A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling

• Novel approach generates pseudo revisited soil samples using environmental similarity. • Provide revisited samples for geographical spatiotemporal analysis. • Pseudo SOC stocks improve RothC model accuracy for SOC dynamics prediction. • Bayesian optimization enhances microbial parameter calibration in RothC model. • Approach outperforms spatiotemporal Random Forest in SOC change modeling. Soil Organic Carbon (SOC) is vital for the global carbon cycle, agricultural sustainability, and climate change. Process-based models like Rothamsted carbon model (RothC) simulate SOC dynamics, but their accuracy relies on revisited soil samples for calibration, which are often scarce, especially in environmentally heterogeneous regions. This study proposes a novel approach to generate pseudo revisited samples using environmental similarity, addressing the lack of revisited samples. For each intervening-year sample, pseudo SOC stocks in unsampled years were constructed by calculating environmental similarity with existing samples and applying weighted averaging. These pseudo SOC stocks served as revisited samples for model calibration. Bayesian optimization was used to adjust RothC’s microbial activity parameters. Validation results showed the approach significantly improved predictive accuracy, with an RMSE of 5.28 t/ha (31.6 % lower than global parameter optimization and 10.7 % lower than spatiotemporal Random Forest) and an R2 improved from 0.319 (spatiotemporal Random Forest) to 0.456. The approach enhances SOC model accuracy by leveraging environmental similarity and parameter optimization, offering a practical solution for regions lacking revisited samples and improving long-term SOC dynamics simulations. This approach not only addresses data scarcity but also provides more reliable predictions for climate and agricultural management.