## 3 Results  
### Basic Statistics of SOC and NDVIs  
The SOC content of the samples collected in this study ranged from 0.32 g kg\({}^{-1}\) to 33.95 g kg\({}^{-1}\) of the topsoil (0-30 cm) with a mean value of 6.24 g kg\({}^{-1}\). Table 2 shows that the mean values of the total calibration and validation datasets were 6.24, 6.72, and 6.00 g kg\({}^{-1}\), with standard deviations (SDs) of 5.02, 5.64, and 4.70, respectively. The basic statistics of calibration and validation datasets were similar to those of the entire dataset. This finding indicated that the calibration and validation datasets can effectively represent the entire dataset. The calibration dataset presented a wider range (33.51 g kg\({}^{-1}\)) and larger minimum (0.44 g kg\({}^{-1}\)) and maximum (33.95 g kg\({}^{-1}\)) values compared with the validation dataset (25.26, 0.33, and 25.59 g kg\({}^{-1}\), respectively). Thus, the SOC values of the validation dataset were entirely included in the calibration dataset to ensure prediction authenticity. SD indicated that the  
Figure 4: Artificial neural network (ANN) schematic. SOC values were generally stable (fluctuated at approximately 5.50), which were contrary to the results showed by the coefficient variation (CV). CV was adopted to illustrate the SOC variability. For soil properties, variability ranking was assigned on the basis of the classification proposed by [PERSON] (2016), where CV \(<\) 15% is the least variable, 15% \(<\) CV \(<\) 35% is the moderate variable, and CV \(>\) 35% is the most variable. The CV totality, points in croplands, calibration, and validation of SOC were 80.44%, 82.27%, 83.93%, and 78.33%, respectively, which exhibited strong variability. The skewness and kurtosis of all datasets denoted that the SOC data had an approximately normal distribution. Thus, these data can be used to create models for SOC prediction in the study area. The spatial variation of terrain factors in Honghu City is shown in Figure 5, and their Pearson's correlation coefficients (\(r\)) with SOC are represented. These maps exhibited that topographic factors had weak spatial variation, and most of the study area had similarly low values. For instance, DEM ranged from \(-\)86 m to 154 m with a low \(r\) of 0.129, indicating a weak correlation with SOC. Similarly, the same spatial characteristics appeared in the rest of terrain factors (slope, surface roughness, and topographic position index (TPI)), and their Pearson's correlation coefficients were 0.021, 0.014, and 0.044, respectively, showing frail relationship with SOC. Thus, the effect of terrain factors for SOC in this low-relief area was insignificant, and the spatial variation of SOC cannot be obtained by the terrain parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Item** & **Num** & **Min** & **Max** & **Mean** & **Range** & **SD** & **CV (\%)** & **Skewness** & **Kurtosis** \\ \hline Totality & 787 & 0.32 & 33.95 & 6.24 & 33.63 & 5.02 & 80.44 & 2.25 & 5.42 \\ Points in croplands & 678 & 0.33 & 33.95 & 6.43 & 33.62 & 5.29 & 82.27 & 2.13 & 4.53 \\ Calibration dataset & 407 & 0.44 & 33.95 & 6.72 & 33.51 & 5.64 & 83.93 & 2.07 & 3.97 \\ Validation dataset & 271 & 0.33 & 25.59 & 6.00 & 25.26 & 4.70 & 78.33 & 2.14 & 5.17 \\ \hline \hline \end{tabular} Note: Min is minimum, Max is maximum, SD is standard deviation, and CV is coefficient variation.  
\end{table} 
Table 2: Basic statistics of SOC (soil organic carbon) (g kg\({}^{-1}\)) in totality, cultivated land, calibration, validation. Figure 5: Terrain factors ((**a**): DEM, (**b**): slope, (**c**): roughness and (**d**): TPI) of the study area and their Pearson’s correlation coefficients (\(r\)) with SOC (soil organic carbon). \(r\) is the Pearson’s correlation coefficients between terrain factors, and SOC, TPI is the topographic position index. The time series NDVI data of the soil samples from January to December in one year are appended as Figures 6 and 7.