## 4 Discussion  
### Comparisons of Model Performance in SOC Prediction  
In this study, NDVI time series data were used to predict the SOC content in Honghu City via five approaches (SLR, OK, PLSR, SVM, and ANN). In summary, all these methods explained the spatial variance of SOC. Among these methods, ANN showed the optimal capacity to estimate the spatial distribution of SOC by providing sufficient useful information and decreasing redundant data. Similar conclusions were reported by [PERSON] et al. (2014), who mapped SOC lateral and vertical variances using data mining techniques (ANN, SVM, k-nearest neighbor, random forests, regression tree models, and genetic programming) and found that ANN is the best method to predict the SOC content. Meanwhile, the performance of SVM models to predict and map SOC was second to that of ANN. In reality, the SVM method was a feasible means for regressing and forecasting in agriculture, hydrology, meteorology, and environmental research ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018). Furthermore, [PERSON] et al. (2016) confirmed the close performance of ANN and SVM models for spatially predicting and mapping the pattern of SOC stocks. The authors proposed that SVM and ANN methods should be calibrated, and the best result should be applied for the spatial prediction of target soil properties. Thus, the ANN model with the lowest \(\text{RMSE}_{\text{P}}\) (3.718) and highest \(\text{R}^{2}_{\text{P}}\) (0.391) was the best method for SOC prediction in this study. By contrast, the PLSR model performed slightly worse because it was based on traditional linear regression although the information of SOC was not. [PERSON] et al. (2018) demonstrated that the ANN model performs better compared with PLSR, which may be attributed to a nonlinear behavior documented for SOC ([PERSON] et al., 2018; [PERSON] et al., 2018) and appeared to be overcome by the nature of ANN in solving nonlinear problems. With regard to the OK model, some high values were underestimated, and some lower values were overestimated because of the filtration of partial information and reservation of the whole tendency ([PERSON] et al., 2018). In particular, OK exhibited a smoothing effect, which removed the maximum and minimum values of the original data. Meanwhile, the OK model mapped the spatial distribution of SOC in the study region with the soil samples and cannot be extended to neighboring regions. Thus, the application of the OK method was limited ([PERSON] et al., 2018). [PERSON] (2018) summarized that OK interpolation is mainly used in field scales or some large areas with relatively consistent management measures. However, the prediction effect of OK interpolation on the spatial differentiation of SOC is unsatisfactory in some areas with complex terrain and land use. The SLR model performed the worst, with the lowest \(\text{R}^{2}_{\text{P}}\) (0.281) and slightly higher \(\text{RMSE}_{\text{P}}\) (3.930). This finding can be explained by the collinearity among the predictors. The existence of multicollinearity between environmental predictors might be inevitable because soil formation is determined by the interaction of various soil-forming factors, leading to the weak stability and poor prediction ability of the established soil attribute prediction model ([PERSON] et al., 2018). In the SLR model, the collinearity problem was evaluated through multicollinearity diagnosis and computation of variance inflation factor (VIF), as shown in Tables 7 and 8. In principle, VIF \(>\) 10 indicates a collinearity problem ([PERSON] et al., 2018). Although the VIF values showed no evident collinearity (Table 1), the lowest eigenvalue was 0.01, and the highest condition index was 27.10, indicating a slight collinearity problem with SLR, as shown in in Table 7. Thus, the stability of simple multiple linear regression model was poor. These conclusions were supported by [PERSON] et al. (2018), who summarized the underperforming result of SLR models.