## 4 Discussion  
This study analyzed soil samples from the state-owned Yachang and Huangmian Forest Farms in Guangxi to determine the effectiveness of the SG-1 DR preprocessing  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Modeling Methods**} & \multicolumn{6}{c}{**Training Set**} & \multicolumn{6}{c}{**Validation Set**} \\ \cline{2-13}  & **RMSE** & **R\({}^{2}\)** & **MAE** & **MAE** & **MBE** & **RPIQ** & **RMSE** & **R\({}^{2}\)** & **MAE** & **MAE** & **MBE** & **RPIQ** \\ \hline CNN & 2.148 & 0.976 & 1.605 & 0.073 & 0.073 & 1.776 & 5.064 & 0.807 & 3.970 & 0.177 & 0.229 & 4.996 \\ SVM & 3.505 & 0.937 & 0.713 & 0.079 & 0.005 & 0.655 & 5.165 & 0.797 & 4.391 & 0.183 & 0.469 & 4.255 \\ CNN-SVM & 2.307 & 0.973 & 1.327 & 0.062 & 0.305 & 0.350 & 4.641 & 0.838 & 3.741 & 0.178 & 1.372 & 3.752 \\ BRA-CNN & 1.756 & 0.984 & 1.279 & 0.069 & 0.347 & 0.366 & 4.744 & 0.831 & 3.475 & 0.163 & 1.036 & 2.312 \\ BRA-CNN-SVM & 1.609 & 0.987 & 1.287 & 0.066 & 0.348 & 0.836 & 3.042 & 0.930 & 2.254 & 0.100 & 0.890 & 1.436 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Modeling results based on SG-1 DR preprocessed data.  
Figure 10: Scatter plots of predicted and measured SOM content.  
method in the experimental area. It also constructed a CNN-SVM model optimized by the Black-winged Kite Algorithm for SOM prediction. This achievement provides an important reference for SOM determination in this area and offers a new method for forest soil monitoring.  
In terms of preprocessing, due to different natural conditions, the research results only represent the optimal preprocessing effect in the experimental area and provide a reference method for similar regions. Secondly, although a wide variety of mathematical transformations and models were used in the experiment, it is still possible that not all potentially effective methods were covered, leaving room for the discovery of better combinations. Due to the high difficulty of sample collection, most researchers used a limited number of samples from the experimental area [56; 57]. Although it is not yet concluded whether sample size is a key factor affecting experimental results [58], with sufficient sample size, it is possible to try using multivariate feature selection [59; 60] and the method of concatenating original and preprocessed bands to form a new dataset [61]. More experiments can explore additional possibilities for the optimal preprocessing methods for the experimental area data.  
Regarding predictive models, the results show that CNN can effectively extract rich feature information from spectral data, and SVM can handle the emergence of multicollinearity issues contained in the data processed by CNN. Currently, many researchers use traditional machine learning methods alone, which may struggle to extract effective spectral features when faced with redundant soil spectral data in the experimental area [30; 62]. The complexity and high dimensionality of such data often require more advanced and sophisticated methods, such as the combination of CNN and SVM used in this study or the establishment of more diverse model structures like stacked machine-learning methods [45]. Additionally, some researchers have deployed predictive models in remote sensing to achieve real-time SOM content prediction [63; 64]. This approach has great potential as it allows for large-scale and continuous monitoring of soil conditions, providing valuable information for timely decision-making in agriculture and forestry. There are also studies that combine remote sensing images with field measurement data for model training, yielding good results [65]. Combining spectral data with image data can improve the model's generalization ability, but due to the differences in the forms of these two types of data, feature extraction may not be optimal, requiring further experiments tailored to the specific conditions of the experimental area [66].