Application of the Process‐Oriented and Machine Learning Combined Model in Mapping of Soil Organic Carbon of Non‐Waterlogged Mineral Soils at National Scale

Integrating process‐oriented (PO) and machine learning (ML) models is effective for obtaining dynamic spatial information on soil organic carbon (SOC) stocks. However, PO‐ML integration, particularly at large scales, has received insufficient attention. This gap limits our understanding of and predictive capabilities regarding SOC dynamics. To explore the adaptability and effectiveness of PO‐ML integration on a large scale, we constructed a national‐scale PO‐ML hybrid model. Due to the uncertainty in estimating natural land cover types, we used the PO model to expand the time series of nationwide non‐waterlogged mineral soil natural land cover SOC density data (excluding wetland and croplands) for the period 2000–2014 to enhance the ML model training data and predict the spatial distribution of the average SOC content during this period. The results indicated that the ML model's accuracy (R2 = 0.57) aligned with the average level reported by other digital soil mapping (DSM) studies, whereas the accuracy of the PO‐ML hybrid model was approximately 17% above the highest accuracy reported in other DSM studies. This improvement highlights the advancement our research contributes to the field. Furthermore, the study demonstrates the important role of dynamic environmental covariates in predicting SOC density by showing that they significantly enhanced the model's ability to capture the spatiotemporal dynamics of SOC. Moreover, in the absence of Rothamsted carbon model simulation data, the ML model exhibited higher uncertainty in the sample‐scarce western regions and around the latitudes of 30° N–40° N, whereas the PO‐ML model effectively reduced this uncertainty. These findings indicate that the hybrid model strategy offers significant advantages in SOC simulation and provides important insights into the nationwide spatiotemporal distribution of SOC in non‐waterlogged mineral soils' natural land cover.