Automatic Methane Plume Masking Based on Wavelet Transform Image Processing: Application to MethaneAIR and MethaneSAT data

Zhang, Zhan; Sargent, Maryann; Warren, Jack D.; Chulakadabba, Apisada; Russi, Marcus; Ayvazov, Sasha; Benmergui, Joshua; Knapp, Marvin; Kyzivat, Ethan; Miller, Christopher C.; Roche, Sébastien; Luo, Bingkun; Miller, David J.; Nasr, Maya; Sun, Kang; Williams, James P.; MacKay, Katlyn; Omara, Mark; Guanter, Luis; Gautam, Ritesh; Franklin, Jonathan; Liu, Xiong; Wofsy, Steven C.

doi:10.5194/egusphere-2026-141

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https://doi.org/10.5194/egusphere-2026-141

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30 Jan 2026

| 30 Jan 2026

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Automatic Methane Plume Masking Based on Wavelet Transform Image Processing: Application to MethaneAIR and MethaneSAT data

Zhan Zhang, Maryann Sargent, Jack D. Warren, Apisada Chulakadabba, Marcus Russi, Sasha Ayvazov, Joshua Benmergui, Marvin Knapp, Ethan Kyzivat, Christopher C. Miller, Sébastien Roche, Bingkun Luo, David J. Miller, Maya Nasr, Kang Sun, James P. Williams, Katlyn MacKay, Mark Omara, Luis Guanter, Ritesh Gautam, Jonathan Franklin, Xiong Liu, and Steven C. Wofsy

Abstract. Accurate and efficient plume masking is essential for remote sensing-based detection and quantification of methane and other point source emissions, as plume masks are critical not only for quantifying emission rates, but also for visualization and source localization. However, plume masking relies largely on human operation when the retrieved plume concentrations are weak relative to the background, which hinders the automatic plume detection. This study presents an automatic plume masking method based on wavelet transform image processing. Given a methane concentration enhancement image with no prior knowledge of source locations, a 2D discrete wavelet transform is applied to enhance plume signals while suppressing background noise. The binary plume masks are then generated and filtered using criteria such as concentration, plume shape, and wind direction. The method includes tunable parameters to ensure high detection accuracy under varying background and meteorological conditions. This method detected 60 % more plumes, mainly with lower fluxes, than a thresholding method from both MethaneAIR and MethaneSAT data, while finding fewer false positives, proving its potential to realize automatic plume detection across platforms at different scales and resolutions. Its high sensitivity to low-volume emissions also enables a lower detection limit and provides a more comprehensive emission rate distribution. Compared to machine learning models, this method is computationally efficient and does not require training data. Although designed for MethaneSAT purposes, this method is broadly applicable for plume detection from concentration imagery on various airborne and spaceborne remote sensing platforms and for numerous atmospheric species.

How to cite. Zhang, Z., Sargent, M., Warren, J. D., Chulakadabba, A., Russi, M., Ayvazov, S., Benmergui, J., Knapp, M., Kyzivat, E., Miller, C. C., Roche, S., Luo, B., Miller, D. J., Nasr, M., Sun, K., Williams, J. P., MacKay, K., Omara, M., Guanter, L., Gautam, R., Franklin, J., Liu, X., and Wofsy, S. C.: Automatic Methane Plume Masking Based on Wavelet Transform Image Processing: Application to MethaneAIR and MethaneSAT data, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-141, 2026.

Received: 10 Jan 2026 – Discussion started: 30 Jan 2026

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RC1:
'Comment on egusphere-2026-141', Sandro Meier, 17 Feb 2026 reply
Summary
The authors introduce an automated method to detect methane plumes in satellite and airborne imagery using a 2D wavelet transform to suppress background noise and enhance the detectability of methane plumes, especially from sources with lower emissions. With their approach, the authors claim to improve previous detection methods such as thresholding, statistical tests etc., which often miss weak plumes, or machine-learning based plume detection, which avoids the cost and training requirements of ML methods. They state to show the first implementation of wavelet-based denoising for atmospheric studies. In their processing pipeline, CH4 concentration maps are denoised using 2D wavelets, plumes are identified using thresholding and the detected plumes are filtered using concentration patterns, plume shape, and wind direction. Lastly, emissions are quantified with a previously used divergence-integral method.
Tests on airborne data from MethaneAIR and spaceborne data from MethaneSAT show that the method detects about 60% more plumes than conventional thresholding, while producing fewer false positives. As the newly detected plumes mostly originate from weaker sources, the new method essentially decreases the detection limit of the sensors. The authors state that the limitations of their methods are diffuse emissions with low methane enhancements and atypical shape as well as overlapping plumes. Therefore, some manual checking is still needed with their approach.

