## 1 Introduction
[7] conducted a study using 272 soil samples from the Three-River Source Region in the Qinghai-Tibetan Plateau. In order to properly estimate the soil organic carbon content, they attempted to build a hyperspectral inversion model to determine the best spectral transformation and model combination. The findings of their study provide a scientific basis for the rapid monitoring of the soil organic carbon content in high-altitude regions with cold climates. Similar related studies can be found in citations [8; 9; 10]. In summary, several researchers have combined various spectrum processing techniques with various machine learning prediction models to create inversion models using hyperspectral technology as a basis. These models are used to keep track of the SOM content in study areas. Although the above methods have achieved good performance in different regions and provided a corresponding scientific basis for the prediction of the SOM content in the study area, none of these methods were selected, or only individual differential transformation methods were selected for analysis and did not integrate and provide a detailed overview of the different differential transformation forms in order to compare which differential transformation methods can better model the inversion of the SOM. Additionally, all of the aforementioned techniques create hyperspectral inversion models for the respective areas using conventional machine learning regression techniques [11; 12]. However, these models mostly necessitate setting model hyperparameters. The choice of hyperparameters has a crucial impact on the performance, convergence speed, and generalization ability of the model. By systematically trying different combinations of hyperparameters [13], the performance of the model can be optimized to better fit different datasets and tasks. Therefore, this study collected differential methods that have emerged in recent years for predicting the soil organic matter content using hyperspectral data. These methods were combined with both unoptimized machine learning algorithms and an optimized RandomForest (RF) model for soil organic matter inversion. The aim was to explore the optimal model combination for predicting the soil organic matter content in the study area and to validate the effectiveness of this model. In this study, we first chemically processed and acquired hyperspectral data on soil samples from the wetlands of Zhejiang to derive the organic matter content of the soils in the region. Then, the spectral data were preprocessed via ten differential transforms and underwent SCARS screening for characteristic bands. The MPA-based RF algorithm was used to predict the soil organic matter content, and it was compared with the MLR, PLSR, SVR, and unoptimized RF models. The objectives of this study are as follows: (1) to analyze the effects of ten different differential transforms that are commonly used on spectral data and derive the best combination of algorithms for the region by combining different transforms with different models, and (2) to propose a soil organic matter detection method based on hyperspectral technology, which can be applied to laboratory hyperspectral data to realize the rapid detection of the soil organic matter content.