| 22 May 2026
GESat GEN1: In-Orbit Performance Review of a Compact SWIR Fizeau Interferometer for Facility-scale Methane Detection and Quantification
Abstract. The GESat GEN1 mission is a 16U microsatellite developed and operated by Absolut Sensing to demonstrate high-resolution methane monitoring using a compact SWIR Fizeau interferometer. GEN1 provides a 50m ground sampling distance to monitor facility-scale methane plumes. It is rated for an average detection rate of 500 kg.h−1, with the capacity to resolve leaks as small as 100 kg.h−1 in pristine environmental conditions. The system is designed to achieve a column retrieval precision of 120 ppbv, defined as the mean standard deviation of the retrieved column-averaged dry-air mole fraction of methane over operational conditions. This corresponds to a random error of approximately 6.5% relative to a background
concentration of 1850 ppbv.
The paper first presents the GEN1 mission and its interferometric payload, designed to enable selective methane sensing from a highly compact platform. It then describes the retrieval architecture, based on a physics-guided parametric algorithm that occupies an intermediate complexity between matched-filter methods, as used for missions such as Tanager-1 and EMIT (Carbon Mapper, 2024), and full physics schemes using high accuracy radiative transfer code such as libRadtran or 4A/OP (Mayer and Kylling, 2005; Scott and Chédin, 1981; Chéruy et al., 1995). The proposed model generates top-of-atmosphere radiances in the SWIR [1550, 1700nm] region with no systematic bias relative to reference full-physics model and a random error below 0.2 ppbv in methane column retrieval. Moreover, on identical computing hardware, the physics-guided parametric approach achieves a processing speed approximately 20×103 times faster than the full-physics model. This strategy therefore provides a balanced trade-off between robustness, accuracy, and computational cost, enabling large-scale processing compatible with constellation-level operations while preserving radiometric fidelity required for quantitative methane retrievals. Supported by this instrument and retrieval design, representative in-orbit results are reported, including methane plume detections over industrial facilities, together with an assessment of current limitations and planned evolutions of the processing chain. Finally, the paper concludes by evaluating the mission’s performance against its design specifications.With more than 160 acquisitions processed, the system achieves an average precision of 111 ppbv, successfully meeting the mission’s primary target, with an average detection sensitivity of 450 kg.h−1. Notably, several measurements achieved precision better than 80 ppbv, which enables an ultimate methane plume detection limit corresponding to emission rates below 100 kg.h−1 under ideal meteorological conditions. This on-orbit demonstration confirms the viability of the Fizeau interferometric approach for high-resolution monitoring and paves the way for future constellation deployment.
The manuscript by Pignol et al. presents GESsat GEN1, a new satellite system designed for high resolution detection and quantification of methane emissions. The manuscript describes the instrument, the methane retrieval set-up, and results from an initial performance assessment based on real on-orbit data.
I consider that this contribution may be relevant to the AMT readership, as it introduces a promising new mission for the growing methane-sensitive satellite ecosystem. On the other hand, I do not think that the manuscript does a good job to present this new system and its processing chain.
Major comments:
1) Retrieval: The authors present a relatively sophisticated retrieval set-up implemented for this mission. It includes a specific radiative transfer model and an optimal estimation retrieval framework, the description of which spans more than one third of the text. The authors also state that “to validate a physics-guided parametric retrieval framework that balances accuracy, robustness, and computational efficiency for large-scale processing” (L54) is an objective of this work.
However, the authors adopt a number of assumptions and simplifications on this general retrieval framework (spectrally-constant albedo, no scattering, fixed vertical profiles, ...), which ends up in a retrieval state vector consisting of only two elements (XCH4 and XH2O). Also, there is no description of the prior used to constrain the retrieval in the optimal estimation framework.
So it seems that despite the complexity of the initial retrieval framework, the actual retrieval simply relies on a two-parameter forward model applied to 25-nm fiting windows. I can imagine that the resulting XCH4 maps can still show XCH4 fields with a decent SNR, but also very inaccurate (confirmed by the relatively large systematic errors shown in Figs. 13-14). Also, a simplified forward model consisting in two-way gas transmittances derived from CH4 and H2O cross sections neglecting scattering would lead to a similar performance than the retrieval approach proposed here. Those transmittances could be easily generated from Hitran, e.g. using Libradtran.
I would recommend to substantially revise section 3 to properly present the actual retrieval scheme, with a proper discussion of the compromises adopted and their potential impact on the large retrieval offsets obtained in the comparison with TCCON data.
2) Scope: The manuscript presents a number of technical system performance and data quality tests, including inteferogram formation or Fizeau interferometer imperfections. However, I miss some more information on the mission characteristics and data quality that could be of interest to potential data users.
In particular, more examples of good and bad retrieval cases could be provided (only a couple of methane plumes are shown). I would also welcome a discussion of the potential and limitations of this new system with respect to other available satellite data sources, including GHGSat, Carbon Mapper or the several public imaging spectroscopy missions being used for plume mapping (EnMAP, EMIT). The comparison to GHGSat would be particularly relevant because of the technical similarity between the two missions.
Other minor comments:
L3. please also provide the instrument’s spectral window and a spectral resolution and the acquisition’s spatial swath
L11. I don’t think that Carbon Mapper is the best reference for the matched filter retrieval. For example, Thompson et al. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016GL069079 would be a much better reference
L29. Jongaramrungruang et al. is not a proper reference of GWP numbers
L68. The 120 ppbv calculation corresponds to a single pixel, which is not representative of a real cases in which several pixels above the noise level would be needed to detect a plume
Section 2.1: please add information on swath and spectral window and resolution
L93 and 96: what are “low noise” and “sufficient SNR”?
Fig. 3. I am not sure this figure is relevant
Fig. 5. It is not clear to me how the radiance differences between Libradtran and the parametric model may relate to XCH4 retrieval errors?
Eq. 17-18: how is Sa generated?
L336-341: this could be in the Methods sections, rather than in Results.
Fig.8: a fit & residual of the forward model to those spectra would be ore informative of the data quality and retrieval performance
Fig. 10: what fraction of the XCH4 STD is due to surface clutter and what to measurement noise? Acquisitions with surface with different levels of spatial heterogeneity could be used to evaluate that, and it would be useful information to users.
Fig. 12: it is difficult to appreciate what is represented in this “internal absolute consistency” plot.