Land carbon sinks offset roughly one-third of anthropogenic CO₂ emissions, yet major uncertainties remain in their magnitude, variability, and underlying driverss. Researchers use top-down approaches (e.g., atmospheric inversions) and bottom-up models (e.g., dynamic global vegetation models) to estimate land carbon fluxes. Top-down models constrain net fluxes at broad scales but struggle to partition flux components  at finer resolutions. Bottom-up models represent process-level mechanisms but often suffer from structural and scaling biases. Consequently, spatial and temporal discrepancies persist between these two approaches. These inconsistencies stem from limited long-term observations in tropical regions, incompletely characterized ecosystem responses (e.g., to climate change, vegetation mortality, and carbon allocation), and insufficient representation of key processes (e.g., disturbance-induced or anthropogenic degradation). We invite contributions that integrate in-situ observations, remote sensing, and innovative modeling to reduce these uncertainties, reconcile divergent estimates, and improve robust assessments of land carbon sinks