## 6 Conclusions  
This article focused on predicting the soil organic matter content using soil samples from the Xianshan Lake wetland in Zhejiang Province. The actual organic matter content of the samples was measured using chemical methods, and the spectral data of the samples was obtained using a laboratory spectrometer. After preprocessing the spectral data using eight spectral differentiation transformation methods, the SCARS algorithm was applied to select the characteristic bands for feature extraction. Five inversion models, including MLR,PLSR, SVR, RF, and MPARF, were constructed. By comparing the simulation accuracy and stability of these models, this study aimed to determine the optimal combination of spectral transformation methods and models for predicting the soil organic matter content. The main conclusions are as follows:  
(1) The optimization of the Random Forest (RF) model using the Marine Predators Algorithm (MPA) effectively improves the performance of the RF model, enhancing the accuracy and generalization ability of regression predictions. The MPA allows for global search in the solution space, maintaining diversity and exploration capabilities. It possesses adaptability and flexibility, enabling it to find optimal parameter configurations for the RF model and improve regression predictions. The predictive accuracy of the model is significantly enhanced, demonstrating high stability and accuracy. SD-MPARF exhibits accuracy comparable to the chemical methods, with the highest R\({}^{2}\) value (0.92) and the lowest RMSE (1.27 g/kg).  
(2) The soil organic matter content in the study area ranges from 3.68 to 19.40 g/kg, with an average of 9.77 g/kg, indicating a relatively low organic matter content. As a result, the correlation coefficient between the high-spectral data and organic matter content is relatively low.  
(3) The correlation coefficients between the original spectral reflectance and the soil organic matter content significantly increase after various spectral differentiation transformations. The correlation coefficient improves from 0.1 for the original spectral data to 0.55, indicating that different forms of differentiation transformations highlight detailed information and local variations in the spectra while weakening the noise and background signals, thereby enhancing the discriminability of the spectral features.  
(4) The feature bands selected by the SCARS algorithm account for less than 14.29% of the full spectrum. This algorithm dynamically adjusts feature weights and selects feature bands with significant discriminatory power, greatly reducing redundant information in the spectral data and improving the modeling speed.  
Of course, this paper also has some limitations. For example, Xianshan Lake may not represent wetland parks in the entire Zhejiang region. In the future, we plan to collect data from more diverse wetland parks in different regions to conduct a more comprehensive study. Additionally, the machine learning algorithms used in this paper, while classic, are relatively older. In the future, we intend to combine more advanced and comprehensive regression models to build more robust inversion models.  
Conceptualization, L.J. and G.G.; methodology, L.J. and M.Z.; validation, W.Z. and F.Y.; formal analysis, G.G.; investigation, M.Z.; data curation, F.Y.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z. and L.J.; supervision, L.J.; project administration, L.G.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Huzhou Key R&D Merit Commissioned Project (2021 ZD2003), the Natural Science Foundation of Zhejiang Province Public Welfare Project (LGF22C160002), and the Huzhou Public Welfare Project (2022 GZ36).  
Not applicable.  
The authors declare no conflict of interest.