## 5 Discussion  
Numerous studies have demonstrated that spectral transformations can reduce noise in the original spectral data, improve the discriminability of spectral data, and enhance data interpretability [25; 26]. In order to improve the predictive accuracy of the model, this study applied various forms of differential transformations to the original spectral data of the study area. The results show that differential transformations can enhance the correlation between spectral reflectance and the SOM content. The correlation between the original data and the SOM was less than 0.1, but after preprocessing, the correlation was increased to a maximum of 0.54. Individual differential transformation methods can highlight features such as slopes, peaks, and valleys in the spectrum, thereby increasing the correlation between the organic matter content and reflectance. Second-order differential transformation, compared to first-order differential transformation, can provide a better estimation of the soil organic matter content in the study area, which is consistent with the findings of [PERSON] et al. [27]. The combination of reciprocal logarithm and differential transformation can enhance the features of the spectral signal and reduce the influence of background interference and noise on the spectral data, which is in line with the results obtained by [PERSON] et al. [28].  
This study found that the correlation coefficient between the soil spectra and the organic matter content in the study area is less than 0.1. Therefore, it is challenging to select feature bands based on the original spectral data. Previous researchers have used methods such as correlation coefficient analysis [29] or variance analysis [30] to study the relationship between the soil properties and soil spectral reflectance, selecting bands with high correlation coefficients or high variance contributions as feature bands. Currently, many scholars use methods like successive projections algorithm, principal component analysis, or CARS for feature band selection. In this study, the SCARS algorithm was employed to extract feature bands. The research shows that the SCARS algorithm can remove redundant information from the original spectral data and reduce the number of bands. For different spectral transformation methods, the SCARS algorithm selected feature bands that accounted for less than 14.29% of the full spectrum, thereby addressing the issue of input variables in the model. In previous studies on the spectral inversion of the soil organic matter content, differential transformation has been a commonly used spectral preprocessing method, and SCARS has also been widely used for feature wavelength selection. PLSR, SVR, and RF are widely used inversion model algorithms. From the above five models, both linear models, MLR and PLSR, exhibit greater stability compared to the non-linear models, SVR and RF. This may be attributed to the relatively uniform distribution of the soil organic matter content in the study area and the presence of linear relationships. Among these models, the RF model shows the least stable prediction accuracy, with an accuracy of only 0.36 using the original spectra. After applying RFD transformation, the prediction accuracy increases to 0.80. This instability in the RF model could be due to its inherent randomness. However, by using optimization algorithms to adjust the hyperparameters of the RF model, its stability can be improved [31]. In contrast to RF, MPARF not only enhances the original RF model's performance but also exhibits improved stability. It is worth noting that our dataset was relatively small, and MPARF was validated on limited laboratory hyperspectral data, limiting its generalizability. Additionally, further investigations could apply this method to aerial hyperspectral data in the study area to demonstrate its broader applicability.