## 5 Conclusions  
Traditional environmental factors, such as terrain, landscapes, climatic, and vegetation types, exhibit similar spatial distribution characteristics in plain or flat areas. These factors cannot effectively reflect the spatial variation of soil properties. Thus, NDVI time series data were used as the auxiliary variables for digital soil mapping using five methods, namely, OK, SLR, PLSR, SVM, and ANN. 1. The results demonstrated that NDVI time series was correlated with SOC stock. A significant positive correlation was observed between the SOC content and NDVI in July and August. By contrast, a significant negative correlation was observed between the SOC content and NDVI in January, February, March, April, November, and December. This finding was attributed to the cultivation of farming work and the phenophase of the crops that influenced the land surface vegetation landscapes. 2. The comparison result of different methods showed that ANN was the overall best method with the lowest RMSE\({}_{\text{P}}\) of 3.718 and highest R\({}^{2}\)P of 0.391, followed by SVM (RMSE\({}_{\text{P}}\) = 3.753, R\({}^{2}\)P = 0.361), OK (RMSE\({}_{\text{P}}\) = 3.727, R\({}^{2}\)P of 0.372), PLSR (RMSE\({}_{\text{P}}\) = 4.087, R\({}^{2}\)P = 0.283), and SLR (RMSE\({}_{\text{P}}\) = 3.930, R\({}^{2}\)P = 0.281). Thus, ANN was the optimal model to predict SOC using the NDVI time series. 3. The SOC maps estimated by the five models were similar. The SOC content from east to west of the study area showed distribution ranging from low to high (0.3-40 g kg\({}^{-1}\)). However, the local details clearly indicated that OK interpolation smoothed the result, and the maps generated by the SLR and PLSR models highlighted high values in the center of the maps. Moreover, 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. These results confirmed that the spatial distribution of the SOC content can be digitally mapped through NDVI time series. 4. The prediction of SOC using single-data NDVI showed unsatisfactory accuracy, indicating the unpredictability of single-data NDVI compared with multi-time NDVI. The prediction results of two short NDVI time series, which were correlated to summer and winter crop production, respectively, manifested that short NDVI time series can be used for SOC prediction to some extent, but its prediction accuracy was lower than that of long time series. In addition, the correlation between topographic parameters and SOC was low. The terrain variables used as predictors in the model failed to produce good results. Hence, the effect of topography for SOC was negligible in this small-scale plain area. Finally, this study provided an example for efficiently mapping regional SOC to overcome the selection of auxiliary variables in plains. The NDVI time series data exhibited huge potential in predicting SOC, and ANN extracted substantial useful information for digital soil mapping. Thus, the approach that combines the time series characteristics of multiple important auxiliary variables via ANN should be investigated and used in other areas for other soil properties. An enhanced understanding of the spatial distribution of soil properties facilitates soil management and accelerates agricultural production and ecological planning. Conceptualization, G.L. ; Data curation, Y.Z. ; Formal analysis, Y.Z. ; Funding acquisition, H.Z. ; Investigation, M.L. ; Methodology, G.L. ; Project administration, G.L. ; Resources, Y.C. ; Software, S.W. ; Supervision, Q.J. ; Validation, T.S. ; Writing--original draft, Y.Z. ; Writing--review & editing, G.L. This research was funded by the National Natural Science Foundation of China, grant number (41471179, 41371227); the Basic Research Program of Shenzhen Science and Technology Innovation Committee (No. JCYJ20170302144323219); the Natural Science Foundation of Hubei, grant number (2018 CFB372); the National Key R&D Program of China (No.