## 1 Introduction  
As a critical source of nutrients and a fundamental element of a soil's energy, the soil organic matter (SOM) content describes the organic material that is present in soils. Changes in the SOM content, which is a key determinant of soil fertility, have a direct impact on the quantity of water and other microorganisms in the soil. These changes also have a significant effect on the biological, chemical, and physical characteristics of the soil [1]. Therefore, the rapid and accurate acquisition of the SOM content has become an inevitable need for modern agriculture development, which plays a vital role in maintaining ecological balance and promoting sustainable agricultural development. In the past few decades, methods for evaluating the SOM content have been thoroughly studied by researchers, and these methods may primarily be divided into two categories, i.e., conventional chemical technology and hyperspectral technology [2]. Due to their greater precision, stability, and dependability, traditional chemistry technologies are frequently used to determine the SOM content. But there are a lot of drawbacks, including cumbersome operation, environmental pollution, time-consuming and labor-intensive processes, and the need for experienced measurement personnel. Compared to conventional chemical technology, hyperspectral technology offers a number of benefits while retaining excellent accuracy,such as being easier to use, using fewer resources, performing analyses more quickly, and not polluting the environment. Specifically, hyperspectral techniques utilize soil spectral reflectance to capture the optical properties of soils at different wavelengths. By measuring and evaluating the spectral reflectance of soils, the physical and chemical qualities and properties of soils can be determined. With the theory of hyperspectral technology gradually improving in recent years, it has been extensively used in the rapid estimation of the SOM content. In order to determine the SOM content of the Yinchuan Plain, [PERSON] et al. [3] combined a variety of spectral transformation techniques with feature variable extraction techniques and established an estimation model of the SOM content using a variety of machine learning regression models to provide a theoretical foundation for the quick monitoring of the SOM content. In order to accurately and efficiently monitor the soil organic carbon content and the total soil nitrogen content in forest ecosystems, [PERSON] et al. [4] collected 513 topsoil samples from northeastern China as research objects. They also made an effort to quickly predict the soil organic carbon and total soil nitrogen contents in dense vegetation cover areas using various machine learning models. The presence of environmental or human influence, which might introduce more or less noise into the data, is typically unavoidable during the collection of spectral data. Therefore, it is essential to preprocess the raw spectral reflectance data. At the moment, differential transform and wavelet transform are the major preprocessing techniques used for SOM spectral reflectance data. In their study, [PERSON] et al. [5] focused on arid soil data and utilized a continuous wavelet transform with different scales to eliminate noise in the data. They combined it with a continuous projection algorithm for feature band extraction and used multiple regression methods to construct an inversion model of the soil organic carbon content. [PERSON] et al. [6] conducted research employing soil samples from soils in Liaoning Province that were generated from loess parent material in order to quickly ascertain the organic matter content of the soil samples. To lessen the interference of noise in the experiment, they used a variety of spectral modification techniques on the original spectrum data and developed a hyperspectral prediction model to determine the organic matter content unique to the relevant area. [PERSON] et al.