Analysis of Local Methane Emissions Using Near-Simultaneous Multi-Satellite Observations: Insights from Landfills and Oil-Gas Facilities

Alvise Ferrari; Giovanni Laneve; Raul Alejandro Carvajal Tellez; Rocchina Guarini; Simone Saquella; Valerio Pampanoni

REVIEW 4 major objections 5 minor 22 references

Analysis of Local Methane Emissions Using Near-Simultaneous Multi-Satellite Observations: Insights from Landfills and Oil-Gas Facilities

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims methane emission rates estimated from four different satellites are broadly consistent when the overpasses are minutes to a couple of hours apart, and that residual spreads trace to source variability and sensor design.

desk verdict A genuinely new multi-satellite methane comparison that needs a unit correction and a softer conclusion before the consistency claim can be trusted. read the letter →

arxiv 2506.01113 v1 pith:34SCC63Z submitted 2025-06-01 eess.IV

Alvise Ferrari , Giovanni Laneve , Raul Alejandro Carvajal Tellez , Valerio Pampanoni , Simone Saquella , Rocchina Guarini This is my paper

classification eess.IV

keywords methaneemissionshyperspectralremotesensingPRISMAEnMAPGHGSatEMITIntegratedMassEnhancementclutter-matchedfilter

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that independent spaceborne methane sensors can be trusted to agree when they observe the same source almost simultaneously. Using two case studies—a landfill in Buenos Aires and a gas-compression site in Turkmenistan—the authors compare emission fluxes estimated by PRISMA, EnMAP, EMIT, and GHGSat from overpasses separated by minutes to a few hours. They find that acquisitions close in time return consistent emission rates, while discrepancies grow with longer time gaps and with differences in sensor characteristics. The practical point is that a fleet of diverse satellites, read together, could monitor local methane sources more continuously than any single instrument alone.

What carries the argument

The argument is carried by a retrieval-plus-quantification pipeline applied consistently across sensors. The clutter-matched filter estimates per-pixel methane column enhancement $\Delta X_{\mathrm{CH_4}}$ using a scene-specific target spectrum generated by radiative-transfer simulations and convolved with each sensor's spectral response. The Integrated Mass Enhancement (IME) model converts pixel enhancements to total excess methane mass, and the emission rate is $Q = \mathrm{IME} \cdot U_{\mathrm{eff}} / L$, where $L$ is the square root of the plume-mask area and $U_{\mathrm{eff}}$ is an effective wind speed obtained from the 10-m wind $U_{10}$ through sensor-specific linear relations ($0.37 U_{10} + 0.70$ for PRISMA, $0.37 U_{10} + 0.69$ for EnMAP, $0.45 U_{10} + 0.67$ for EMIT). Manual plume delineation, guided by high-resolution imagery, determines which pixels enter the IME sum.

What would settle it

A controlled-release test—a known methane flow metered at the source while two or more of these satellites overpass within minutes—would settle the claim: if the instrument estimates bracket the metered flux as closely as GHGSat and EnMAP do at Buenos Aires, the consistency claim is confirmed, and if they diverge by factors of three, the spread is sensor-related rather than source-related.

Watch

Extended reading notes

Core claim

The central claim is that near-simultaneous multi-satellite observations reveal a high degree of consistency between instruments for local methane emission quantification. At the Buenos Aires landfill, GHGSat and EnMAP, passing 97 seconds apart, return closely matching flux estimates, while EMIT several hours later gives a lower value under different illumination. At Kamishlidza, GHGSat, PRISMA, and EnMAP pass within about 80 minutes and report 18.54, 12.29, and 37.72 t/h; the paper attributes this spread to emission variability across the 45-minute intervals and to sensor-specific factors such as EnMAP's higher signal-to-noise ratio, declining to draw a definitive conclusion from that single case. The paper's conclusion states that the results reveal a high degree of consistency between instruments when observations are acquired within a short timeframe.

Load-bearing premise

The result stands on the assumption that the effective wind speed for each scene is correctly given by the published linear formulas derived for other sensor configurations; if that conversion is wrong for these sites, every reported flux shifts in proportion and the apparent inter-satellite agreement could change.

