the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Satellite-based inversion of global methane fluxes: Capabilities and implications of GOSAT-2 measurements
Abstract. Methane (CH4) is a key greenhouse gas with a strong climate impact and a relatively short atmospheric lifetime, making accurate monitoring essential for mitigation strategies. Satellite observations provide global coverage and independent constraints on CH4 emission estimates, and GOSAT-2, launched in 2018 as the successor to GOSAT, was designed to improve retrieval accuracy and enhance flux estimation. This study presents an evaluation of the GOSAT-2 Level 4 (G2L4) CH4 flux product, supported by analysis of the underlying Level 2 (L2) XCH4 retrievals, and summarizes key findings on global and regional CH4 budgets. Using an atmospheric inversion framework, we generated G2L4 posterior CH4 fluxes and assessed their consistency by comparing them with inversions constrained by alternative observational datasets, including GOSAT L2 retrievals and ground-based and aircraft measurements. GOSAT-2 achieved substantial improvements in observational coverage and data density compared to its predecessor, particularly in tropical and high-latitude regions. Posterior flux estimates derived from G2L4 are broadly consistent with global CH4 budgets reported in synthesis studies, while prior-to-posterior differences reveal positive corrections in tropical regions and negative adjustments in several mid-latitude industrial areas. A preliminary sector-focused assessment further demonstrates the potential of GOSAT-2 to inform anthropogenic CH4 emission evaluations in regions where such sources dominate. These findings highlight the capability of GOSAT-2 to refine regional and global CH4 emission estimates and underscore priorities for future improvements in retrieval algorithms, observation strategies, and integration with complementary datasets.
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Status: open (until 24 Jun 2026)
- RC1: 'Comment on essd-2026-171', Anonymous Referee #1, 17 May 2026 reply
Data sets
GOSAT-2 L4A Global CH4 Flux Product Makoto Saito https://prdct.gosat-2.nies.go.jp/app/searchproduct/display
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- 1
This study by Saito et al. provides a high-level overview and evaluation of the GOSAT-2 XCH4 product and the fluxes inferred from its use. GOSAT-2 is shown to produce significantly more successful observations than GOSAT-1, owing in part to its intelligent pointing. GOSAT-2 and GOSAT-1 XCH4 observations, in aggregate, are consistent, though the GOSAT-1 bias correction appears to drive a latitude-dependent bias between the two instruments. The GOSAT-2 observations are ingested into a previously described inversion system, with the posterior methane fluxes being remarkably different than the prior spatially (South America, Arabian Peninsula) and temporally (Northern Hemisphere seasonality). Generally speaking, the inversion using GOSAT-2 observations is consistent with the inversion using the GOSAT-1 observations, and both differ from the inversion using in situ observations, particularly close to the Equator. This paper will serve as a nice reference for GOSAT-2, and I recommend its publication with minor revisions described below, all at the discretion of the authors.
Minor comments
While reading the methods, I was interested in 1) how the pre- and post-screening works, and 2) the spectral range used for retrievals. Just a sentence on each would be helpful.
Line 256: you write “repeated soundings over the same location within short time intervals were counted only once per day.” Could you clarify what you mean by this and when repeat soundings would occur?
For lines 291–293, could you add a sentence explaining how the South Atlantic Anomaly would impact the retrievals, and only for GOSAT-2 (as well as a reference if possible)?
Technical corrections
Line 17: would water not be the most abundant reactive greenhouse gas?
Line 20: “force” to “forcer”
Line 23: remove “to guide”
Line 42: might Frankenberg et al. (2005) be a better reference here?
Figure 4: maybe a red-white-blue color map would be easier to read for the “diff” plot.