Improving digital mapping of soil organic matter in cropland by incorporating crop rotation

Abstract

Despite human activities are key influencing factors for cropland soil organic matter (SOM), detailed characterization of human activities has always been limited in the digital mapping of SOM due to the lack of proper representations of human’s cropland use activities. Crop rotation is an essential human agricultural practice significantly affecting the spatial–temporal variations of SOM due to the periodically dynamic changes of crops. Thus, incorporating crop rotation in the digital soil mapping holds high potential for improving SOM prediction. Here, we applied time-series radar Sentinel-1 and optical Sentinel-2 to map crop rotation systems by a hierarchical rule-based method. Then we explored the effectiveness of incorporating such information in predicting SOM by implementing various combinations of predictive variables. We chose a typical multiple cropping region with various crop rotations in southern China. The model performance was evaluated by 10-fold cross-validation. Results showed significant differences in SOM among the crop rotation systems, and the single rice rotated with vegetables has the highest SOM followed by the high-diversity vegetables and long-term orchard systems. Adding crop rotation enhanced the predictability of SOM with a decrease in RMSE by 7% and an increase in R2 by 24%. Furthermore, the crop rotation systems appeared more important in the predictive models than the soil, topographic, and climatic variables. Our results demonstrated the effectiveness of including crop rotation in predicting SOM over complex agricultural landscapes. Our study indicated that human activities should be characterized more detailedly in cropland soil mapping, and that crop rotation containing information on the seasonal dynamics of cropland may be an option for such characterization.

Keywords

• Digital soil mapping
• Sentinel-1/2
• Crop rotation
• Google Earth Engine
• Variable selection