Yang, L; He, XL; Shen, FX; Zhou, CH; Zhu, AX; Gao, BB; Chen, ZY; Li, MC (2020). Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data. SOIL & TILLAGE RESEARCH, 196, 104465.

Mapping the spatial distribution of soil organic carbon (SOC) content or stock is important for climate change studies and land management decisions. When using environmental covariates to map SOC content or stock, variables indicating human activities have drawn growing attentions. Crop species/crop rotations and agricultural management significantly affect the spatial variation of SOC in croplands. For areas where climatic conditions and farming managements are generally consistent in cultivation territory of one crop species, crop phenology largely indicates the crop response to soil. Therefore, phenological parameters incorporating with crop rotation could be effective for mapping soil organic carbon in these areas. In this study, we extracted phenological parameters from Normalized Difference Vegetation Index (NDVI) time series data, and used these variables with crop rotation for predicting topsoil organic carbon content in a cropland area in Anhui province, China. Forty-nine sampling points were collected in field. For these points, there were three crop rotations each with two crop species per year. Twenty-two HJ-1 A/B images for 2010 year with a 30 m resolution were obtained. Eleven phenological parameters for each of the two growing seasons were obtained with a dynamic threshold method. Various combinations of predictive variables were developed based on variable importance and experimented for predicting topsoil organic carbon using random forest. The prediction results were validated using a cross validation approach. Results showed that base levels (given as the average of the left and right minimum values of a time series profile) for both seasons were the most important predictors in this area. Adding both crop rotation and the two phenological parameters to the natural environment variables increased the prediction accuracies by 50% in terms of R-2 and 13.4% in terms of root mean square error (RMSE). This study demonstrates the effectiveness of crop phenology in mapping SOC in croplands.