Editorial extensions

If this is right

If short-timeframe consistency holds, a single source can be cross-checked by independent sensors, turning single-satellite detections into verified emission rates.
Sub-hour emission changes at oil and gas facilities become observable by scheduling overlapping overpasses, because nearby-pass differences can be read as source change rather than sensor error.
Landfills, as stable diffuse emitters, can serve as natural calibration targets for comparing instruments across different overpass times.
A harmonized multi-satellite monitoring framework becomes feasible, combining each sensor's strengths in revisit frequency, spectral resolution, signal-to-noise ratio, and swath width.
Sensor-specific factors such as signal-to-noise ratio, spectral smile, spectral full-width at half-maximum, and view geometry must be documented in every intercomparison, since they explain part of the remaining spread.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the consistency claim generalizes, controlled-release experiments with metered methane flow under known winds could turn near-simultaneous comparisons into an operational calibration chain, anchoring weaker sensors to stronger ones.
The spread at Kamishlidza offers an alternative reading the paper leaves open: at low wind speed, the linear effective-wind conversion may amplify small plume differences, so part of the apparent temporal variability could be wind-model error rather than true emission change.
A natural extension is to replace the single linear wind conversion with local large-eddy-simulation wind fields for each overpass, testing whether short-time consistency improves further.
Comparing satellite flux estimates against ground-based eddy-covariance or inverse-model emissions at the same landfill would provide an absolute accuracy benchmark that the relative inter-satellite comparison cannot give.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take on arXiv:2506.01113. The paper does something new: it lines up near-simultaneous overpasses of PRISMA, EnMAP, EMIT, and GHGSat over the same sources and compares IME flux estimates. Two cases, one landfill in Buenos Aires and one gas facility in Turkmenistan. The near-simultaneity of GHGSat and EnMAP at 97 seconds is a nice opportunity. The authors also make their PRISMA and EnMAP processing code available, which is a plus. The retrieval methodology is standard CMF + IME, so no breakthrough there, but the comparison exercise is a legitimate contribution to an active field.

That having been said, the central claim is not supported as printed. In Case 1, GHGSat gives 20.637 kg/h, while EnMAP and EMIT are reported as 18.55 t/h and 13.57 t/h. If 't/h' is literal, EnMAP is about 900 times larger than GHGSat, which is the opposite of 'very close.' If it's a typo for kg/h, the consistency claim is salvageable, but the paper needs to say so. This is the only near-simultaneous pair that supports the headline conclusion, so as written it's unreadable.

Case 2 shows a threefold spread (18.54, 12.29, 37.72 in whatever unit) across overpasses ~35 minutes apart. The authors write that no definitive conclusions can be drawn and attribute the spread to temporal variability, which is plausible. But without uncertainty quantification you cannot distinguish real variability from sensor differences. The paper openly defers uncertainty propagation to future work, and there is no sensitivity analysis for the effective wind speed, which is taken from previous LES fits for each sensor without in-scene validation. Since Q is directly proportional to that wind speed, a 30% error shifts every flux by 30% and could reconcile or widen the gaps. There is also manual plume delineation and a single weather source.

This is a two-case pilot, not a validation. The right verdict is: important question, real data, open code, but the manuscript overclaims. A desk reject would be too harsh, but it needs major revision. I would send it to peer review to let the authors fix the units, add at least a wind-speed sensitivity test, and soften the conclusion to something like 'these cases illustrate the potential and the challenges of multi-satellite comparison.' That version would be worth citing. As is, I would not cite it yet. For a reading group, it could be a good case study on how unit errors and missing errors can cripple a comparison paper. So 'yes' for peer review, 'maybe' for reading group.

Referee Report

4 major / 5 minor

Summary. The paper compares methane emission flux estimates from four satellite instruments (PRISMA, EnMAP, EMIT, GHGSat) over two sites: the Buenos Aires landfill and the Kamishlidza gas compression station. Using the integrated mass enhancement (IME) model with wind from a single weather service, the authors report fluxes from near-simultaneous overpasses and claim that the instruments show a high degree of consistency when observations are close in time. The paper also describes the matched-filter retrieval, the sensor-specific effective-wind parameterizations, and the manual plume-mask methodology, and it defers a fuller uncertainty analysis to future work.

