From Global to Local: Precision Carbon Flux Mapping for Natural Climate Solutions

Accurate monitoring of terrestrial CO₂ uptake is essential for Natural Climate Solutions and reducing carbon accounting uncertainty. Project-scale certification protocols can provide robust estimates but often depend on costly site-level measurements and are difficult to scale. Global carbon flux models provide continuous coverage, but their coarse resolution cannot represent heterogeneous land management. Bridging these approaches requires high-resolution, scalable carbon monitoring with transparent uncertainty estimates.

Within the BenchFlux project, we present initial results from a High-Resolution Carbon Flux Monitoring framework based on the Global Estimation Model (CARBON-GEM). CARBON-GEM integrates (i) surface reflectances from the HISTARFM data-fusion approach (Moreno-Martinez et al., 2020), (ii) meteorological drivers, and (iii) eddy-covariance (EC) observations to estimate both gross primary production (GPP) and net ecosystem exchange (NEE) daily at 30 m resolution. The approach utilizes machine-learning methods, such as neural networks, to capture nonlinear responses, and is implemented in Google Earth Engine for scalable mapping. The workflow also delivers pixel-level uncertainty quantification, moving beyond categorical quality flags to support auditability and interpretation.

In addition to standard out-of-sample cross-validation to assess robustness and generalization, we validate CARBON-GEM against independent, scale-aware FluxMapper ground truth (Metzger, S., 2018) provided by BenchFlux’s SpatialEddy component. FluxMapper couples next-generation EC processing with flux spatialization to enable explicit space-time matching and local-to-regional nesting. In this context, CARBON-GEM extends the FluxMapper-scale structure beyond individual stations, allowing continuity across diverse landscapes. Complementing this, FluxMapper provides a novel, independent benchmark for high-resolution carbon-flux estimates and serves as a robust reference point, alleviating standard EC spatial-resolution constraints and facilitating the decomposition of aggregate point measurements into fine-grained spatial patterns. CARBON-GEM and FluxMapper together establish a foundation for scalable, uncertainty-aware 30-meter monitoring of GPP and NEE. This approach captures essential spatial heterogeneity necessary for large-scale real-world auditing in NCS planning, reporting, and verification.

• Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high-resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.
• Metzger, S. (2018). Surface-atmosphere exchange in a box: Making the control volume a suitable representation for in-situ observations. Agricultural and Forest Meteorology, 255, 68-80.