## 4 Discussion
### Superiority of Time Series NDVI Approach
#### 4.2.2 Effect of Terrain Factors on SOC Prediction  
Digital mapping has become a crucial and popular method to investigate the spatial distribution of soil properties, which provide substantial details. Presently, numerous studies have focused on exploring a feasible and reliable method to acquire spatial SOC information [24; 53; 98]. However, all these methods have considered topography as a necessary influential factor. In general, terrain attributes are pivotal factors that affect the SOC stock in areas with complex and varied topography, such as mountainous regions [98; 99]. However, in plains or small-scaled areas, the spatial distribution of terrain is flat and has indistinctive variation. As previously mentioned, the correlation between topographic parameters (surface roughness, DEM, slope, and TPI) and SOC was low (0.014, 0.129, 0.021, and 0.044). The four terrain variables were used as predictors to model ANN and SVM to prove that topographic factors had small effect on SOC prediction in this plain area. However, the best modeling methods failed to produce good results (Table 11). Hence, the effect of topography for SOC was negligible in this small-scale plain area. Similar conclusions were reported by [PERSON] et al. [100], who regarded that local terrain attributes played less important role than other predictors at a small scale. [PERSON] et al. [101] concluded that terrain attributes can capture large-scale influences of soil transport but not those occurring at a specific point. Thus, the overuse of terrain factors in small-scale plain areas reduces the prediction accuracy and increases the calculation complexity. In summary, in plains and gentle undulating terrains, easy-to-measure environmental factors, such as topography (elevation and slope), do not co-vary with soils that have spatial variations that are difficult to effectively reflect [29; 102]. [PERSON] et al. [29] evaluated the change patterns (dynamic \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Predictors**} & \multicolumn{2}{c}{**Modeling Accuracy**} & \multicolumn{2}{c}{**Prediction Accuracy**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline Subset1(summer) for ANN: NDVI\({}_{\text{Apr}}\). NDVI\({}_{\text{May}}\) & 4.818 & 0.269 & 4.119 & 0.281 \\ NDVI\({}_{\text{Jun}}\). NDVI\({}_{\text{Jul}}\). NDVI\({}_{\text{Aug}}\). NDVI\({}_{\text{Sept.}}\) & & & \\ Subset2 (winter) for ANN: NDVI\({}_{\text{Jan}}\). NDVI\({}_{\text{Feb.}}\) & 4.855 & 0.141 & 4.980 & 0.137 \\ NDVI\({}_{\text{Mar}}\). NDVI\({}_{\text{Oct}}\). NDVI\({}_{\text{Nov}}\). NDVI\({}_{\text{Dec.}}\) & & & \\ Subset1 (summer) for SVM: NDVI\({}_{\text{Apr}}\). NDVI\({}_{\text{May}}\) & 4.158 & 0.477 & 3.863 & 0.343 \\ NDVI\({}_{\text{Jun}}\). NDVI\({}_{\text{Jul}}\). NDVI\({}_{\text{Sep.}}\) & & & \\ Subset2 (winter) for SVM: NDVI\({}_{\text{Jan}}\). NDVI\({}_{\text{Feb.}}\) & 4.575 & 0.237 & 4.908 & 0.162 \\ NDVI\({}_{\text{Mar}}\). NDVI\({}_{\text{Oct}}\). NDVI\({}_{\text{Nov}}\). NDVI\({}_{\text{Dec.}}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Two short subtime series predictors for model analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Modeling Accuracy**} & \multicolumn{2}{c}{**Prediction Accuracy**} \\ \cline{2-5}  & **RMSE** & **R\({}^{2}\)** & **RMSE** & **R\({}^{2}\)** \\ \hline ANN & 5.580 & 0.019 & 4.829 & 0.027 \\ SVM & 5.199 & 0.021 & 5.305 & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: SOC with respect to topographic variation (digital elevation, slope, surface roughness, and topographic position index (TPI)) for ANN and SVM models. feedback patterns) of the land surface, such as those captured daily by remote sensing images during a short period (6-7 d) after a major rain event, which can be used to differentiate soil types. However, the main problem is that remote sensing images require a short round trip period of the satellite. Meanwhile, data collection is susceptible to cloud, snow, and rain.