Significance. If the consistency claim were robust, the study would be a useful step toward multi-satellite harmonization of methane monitoring, and the public availability of the processing repositories (PRISMA-CH4, EnMAP-CH4) is a concrete strength. However, the paper's central claim is currently not supported by the evidence as presented: one of the two cases contains an apparent factor-of-1000 unit inconsistency, the other shows a threefold spread that the authors themselves cannot explain, and no uncertainty quantification is provided. The work is therefore a potentially valuable case study, but its main conclusion needs substantial revision before it can be accepted.

major comments (4)

[IV.A] The central comparison in Case 1 is internally inconsistent: GHGSat is reported as 20.637 kg/h, while EnMAP is reported as 18.55 t/h and EMIT as 13.57 t/h. If the t/h values are literal, EnMAP is about 900 times larger than GHGSat, which contradicts the text's claim of 'excellent agreement.' If kg/h was intended for EnMAP and EMIT, then the unit notation must be corrected throughout the paper and all derived flux values re-expressed consistently. As printed, the primary evidence for inter-instrument consistency is unreadable.
[IV.B] Case 2 shows flux estimates of 18.54 t/h (GHGSat), 12.29 t/h (PRISMA), and 37.72 t/h (EnMAP), a spread of roughly a factor of three over about 80 minutes. The authors state that 'it is not possible to draw definitive conclusions' from this case, yet the abstract and conclusions claim 'a high degree of consistency between instruments when observations are acquired within a short timeframe.' The Case 2 data do not support that general claim, and attributing the differences to temporal variability is speculative without supporting emission-activity data or a quantitative bound on source variability.
[II.B.3] Equation (4) makes Q directly proportional to U_eff, and U_eff is taken from sensor-specific linear relations (e.g., U_eff = 0.37 U_10 + 0.70 for PRISMA) reported in [11] for area-source emitters. The manuscript provides no in-scene validation of these parameterizations and no sensitivity analysis; a 30% error in U_eff translates directly into a 30% shift in every reported Q, which could either reconcile or widen the inter-satellite differences. Since the paper explicitly defers uncertainty propagation to future work, the consistency claim is currently unquantified and therefore not falsifiable from the presented data.
[II.B.1] Plume segmentation is described as manual polygon delineation, with no inter-operator comparison or sensitivity test. Because IME and Q depend directly on which pixels are included in the plume mask, the reported agreement (or disagreement) between instruments could be influenced by subjective choices. At minimum, the authors should state how the mask was defined consistently across sensors for each case and provide some measure of how Q changes with reasonable mask variations.

minor comments (5)

[II.B] The conversion factor from ppm·m to ppb is stated as 0.125 with an assumed 8 km scale height, but the units are not fully specified; please clarify the exact column-height and units used in Eq. (2), since k depends on this factor.
[III.C] The acronym GHGSat is misspelled as 'GHGsat' in the Introduction; please standardize throughout.
[References] Reference [11] (Zhang et al., 2024) has no journal or DOI; if it is a preprint or submitted manuscript, that should be stated. Also, the IME references [18] and [19] should be formatted consistently with the journal style.
[Figures] Figures 1 and 2 appear at the end of the manuscript without detailed captions; the captions should be expanded to identify each panel, the sensor, the acquisition time, and the plume mask so that the reader can follow the Case 1 and Case 2 comparisons without guessing.
[IV.A] The local-time parentheticals ('around 11:45 local time', 'around 16:00 local time') are not derived from the UTC times (14:45, 14:46, 18:59 UTC); if local time means Buenos Aires time (UTC−3), the times should read 11:45 and 15:59. Please verify and correct.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: flux estimates use an external IME model and external LES-derived wind parameterizations; the consistency claim is not reduced to any fitted input or self-citation.

full rationale

The paper computes Q from Eq. (4) using IME and effective wind speeds whose linear relationships (e.g., U_eff = 0.37·U_10 + 0.70 for PRISMA) are attributed to [11], an external LES-based study, not fitted to the scenes analyzed. Equation (4) is a standard physical scaling: Q = IME·U_eff/L. No parameter in the paper is estimated from the flux values and then reused as a 'prediction'; plume delineation is manual but not a fitted input. The self-citations [6], [13], and [14] document the matched-filter implementation and code provenance; they are not used as evidence for the inter-satellite consistency claim. The paper candidly notes in Case 2 that 'it is not possible to draw definitive conclusions' and defers uncertainty propagation to 'a future, more comprehensive study,' which is a limitation rather than a circular step. The printed Case 1 comparison mixes kg/h for GHGSat (20.637 kg/h) with t/h for EnMAP (18.55 t/h) and EMIT (13.57 t/h); if literal, this contradicts the claimed agreement, but this is an internal-consistency/correctness problem, not an equivalence of the derivation to its inputs. Accordingly, no circularity step can be exhibited under the defined kinds, and the circularity score is 0.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The central flux estimates depend on several external models and assumptions: the CMF retrieval, the IME model, effective wind speed parameterizations from prior work, and weather data. No new entities are introduced, but the validity of these assumptions is not evaluated within this paper.

