## 3 Results
### Digital Mapping of SOC  
The spatial outputs of digital SOC maps were mapped with extracted cultivated land via the aforementioned models (Figure 11). In general, the maps were relatively similar and showed strong spatial variation of SOC. High predicted SOC values occurred in the center, western, and northwestern parts of the study area, where the land was mainly covered by rice, lotus root, and oilseed rape (_Brassica napus_ L.) with dense vegetation coverage. The high SOC contents in these parts of the research area could be explained by the long-term benign cultivation. Thereafter, low values were observed in the southern and eastern parts of the maps close to Yangtze River possibly because of the loss of soil nutrients in this area.  
However, some evident differences were observed in the local details among the five methods. This finding was highly significant for the differences of SOC concentrations in the eastern part of this region. Figure 11d depicts that the predicted SOC content (0.5-3.5 g kg\({}^{-1}\)) of the OK model was generally lower compared with the other maps with higher SOC values (3.5-10 g kg\({}^{-1}\)) in the eastern part of the study area. As shown in the result of OK model (Figure 11d), the SOC content obtained by OK method was approximately low (the red part was small). However, from the perspective of cartographic quality, the OK model did not accurately show the local details of SOC content distribution  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Modeling Accuracy**} & \multicolumn{2}{c}{**Prediction Accuracy**} \\ \cline{2-5}  & **RMSE** & \(\mathbf{R^{2}}\) & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline Stepwise Linear Regression (SLR) & 4.863 & 0.270 & 3.930 & 0.281 \\ Ordinary Kriging (OK) & 3.549 & 0.524 & 3.727 & 0.372 \\ Partial Least Squares Regression (PLSR) & 4.970 & 0.230 & 4.087 & 0.283 \\ Support Vector Machine (SVM) & 4.269 & 0.453 & 3.753 & 0.361 \\ Artificial Neural Network (ANN) & 4.326 & 0.417 & 3.718 & 0.391 \\ \hline \end{tabular} Notes: RMSE: root mean square error; \(\mathbf{R^{2}}\): coefficient of determination.  
\end{table}
Table 6: Accuracy assessment of prediction models.  
Figure 10: Scatter plot of measured and predicted SOC (soil organic carbon) values of the validation dataset by five models. Note: the red lines show 1:1 relationship and do not fit the data. (**a**): OK, (**b**): SLR, (**c**): PLSR, (**d**): SVM and (**e**): ANN.  
compared with other methods that produced many detailed patterns. This finding was because of the insufficient soil samples and the smoothing effect of OK interpolation. Thus, OK was unfit for predicting object values beyond the study region. The maps generated by the SLR and PLSR models are approximated in Figure 11a,e, which highlight the high values in the center of maps. Compared with the SLR method, the map of PLSR more strongly manifested low SOC values in the southern part of the study area. Moreover, the map obtained by SVM resembled that of the ANN model (0 however, the map acquired by SVM tended more toward the middle and low values compared with that of the ANN model, which mostly presented middle and high values with lesser low values).  
In summary, the SOC content from east to west of the study area showed the distribution ranging from low to high (0.3-40 g kg\({}^{-1}\)). The finding verified that the spatial distribution of SOC content can be digitally mapped through the five models as expected. The NDVI time series data were closely related to the SOC content, which can be considered an indicator for spatial prediction.  
Figure 11: Spatial distribution of predicted SOC (soil organic carbon) content by (**a**) PLSR (Partial Least Squares Regression), (**b**) SVM (Support Vector Machine), (**c**) ANN (Artificial Neural Network), (**d**) OK (Ordinary Kriging), and (**e**) SLR (Stepwise Linear Regression).