## 3 Results
### Prediction Results of Soil Organic Matter Content  
The experimental results are summarized in Table 5, where the GPTransCNN demonstrates the best performance in predicting SOM. To further explore the differences among the predictions of these models, scatter plots and violin plots were generated, as shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Min (g/kg)** & **Max (g/kg)** & **Mean (g/kg)** & **Standard Deviation (g/kg)** & **Kurtosis** & **Skewness** & **CV (\%)** \\ \hline SOM & 4.9 & 63.7 & 25.4820 & 8.0814 & 4.6163 & 0.8877 & 31.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of soil organic matter. Figure 7: Scatter plots and violin plots of soil organic matter in the test set. The predictions from the GPTransformer are relatively concentrated, with an R\({}^{2}\) of 0.3643, representing the weakest performance among the three models. Examination of the scatter plot reveals that its predicted values primarily cluster within the range of 15-30 g/kg, a pattern consistent with the distribution depicted in the violin plot, indicating a lack of sensitivity to extreme values. The spatial distribution of SOM is not only governed by regional-scale climate and topographic patterns but also influenced by local variations in microtopography, moisture redistribution, and vegetation inputs [52]. The GPTransformer effectively captures global spatial heterogeneity and the overall relationships among covariates, facilitating the understanding of large-scale SOM distribution trends, but its ability to extract local details is limited, which limits the prediction accuracy of the model in areas with strong spatial heterogeneity. The predictive performance of the 1D-CNN surpasses that of the GPTransformer, achieving an R\({}^{2}\) of 0.3765, which ranks it in the middle among the three models. Although the scatter plot reveals deviations between predicted and observed SOM values in the 0-25 g/kg and high-value ranges, the violin plot further indicates that the overall distribution of the predicted values more closely aligns with the measured data. This improvement is largely attributed to the use of small convolutional kernels, which enable the 1D-CNN to more effectively capture the influence of local environmental factors (such as terrain undulation, precipitation, temperature, etc.) on SOM [53]. As a result, the model is suited for handling SOM data with high spatial heterogeneity and small-scale variability, making it more effective in capturing the micro-scale patterns of the SOM distribution. GPTransCNN achieved the best predictive performance among the three models, with an R\({}^{2}\) of 0.4329, indicating the strongest fitting capability. While some discrepancies between predicted and observed values remain, the scatter plot demonstrates an improved overall fit. Moreover, the distribution of predicted SOM values in the violin plot most closely matches the observed distribution, further confirming the model's accuracy. These results suggest that GPTransCNN effectively integrates the large-scale spatial awareness of the GPTransformer with the local feature extraction capabilities of the 1D-CNN, thereby enhancing the model's ability to capture the spatial heterogeneity of SOM and improving predictive accuracy. As shown in Figure 7, all three models exhibit larger prediction errors in regions with higher SOM values. The main reason is the limited number of high-value samples in this range, which leads to insufficient training in these areas and subsequently affects the prediction accuracy. To further ensure the reliability and rigor of the experimental results, this study employed ten-fold cross-validation, a method that divides the dataset into ten equal parts. In each iteration, nine parts are used for training and the remaining one is used for testing. This process is repeated ten times so that each part is used once as the test set.