Operational XCH₄ Retrievals from MethaneSAT: Demonstrating Sensor Performance for Constraining Regional Methane Emissions

Abstract. MethaneSAT, launched in March 2024, was designed to quantify regional methane (CH₄) emissions, with a primary focus on the oil and gas (O&G) sector. The satellite mission bridges the gap between coarse-resolution global flux mappers and high‑resolution plume imagers by combining fine spatial resolution (~110 x 400 m² pixels at nadir), high spectral resolution (~0.23 nm FWHM), and a ~220 km swath at nadir. In this study, we present the first operational retrievals of CH₄ column-averaged dry-air mole fractions (X_CH4) using the CO₂‑proxy method. We assess the instrument and retrieval algorithm performance relative to the mission's precision and accuracy requirements needed to constrain CH₄ emissions at the scale of individual O&G basins.

We focus on evaluating the retrieval against four major potential sources of systematic error: albedo dependent CH₄/CO₂ column retrieval sensitivity differences, cross-track stripe biases, aerosol-induced light path errors, and subscene CO₂ variability. Tuning the a priori covariance matrix suppresses albedo-dependent errors caused by the influence of priors to sub ppb levels. Through a careful selection of homogeneous validation targets, we show that X_CH4 cross-track biases strongly correlate with changes in the apparent instrument spectral response function (ISRF). We develop a stripe-correction algorithm using retrieved ISRF variations in a regression model combined with wavelet-Fourier filtering to reduce stripe noise from approximately 15 ppb standard deviation to near the random noise limit.

Analysis of the homogeneous validation scenes shows that single-pixel precision is approximately 30 ppb for conditions from a typical bare-ground O&G scene (0.4 albedo, 30° SZA), corresponding to ~3 ppb at 2 x 2 km², and thus well within the mission requirement of 3 ppb at 5 x 5 km². This requirement is also met for all targeted viewing geometry/albedo combinations. Comparisons with X_CH4 from TROPOMI show excellent agreement, with a mean bias of 0.1 ppb and a regression slope of 0.99 when using X_CO2 from the CAMS greenhouse gas forecast as the prior in place of the GINPUT prior used operationally. The spatial pattern of Permian basin X_CH4 enhancements between the two instruments is highly consistent.

Observations of a Pseudo-Invariant Calibration site in Libya confirm that the CO₂-proxy approach effectively mitigates biases induced by cloud and aerosol scattering, with subscene X_CO2 variability emerging as the most challenging remaining error source; gradients of a few ppm can lead to X_CH4 errors comparable to typical basin enhancements, though such conditions appear infrequent in both MethaneSAT observations and CAMS GHG forecast simulations. Overall, MethaneSAT retrieves X_CH4 with the precision and accuracy required for basin-scale emissions inversion. These results suggest that the CO₂-proxy approach remains the most viable retrieval approach for regional CH₄ emission mapping, with improved treatment of subscene X_CO2 variability representing the key priority for future work.