV reduces scattering effects caused by sample surface irregularities and particle size differences [18; 19; 20]. For example, [PERSON] et al. used a wavelet transform combined with nine mathematical transformations in Xinjiang to establish a Random Forest (RF) model, effectively enhancing spectral band features and reducing noise interference [21]. [PERSON] et al. found that SG denoising combined with first-order differential transformation effectively extracted the spectral characteristics of sandy loam soil in the experimental area [22]. [PERSON] et al. used Standard Normal Variate (SNV) preprocessing to establish an SVM model to predict SOM content in southern Brazil [23]. In terms of modeling, more and more researchers are using machine learning (ML) and deep learning (DL) techniques to explore the relationship between hyperspectral data and SOM. PLSR [24], RF [21], and SVM [25] are still robust prediction models in this field. With the improvement of computing power, DL is gradually being applied in various fields. DL can reveal complex nonlinear relationships between spectra and soil properties and has been proven to outperform geostatistics and other existing traditional ML algorithms [26; 27; 28]. For example, [PERSON] et al. used Scottish soil spectral data to predict multiple soil contents and found that CNN performed better than PLSR and SVM [29]. [PERSON] et al. used a dual-branch CNN network for modeling, effectively extracting spectral data features [30]. Differences in soil physical properties lead to changes in spectral data characteristics.