Performance validation of the GHGSat Methane Constellation via controlled releases

Abstract. 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.