free parameters (2)

Effective wind speed linear relation coefficients = PRISMA: 0.37 and 0.70; EnMAP: 0.37 and 0.69; EMIT: 0.45 and 0.67
These coefficients from [11] convert 10-m wind speed to effective wind speed for the IME model. They are based on large-eddy simulations for specific sensor resolutions and are applied here without recalibration. They directly scale Q, so any error propagates linearly.
Conversion factor from ppm·m to ppb = 0.125
The paper assumes an 8 km scale height and uniform vertical distribution to convert column enhancements to mixing ratios. This is a chosen constant, not site-specific.

assumptions (6)

domain assumption The CMF retrieval using scene-specific MODTRAN target spectra gives accurate column enhancements.
Section II-A describes the retrieval; no validation against in situ measurements is provided.
domain assumption The IME model accurately converts plume column enhancements to emission flux.
Section II-B-2 uses IME from [18,19]; the paper assumes its validity for these area sources.
domain assumption The effective wind speed relationships from Zhang et al. apply to the specific sensor scenes.
Section II-B-3 lists linear relations for PRISMA, EnMAP, EMIT; these are from [11] and not revalidated.
domain assumption Wind speed and direction from OpenWeather are representative of the overpass times.
Section IV uses a single wind measurement (6.7 m/s for Case 1, 2.7 m/s for Case 2) without uncertainty or vertical profile.
domain assumption Manual plume delineation identifies the full plume and excludes false positives.
Section II-B-1 states plumes are manually delineated; this introduces subjectivity.
domain assumption The assumed 8 km scale height for the ppm·m to ppb conversion is valid.
Section II-B cites [5] for this constant; it may not hold for all scenes.

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Cite this review

Pith. "Pith review of Analysis of Local Methane Emissions Using Near-Simultaneous Multi-Satellite Observations: Insights from Landfills and Oil-Gas Facilities." pith.science (2026). https://pith.science/paper/34SCC63Z

@misc{pith2026250601113,
  author       = {Pith},
  title        = {Pith review of: Analysis of Local Methane Emissions Using Near-Simultaneous Multi-Satellite Observations: Insights from Landfills and Oil-Gas Facilities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/34SCC63Z}},
  note         = {Machine review of arXiv:2506.01113}
}

read the original abstract

Methane (CH4) is a potent greenhouse gas, and its detection and quantification are crucial for mitigating the greenhouse effect. This study presents a comparative analysis of methane emissions observed using near-simultaneous observations from hyperspectral imaging spectrometers hosted aboard different satellite platforms (PRISMA, EnMAP, EMIT and GHGSat). Methane emissions from oil and gas facilities and landfills are analyzed to evaluate the consistency and precision of the sensors and temporal variability of the source. Landfills, characterized by diffuse and stable emissions, and dynamic oil and gas facilities, subject to operational variability, provide contrasting use cases for emission monitoring. Emission rates are quantified using the Integrated Mass Enhancement (IME) model and validated across satellites with overlapping acquisitions. This study highlights the advantages and limitations of each satellite system, emphasizing the critical role of multi-sensor integration in bridging temporal and spatial observation gaps. Insights derived here aim to enhance global methane monitoring frameworks and guide future satellite design for improved emission quantification.

