## 4 Results
### Comparative Analysis of Soil Organic Matter Content Inversion Model Accuracy  
The sensitive bands selected by the SCARS algorithm were used as input variables for the inversion models, with the SOM content as the dependent variable. The MLR, PLSR, SVR, RF, and MPARF methods were used to construct the soil organic matter content prediction models. The experimental results are shown in Table 5. From Table 5, it can be observed that there are differences in the prediction results among different spectral transformation methods. However, considering the overall evaluation criteria, both for the training set and the testing set, the experimental results of the spectral data after undergoing the eight spectral transformations are superior to those of the original spectra without any transformation. Furthermore, among the five prediction models, MPARF demonstrates better experimental results and stability compared to the other four models, indicating its ability to predict the soil organic matter content in the study area more effectively. Among all the combinations of spectral transformations and inversion models, the SD-MPARF and RLSD-MPARF models performed well on both the training and testing sets, while the N-RF and N-SVR models yielded poorer results. This could be due to the presence of noise in the original spectra or the limited extraction of informative spectral bands. Overall, the MPARF model demonstrated high stability and fitting performance on both the training and testing sets. The MLR and PLSR models showed high stability but had inferior predictive results compared to MPARF. The SVR model and the unoptimized RF model exhibited highly unstable performance. The overall estimation performance of the five models, ranked from high to low, was MPARF \(>\) PLSR \(>\) MLR \(>\) SVR \(>\) RF. This could be attributed to the high linear relationship between the soil types and organic matter content in the study area, resulting in the linear models performing better than the non-linear models. The poor performance of the RF model may be attributed to a low number of features or low correlation between the features and variables. Due to its adaptability, the MPA can adjust its search strategy based on the problem's characteristics and data features, allowing for it to be flexibly applied to different regression prediction problems and find the optimal configuration for the RF model. Considering each individual model, various spectral transformation combinations showed good fitting performances. The MLR model based on LRSD spectral transformation exhibited good prediction results, with a training set R\({}^{2}\) value of 0.82, a testing set R\({}^{2}\) value of 0.72, and a mean RPD of 2.11. The PLSR model based on SD spectral transformation showed good prediction performance, with a training set R\({}^{2}\) value of 0.88, a testing set R\({}^{2}\) value of 0.65, and a mean RPD of 2.40. The SVR model based on RLSD spectral transformation demonstrated good prediction performance, with a training set R\({}^{2}\) value of 0.81, a testing set R\({}^{2}\) value of 0.66, and a mean RPD of 2.02. The RF model based on RSD spectral transformation yielded good prediction results, with a training set R\({}^{2}\) value of 0.80, a testing set R\({}^{2}\) value of 0.70, and a mean RPD of 2.03. The MPARF models based on various differential transformations all outperformed the prediction of untreated spectral data, exhibiting higher stability and accuracy than the other models, with training and testing set R\({}^{2}\) values exceeding 0.85. Based on the results of the eight spectral transformations, the MPARF, RSD-SVR, RSD-PLSR, RLFD-RF, RLFD-PLSR, and MPA-RF models exhibited testing set RPD values above 2.0, indicating strong reliability and more trustworthy prediction results. On the other hand, the N-SVR, N-RF, SD-SVR, and SD-RF models had testing set RPD values below 1.4,suggesting lower reliability of the models established using these methods.