Spatiotemporal modelling of soil organic carbon: integrating process-based and machine learning approaches

Soil organic carbon (SOC) underpins the global carbon cycle and represents a central lever for climate change mitigation and food security. Yet its accurate spatiotemporal quantification remains a challenge, owing to the complex interactions among biological, chemical and physical processes operating across scales. This review critically evaluates the current state of SOC spatiotemporal modelling frameworks and their limitations, and future directions. Process-based models provide mechanistic insight into carbon dynamics but are constrained by parameterisation, structural assumptions and computational demands. In contrast, machine-learning (ML) approaches excel at capturing spatial patterns from large datasets but often struggle to represent temporal kinetics, enforce physical consistency or generalise across scales and environmental contexts. We argue that the next generation of SOC modelling will emerge from the convergence of process-based understanding and data-driven inference through knowledge-guided ML and hybrid modelling strategies. We synthesise four principal integration pathways: (1) meta -modelling to accelerate computationally intensive simulations; (2) sequential hybridisation to embed mechanistic trends as dynamic covariates; (3) ensemble frameworks to reduce structural uncertainty; and (4) data assimilation to constrain model trajectories with observations. We further highlight emerging frontiers, including residual and parameter learning, physics-informed neural networks, foundation models for earth observations, and omics-informed frameworks that link microbial functional potential to carbon turnover processes. We conclude that progress toward causally interpretable, uncertainty-aware monitoring frameworks will require tighter integration of mechanistic theory, interpretable artificial intelligence and cross-scale data synthesis.