the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jul 2026
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 (XCH4) 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 XCH4 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 XCH4 from TROPOMI show excellent agreement, with a mean bias of 0.1 ppb and a regression slope of 0.99 when using XCO2 from the CAMS greenhouse gas forecast as the prior in place of the GINPUT prior used operationally. The spatial pattern of Permian basin XCH4 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 XCO2 variability emerging as the most challenging remaining error source; gradients of a few ppm can lead to XCH4 errors comparable to typical basin enhancements, though such conditions appear infrequent in both MethaneSAT observations and CAMS GHG forecast simulations. Overall, MethaneSAT retrieves XCH4 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 XCO2 variability representing the key priority for future work.
- Preprint
(52052 KB) - Metadata XML
-
Supplement
(4789 KB) - BibTeX
- EndNote
Status: open (until 08 Aug 2026)
- RC1: 'Comment on egusphere-2026-3365', Anonymous Referee #1, 20 Jul 2026 reply
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 61 | 43 | 9 | 113 | 21 | 7 | 9 |
- HTML: 61
- PDF: 43
- XML: 9
- Total: 113
- Supplement: 21
- BibTeX: 7
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
General comments
A strong and important paper documenting the first operational MethaneSAT XCH4 retrievals. The stripe-correction and validation work is careful overall. Comments below are mostly requests for clarification or additional detail; points 2 and 7 affect the headline accuracy claims and I'd like to see them addressed.
Specific comments
1. L4, spatial resolution. Native pixels are ~110×400 m² but precision is reported at 2×2/5×5 km at L14-16. A sentence on why this resolution was chosen, presumably to support cloud/aerosol screening and stripe/ISRF characterization rather than the reported precision itself, would help readers.
2. L19-20, Section 4.2, CO2 prior choice. The mean bias against TROPOMI is similar under either prior (~0.1 ppb), but the regression slope and hemispheric behavior differ substantially: under the operational GGG2020 prior the slope is 1.069, driven by an 8 ppb low bias in the Southern Hemisphere (vs. +2 ppb in the North); under CAMS the SH bias drops to 1.4 ppb and slope to 0.991. The abstract's slope figure of 0.99 is explicitly tied to the CAMS substitution. Please state more clearly in the abstract/conclusions which prior underlies which reported number.
3. L106-108, dataset selection. Please clarify how the 340 scenes were selected from the 1160 processed to L2, and ideally show how they compare to the full dataset in albedo/SZA/region, so readers can judge how representative the validation statistics are.
4. Section 3.2, prior covariance tuning. γ_CO2 = 10 is selected to give "near unity PBL averaging kernel responses" in Fig. 2, a reasonable but qualitative criterion. A brief comment on sensitivity of the reported precision/bias numbers to this choice would strengthen the section.
5. Section 3 ordering. The DoFS-based quality filter in 3.1 depends on the retrieval's averaging kernels, which in turn depend on the prior covariance tuning introduced afterward in 3.2. Consider reordering for clarity.
6. Eq. 13-14, Figure 4. The precision estimator assumes "natural XCH4 variability occurs over spatial scales larger than a few pixels" (L270), which may not hold near plume edges. Since this feeds into homogeneous-scene selection and the stripe correction training set, a brief comment on sensitivity here would be useful.
7. Section 3.4, destriping. PLS alone reduces σ_b to 6.1 ppb; adding the wavelet step pushes it below the 3.5 ppb theoretical noise floor, which the text attributes to the band-pass filter suppressing some real Fourier modes along with the stripe noise. A wavelet-only run (without the PLS step) would help clarify how much each stage of the correction actually contributes to the final result.
8. Section 4, independent validation.The TROPOMI and L1/L2 self-consistency checks are useful but not fully independent of the retrieval framework. Was a comparison against controlled methane release data considered?
9. TCCON comparison. Since GINPUT/GGG2020 (used as the CO2 prior) is itself developed for TCCON (L130-131), a direct MethaneSAT–TCCON comparison could help clarify the prior-sensitivity question in point 2, where coverage allows.
10. Section 4.3, aerosol dependence. The Libya PICS analysis covers one aerosol regime (bright surface, mineral dust). It would help to know whether targets with other aerosol types are available to test the robustness of the CO2-proxy approach more broadly.
11. Data availability. Given the paper's role as a mission-performance demonstration, consider releasing the 340-scene validation subset (with homogeneous-scene flags) as a standalone dataset alongside the gated access form.
Minor
• Eq. 6: briefly define h in words on first use.
• L195-199: clarify whether the averaging kernels in Fig. 2 include the full state vector or just CH4/CO2 layers.
• Fig. 12: correlation coefficients per scene range 0.46-0.86; worth stating whether cloud-contaminated scenes are excluded from any summary statistics or only flagged qualitatively.