Performance validation of the GHGSat Methane Constellation via controlled releases

Antoine Ramier , Hanford Deglint, Ariane Deslieres, Lia Formenti, Marianne Girard, Dylan Jervis, David Marshall, Jean Philippe MacLean, Jason McKeever, Andrew McKell, Mathias Strupler, Ewan Tarrant

Satellite remote sensing has become an important tool for detecting, quantifying, and attributing methane emissions, yet a rigorous, condition-dependent characterization of point-source imager performance has not previously been established, despite being essential to support its use in regulatory and voluntary reporting frameworks. We present a comprehensive performance assessment of the GHGSat constellation of high-resolution methane-imaging satellites based on a multi-campaign controlled release dataset spanning 2021 to 2026 that combines self-organized campaigns with independent third-party experiments. We develop a probabilistic, environmental conditions-dependent detection model in which the probability of detection depends on the plume signal to noise ratio (SNR), which is in turn a function of emission rate, wind speed, retrieval noise, and spatial resolution. We find that an SNR of 1.72 ± 0.39 is required to achieve a 50% probability of detection, which translates to a detection limit Q50 = 99.0 ± 5.3 kg h⁻¹ in median environmental conditions encountered in controlled releases (3 m s⁻¹ wind speed, 7 mmol m⁻² column density noise, 27 m resolution). The estimated Q50 is shown to converge stably as data accumulate and to remain consistent when blind validation samples are added to self-organized releases. Quantification accuracy is evaluated through parity analysis of estimated versus metered emission rates, yielding an ordinary least squares slope of 0.93 ± 0.03 and R² of 0.92 using reported model winds, improving to 0.96 ± 0.02 and R² = 0.95 with a co-located anemometer. A comparison of emission rate estimates based on ERA5, IFS, and HRRR winds shows that quantification accuracy is largely insensitive to the choice of operationally available wind product, with a residual underestimation at low emission rates that correlates with wind model spatial resolution. We further demonstrate a bias correction combining a local concentration background correction with an empirically recalibrated effective wind speed, which recovers the low-rate sources that were previously underestimated and removes most of the residual bias without degrading accuracy at higher emission rates. These results establish a transparent, statistically grounded baseline for the GHGSat constellation's detection and quantification performance and provide a methodological framework that can be extended as additional controlled-release data become available.