Use Case Description
Monitoring climate mitigation interventions and estimating emission reductions plays a critical role in understanding the effectiveness of climate mitigation efforts. To this aim, Earth Observation (EO) is broadly used in two ways: top-down and bottom-up. In the top-down approach, EO directly observes atmospheric concentrations and infers emissions or fluxes; in the bottom-up approach, EO monitors drivers, activities and stocks that feed into emission or removal estimates or inventories using IPCC methods. The two approaches are often combined where EO‑derived fluxes constrain models and inventories, while EO‑derived activity data strengthens or validates bottom‑up inventories.
The top-down approach can be further sub-divided based on two broad groups of sensors. The first group consists of global or regional GHG mappers – such as Orbiting Carbon Observatory-2 and -3 (OCO-2 and -3), Tropospheric Monitoring Instrument (TROPOMI), Greenhouse gases Observing Satellite-2 (GOSAT-2) – which provide large‑scale fluxes and hotspot identification for comparison with national inventories and Nationally Determined Contribution (NDC) trajectories. The second group includes high-resolution plume imagers – such as GHGSat, the Earth Surface Mineral Dust Source Investigation (EMIT), and Carbon Mapper – which help detect and quantify facility-scale emissions and monitor changes following interventions.
Forest monitoring is one example of the bottom-up approach. EO can track whether a physical intervention, such as afforestation or avoided deforestation, has taken place and how it evolves over time in terms of area, density, height, and condition. The effectiveness of the intervention on GHG emissions reduction can then be inferred using models such as biomass growth curves. At larger scales, aggregated EO-derived datasets on biomass, vegetation productivity, and land cover change are used to constrain and evaluate process-based models, including Dynamic Global Vegetation Models (DGVMs) and bookkeeping models, which underpin global carbon budget estimates. This benchmarking is crucial for reducing uncertainties in regional and global GHG emission and removal estimates, given the substantial variability among model outputs.