General comments
Overall, the manuscript is well written and addresses an important constraint in trace gas remote sensing: Plume detection and emission quantification often require extensive manual input and can therefore be time consuming. Another key strength of the study is also that data from both a spaceborne and airborne instrument are used which demonstrates that their approach has the potential to be widely applicable.
The novelty implied by the title (“automatic methane plume masking”) appears to be somewhat overstated. The plume detection approach is largely consistent with existing threshold-based methods used in prior studies (see line 130), followed by standard filtering based on plume morphology and wind direction. The primary methodological difference appears to be the inclusion of a denoising step prior to plume detection. Therefore, the claim in the title does not align well with the content of the manuscript and its key strength: Using wavelet denoising for atmospheric (hyperspectral) data which is also what the authors state on line 96. Therefore, the authors should either put a focus on how a detection pipeline should be set up to function in a (semi-)automated way (e.g., 1. apply any denoising method, 2. Filter the detected plumes using the described methods, 3. Quantify emissions with any method) or focus on the effect of denoising on detection limits and emission quantification for given sensors.
When it comes to denoising, the authors use the well-established 2D wavelet transform for image denoising. As previous studies have shown, this method is also viable for hyperspectral air- and spaceborne data (e.g., in Rasti et al., 2018, https://www.mdpi.com/2072-4292/10/3/482). However, these studies also show that for hyperspectral images (i.e. also MethaneAIR and MethaneSAT) there are more advanced methods than a 2D wavelet transform, such as 3D wavelet or HyRes (e.g., https://ieeexplore.ieee.org/document/8098642); especially since these methods benefit from redundancy of information in the spectral bands.
Therefore, the authors should address the following aspects:
The study uses CH4 maps derived from hyperspectral data. As hyperspectral data contains much more redundancy of information in the spectral bands, which is beneficial for denoising, the authors should either motivate why they apply the denoising only to the CH4 maps or use a more sophisticated denoising method such as 3D wavelets or HyRes and compare it to the current results using 2D wavelets.

The goal of remote sensing of trace gases is not only to detect sources but also to quantify their emissions. Therefore, the authors should also quantify the impact of the proposed denoising approach (including hyperparameter choices) on the resulting emission estimates and detection limits. Since this requires access to ground truth, a suitable approach would be to use synthetic plume images and add noise with different characteristics (e.g., Gaussian and non-Gaussian). This would allow a systematic assessment of whether and to what extent the denoising step biases or alters the inferred emission rates.

The described denoising effectively improves the detection limit of the instruments. Therefore, it would be good to analyse the effect of denoising on the detection limits.

Specific comments
L11ff: How much lower is the effective detection limit with your method?

L12: There are many more disadvantages of ML methods that could be listed here like the lack of uncertainty propagation, regression to the mean, often unrealistic training data etc.

L13 & L70: What are "MethaneSAT purposes"?

L35ff: One important paper to cite would also be Kuhlmann et al., 2024 (https://gmd.copernicus.org/articles/17/4773/2024/)

L73 & 85: please indicate the along and across-track resolution

L97: As far as I can follow, the pre-processing step was not part of the processing in Hüpfel et al., (2021). Please provide more details about why this is needed in the current study but was not in Hüpfel et al.

L99: What you are doing here is basically the 99% confidence interval. Please make this clearer to the reader.

L104: The authors state that the hyperparameters "were chosen to maximize plume detections while minimizing false positives”. This risks overfitting to the evaluation dataset (e.g., similar sensor properties, surface characteristics etc.). Therefore, this issue should either be addressed or discussed.

L122: How did you arrive at this value for L? Did you perform sensitivity analysis? How do the estimated emissions change when changing L? It would be good to include this in your sensitivity analysis.

L124: Please explain why this additional denoising needed if your previous wavelet transform is supposed to filter out the high frequency noise (L118)? Especially since this step was not performed in Hüpfel et al., (2021).

L149: Please provide values of high and low ratios. Also provide the thresholds used.

L165ff: At the scale of MethaneSAT, using a coarsely resolved wind data product such as HRRR or GFS might work well, especially if there is little topography as it was the case for the data presented. However, at smaller spatial scales (such as for MethaneAIR) and in more complex terrain, filtering your data based on wind direction might become problematic as the influence of turbulence grows larger (see e.g., https://amt.copernicus.org/articles/19/333/2026/). Please discuss this limitation in the discussion section.

L188: Please explain which wind speed product you used for the emission quantification (e.g., 10m wind speed, PBL average, effective wind speed and how you calculated it.)

L235ff: Please be more concrete about the performance of your algorithm: How many low concentration plumes were filtered out and how many remained?

L263: You could also mention that the longer the plumes, the more the assumption of steady wind conditions are violated

L274ff: Please elaborate why the application of the wavelet transform to subregions would improve the sensitivity of your method.

L285: Please be more concrete about the performance of your method: Add numbers to the text or insert a confusion matrix or a summarising table that show how well your method performed compared to the previous results (e.g., TP, FP, FN, detection limits etc.)

Technical corrections
L6 & 92 & 214 & 253 etc.: "Plume signals" sounds a bit off. Use plumes or CH4 enhancements instead.

L14 & 45: "concentration imagery" sounds a bit off. Use concentration maps or CH4 enhancement maps instead.

L33: Not "plume detection methods" but "plume detection"

In my opinion, Figure 1 is quite unintuitive. Please set the label as titles, add colourbars below each subplot, make the figure caption more descriptive and separate the upper and lower row as the lower row is only supposed to show the processing used to obtain figure (c).

Please fix the tick labels in Figure 2, remove the note from the figure and put it into the figure caption, remove the text in the upper right and set it as title

L221: Please use a different expression, e.g. homogeneity

The plume outlines in Figure 6 are hardly visible for people with colour vision deficiency (e.g., with Achromatopsia, see https://www.color-blindness.com/coblis-color-blindness-simulator/). Please use more suitable colour.

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Citation: https://doi.org/10.5194/egusphere-2026-141-RC1

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Short summary

Accurate plume identification from satellite imagery is important for measuring methane emissions, but weak signals often require time-consuming human inspection. We developed an automatic plume masking method that enhances plume patterns while reducing background noise based on wavelet transform image processing. The method finds more small emission sources with fewer false detections and works efficiently across different instruments, helping build a more complete picture of methane emissions.


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