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Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages

[11]

and Thorpe, A.,

Zhang, X., Maasakkers, J.D., Roger, J., Guanter, L., Sharma, S., Lama, S., Tol, P., Varon, D.J., Cusworth, D.H., Howell, K. and Thorpe, A.,

work page
[1]

and Roberts, D.A., 2014

Thorpe, A.K., Frankenberg, C. and Roberts, D.A., 2014. Retrieval techniques for airborne imaging of methane concentrations using high spatial and moderate spectral resolution: application to AVIRIS. Atmospheric Measurement Techniques, 7(2), pp.491-506

work page 2014
[2]

and Leifer, I., 2013

Thorpe, A.K., Roberts, D.A., Bradley, E.S., Funk, C.C., Dennison, P.E. and Leifer, I., 2013. High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster -Tuned Matched Filter technique and imaging spectrometry. Remote Sensing of Environment, 134, pp.305-318

work page 2013
[3]

and Joshi, S.C., 2020

Foote, M.D., Dennison, P.E., Thorpe, A.K., Thompson, D.R., Jongaramrungruang, S., Frankenberg, C. and Joshi, S.C., 2020. Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior. IEEE Transactions on Geoscience and Remote Sensing, 58(9), pp.6480-6492

work page 2020
[4]

and Joshi, S.C.,

Foote, M.D., Dennison, P.E., Sullivan, P.R., O'Neill, K.B., Thorpe, A.K., Thompson, D.R., Cusworth, D.H., Duren, R. and Joshi, S.C.,

work page
[5]

and Colombo, R., 2021

Guanter, L., Irakulis -Loitxate, I., Gorroño, J., Sánchez- García, E., Cusworth, D.H., Varon, D.J., Cogliati, S. and Colombo, R., 2021. Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer. Remote Sensing of Environment, 265, p.112671

work page 2021
[6]

and Rossi, F., 2024, July

Ferrari, A., Laneve, G., Pampanoni, V., Carvajal, A. and Rossi, F., 2024, July. Monitoring Methane Emissions from Landfills Using Prisma Imagery. In IGARSS 2024- 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 3663-3667). IEEE

work page 2024
[7]

and Foucher, P.Y., 2021

Nesme, N., Marion, R., Lezeaux, O., Doz, S., Camy -Peyret, C. and Foucher, P.Y., 2021. Joint use of in -scene background radiance estimation and optimal estimation methods for quantifying methane emissions using prisma hyperspectral satellite data: Application to the korpezhe industrial site. Remote Sensing, 13(24), p.4992

work page 2021

Show all 22 references

[8]

and Guanter, L., 2024

Roger, J., Irakulis-Loitxate, I., Valverde, A., Gorroño, J., Chabrillat, S., Brell, M. and Guanter, L., 2024. High -resolution methane mapping with the EnMAP satellite imaging spectroscopy mission. IEEE Transactions on Geoscience and Remote Sensing

2024
[9]

and Tarrant, E., 2021

Jervis, D., McKeever, J., Durak, B.O., Sloan, J.J., Gains, D., Varon, D.J., Ramier, A., Strupler, M. and Tarrant, E., 2021. The GHGSat -D imaging spectrometer. Atmospheric Measurement Techniques, 14(3), pp.2127-2140

2021
[10]

and Clark, R.N., 2024

Thompson, D.R., Green, R.O., Bradley, C., Brodrick, P.G., Mahowald, N., Dor, E.B., Bennett, M., Bernas, M., Carmon, N., Chadwick, K.D. and Clark, R.N., 2024. On- orbit calibration and performance of the EMIT imaging spectrometer. Remote Sensing of Environme nt, 303, p.113986

2024
[12]

Theiler, J., Foy, B.R. (2006). Effect of Signal Contamination in Matched-Filter Detection of the Signal on a Cluttered Background. IEEE Geosci. Remote Sens. Lett., 3, 98–102

2006
[13]

PRISMA-CH4

A. Ferrari, “PRISMA-CH4”, github repository, [Online], Available at: https://github.com/AlFe23/PRISMA-CH4
[14]

EnMAP- CH4

A. Ferrari, “EnMAP- CH4”, github repository, [Online], Available at: https://github.com/AlFe23/EnMAP-CH4
[15]

EMIT L2B Greenhouse Gas Data Product User Guide

Jet Propulsion Laboratory (2023). EMIT L2B Greenhouse Gas Data Product User Guide. Jet Propulsion Laboratory, California Institute of Technology. Available at https://lpdaac.usgs.gov/products/emitl2bch4enhv001/

2023
[16]

G., Thorpe, A

Brodrick, P. G., Thorpe, A. K., Villanueva -Weeks, C. S., Elder, C., Fahlen, J., & Thompson, D. R. (2023). EMIT Greenhouse Gas Algorithms: Greenhouse Gas Point Source Mapping and Related Products - Theoretical Basis. Jet Propulsion Laboratory, California Institute of Technolog...

