Understanding the processes controlling terrestrial carbon fluxes is one of the grand challenges of climate science. Carbon cycle process controls are readily studied at local scales, but integrating local knowledge across extremely heterogeneous biota, landforms and climate space has proven to be extraordinarily challenging. Consequently, top-down or integral flux constraints at process-relevant scales are essential to reducing process uncertainty. Future satellite-based estimates of greenhouse gas fluxes – such as CO2 and CH4 – could potentially provide the constraints needed to resolve biogeochemical process controls at the required scales. Our analysis is focused on Amazon wetland CH4 emissions, which amount to a scientifically crucial and methodologically challenging case study. We quantitatively derive the observing system (OS) requirements for testing wetland CH4 emission hypotheses at a process-relevant scale. To distinguish between hypothesized hydrological and carbon controls on Amazon wetland CH4 production, a satellite mission will need to resolve monthly CH4 fluxes at a ∼ 333 km resolution and with a ≤ 10 mg CH4 m−2 day−1 flux precision. We simulate a range of low-earth orbit (LEO) and geostationary orbit (GEO) CH4 OS configurations to evaluate the ability of these approaches to meet the CH4 flux requirements. Conventional LEO and GEO missions resolve monthly ∼ 333 km Amazon wetland fluxes at a 17.0 and 2.7 mg CH4 m−2 day−1 median uncertainty

√ level. Improving LEO CH4 measurement precision by 2 would only reduce the median CH4 flux uncertainty to 11.9 mg CH4 m−2 day−1 . A GEO mission with targeted observing capability could resolve fluxes at a 2.0– 2.4 mg CH4 m−2 day−1 median precision by increasing the observation density in high cloud-cover regions at the expense of other parts of the domain. We find that residual CH4 concentration biases can potentially reduce the ∼ 5-fold flux CH4 precision advantage of a GEO mission to a ∼ 2fold advantage (relative to a LEO mission). For residual CH4 bias correlation lengths of 100 km, the GEO can nonetheless meet the ≤ 10 mg CH4 m−2 day−1 requirements for systematic biases ≤ 10 ppb. Our study demonstrates that processdriven greenhouse gas OS simulations can enhance conventional uncertainty reduction assessments by quantifying the OS characteristics required for testing biogeochemical process hypotheses.