Robust quantification of soil organic carbon (SOC) change and greenhouse gas (GHG) fluxes on managed lands is central to emerging decision support tools and MRV systems, but remains limited by sparse observations, uneven driver data, scaling challenges, and uncertainty in both measurements and models. This session focuses on novel approaches and next-generation integrative methods that fuse in situ observations, proximal and remote sensing, process-based models, AI-enabled approaches, and hybrid inference systems to address these limitations and produce more credible, scalable, and decision-relevant estimates. Emphasis is placed on approaches that enable rigorous uncertainty accounting, support rapid scaling, or maintain flexibility across data regimes and accounting objectives. We invite contributions on novel and integrated approaches, benchmark datasets, validation and intercomparison, uncertainty propagation, bias detection, data assimilation, and operational accounting workflows for croplands and grazing lands.

• B - Biogeosciences
• SY - Science and Society