2023
[17]

Brodrick, P. G. et al., “ emit-sds/emit-ghg: Mapping of greenhouse gases with EMIT. github repository, [Online], Available at: https://github.com/emit-sds/emit-ghg
[18]

Airborne methane remote measurements reveal heavy-tail flux distribution in four corners region,

C. Frankenberg et al., “Airborne methane remote measurements reveal heavy-tail flux distribution in four corners region,” Proc. Nat. Acad. Sci. India A, Phys. Sci., vol. 113, no. 35, pp. 9734–9739, Aug
[19]

Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes,

D. J. Varon et al., “Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes,” Atmos. Meas. Techn., vol. 11, no. 10, pp. 5673–5686, Oct. 2018

2018
[20]

Potential of next- generation imaging spectrometers to detect and quantify methane point sources from space,

D. H. Cusworth et al., “Potential of next- generation imaging spectrometers to detect and quantify methane point sources from space,” Atmos. Meas. Techn., vol. 12, no. 10, pp. 5655– 5668, Oct. 2019

2019
[2021]

Remote Sensing of Environment, 264, p.112574

Impact of scene-specific enhancement spectra on matched filter greenhouse gas retrievals from imaging spectroscopy. Remote Sensing of Environment, 264, p.112574
[2024]

Global identification of solid waste methane super emitters using hyperspectral satellites

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [11]

and Thorpe, A.,

Zhang, X., Maasakkers, J.D., Roger, J., Guanter, L., Sharma, S., Lama, S., Tol, P., Varon, D.J., Cusworth, D.H., Howell, K. and Thorpe, A.,

work page

[2] [1]

and Roberts, D.A., 2014

Thorpe, A.K., Frankenberg, C. and Roberts, D.A., 2014. Retrieval techniques for airborne imaging of methane concentrations using high spatial and moderate spectral resolution: application to AVIRIS. Atmospheric Measurement Techniques, 7(2), pp.491-506

work page 2014

[3] [2]

and Leifer, I., 2013

Thorpe, A.K., Roberts, D.A., Bradley, E.S., Funk, C.C., Dennison, P.E. and Leifer, I., 2013. High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster -Tuned Matched Filter technique and imaging spectrometry. Remote Sensing of Environment, 134, pp.305-318

work page 2013

[4] [3]

and Joshi, S.C., 2020

Foote, M.D., Dennison, P.E., Thorpe, A.K., Thompson, D.R., Jongaramrungruang, S., Frankenberg, C. and Joshi, S.C., 2020. Fast and accurate retrieval of methane concentration from imaging spectrometer data using sparsity prior. IEEE Transactions on Geoscience and Remote Sensing, 58(9), pp.6480-6492

work page 2020

[5] [4]

and Joshi, S.C.,

Foote, M.D., Dennison, P.E., Sullivan, P.R., O'Neill, K.B., Thorpe, A.K., Thompson, D.R., Cusworth, D.H., Duren, R. and Joshi, S.C.,

work page

[6] [5]

and Colombo, R., 2021

Guanter, L., Irakulis -Loitxate, I., Gorroño, J., Sánchez- García, E., Cusworth, D.H., Varon, D.J., Cogliati, S. and Colombo, R., 2021. Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer. Remote Sensing of Environment, 265, p.112671

work page 2021

[7] [6]

and Rossi, F., 2024, July

Ferrari, A., Laneve, G., Pampanoni, V., Carvajal, A. and Rossi, F., 2024, July. Monitoring Methane Emissions from Landfills Using Prisma Imagery. In IGARSS 2024- 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 3663-3667). IEEE

work page 2024

[8] [7]

and Foucher, P.Y., 2021

Nesme, N., Marion, R., Lezeaux, O., Doz, S., Camy -Peyret, C. and Foucher, P.Y., 2021. Joint use of in -scene background radiance estimation and optimal estimation methods for quantifying methane emissions using prisma hyperspectral satellite data: Application to the korpezhe industrial site. Remote Sensing, 13(24), p.4992

work page 2021