the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 May 2025
Assessing the Detection of Methane Plumes in Offshore Areas Using High-Resolution Imaging Spectrometers
Abstract. The offshore oil and gas industry is an important contributor to global anthropogenic methane emissions. Satellite-based, high-resolution imaging spectrometers are showing a great potential for the detection of methane emissions over land. However, the use of the same methods over offshore oil and gas extraction basins is challenged by the low reflectance of water in the near- and shortwave infrared spectral windows used for methane retrievals. This limitation can be partly alleviated by data acquisitions under the so-called sun glint configuration, which enhances the at-sensor radiance. In this work, we assess the performance of two space-based imaging spectrometers, EnMAP and EMIT, for the detection of offshore methane plumes. We use simulated plumes to generate parametric probability of detection (POD) models for a range of emission flux rates (Q), at-sensor radiances and wind speeds. The POD models were confronted with real plume detections for the two instruments. Our analysis shows that the spatial resolution of the instrument and the at-sensor radiance (which drives the retrieval precision) are the two factors with the greatest impact on plume POD. We also evaluate the dependence of the at-sensor radiance on the illumination-observation geometry and the surface roughness. Our POD models properly represent the different trade-offs between spatial resolution and retrieval precision in EnMAP and EMIT. As an example, for most combinations of Q and wind speed values at POD = 50 %, EMIT demonstrates better detection performance at Q > 7 t/h, whereas EnMAP performs better at Q < 7 t/h. This study demonstrates the ability of these two satellite instruments to detect high-emitting offshore point sources under a range of different conditions. By filtering data based on these conditions, methane emission detection and monitoring efforts can be optimized, reducing unnecessary searches and ultimately increasing the action taken on these emissions.
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RC1: 'Comment on egusphere-2025-1917', Anonymous Referee #1, 29 Jun 2025
Dear Authors,
Thank you for the opportunity to review your manuscript « Assessing the Detection of Methane Plumes in Offshore Areas Using High-Resolution Imaging Spectrometers ». This work studies the parameters that intervene in the detection of methane plumes in offshore areas. It also introduces a method to estimate the probability of detection of a given plume depending on the wind speed, the emission source rate and at-sensor radiances. The study is carried on simulated data but also real plume data mainly from EnMAP and EMIT. The manuscript is well written and provides in depth details on the methodology and the data used in the experiments.
I suggest some minor clarifications and corrections to be considered before I can recommend this work for publication.
L78 - The work presented here strongly relies on the quality of the retrieval method. What is the magnitude of the retrieval error? In particular, how does the retrieval error evolve with respect to the source rate? Please clarify this in the text. This is an important discussion as a high retrieval error could mean that some plumes might be detected with a more precise retrieval method for example. This directly impacts the estimation of Qmin.
L82 - It is common to measure the methane excess in ppm, however the total amount of gas also depends on the height of the column considered when estimating the concentration. Please add in the text the height of the column used for the retrieval. It will clarify the scale of Figure 6.
L140 - Is only one band considered or a combination of bands?
L170 - How are the simulations included in the L1 acquisitions? In particular, how is chosen the plume location in the image ? Is the source placed where the sun glint is the most pronounced? What happens if only the tail of the plume is visible where there is sun glint?
L189 - I appreciate that the authors acknowledged the limitations due to the dataset size. However, I believe that the biases linked to the small dataset size should be more extensively discussed. It is unlikely that 25 plumes can accurately represent all the possible scenarii for a given wind speed. However, this could be mitigated by a wise selection of the 25 plumes. Were the 25 plumes per wind speed sampled randomly or regularly during the LES procedure? What is the correlation between the samples? It might be interesting to compare the correlation between samples and their associated Qmin. To know if 25 samples are sufficient to estimate a POD I suggest to display the complete distribution of Qmin for different wind speeds.
L184 - This means that a Qmin distribution is sampled for a fixed background. It could be interesting to estimate a Qmin distribution using several background with close Rad values. It will enhance the robustness of the Qmin estimate.
L197 - In Eq.6 the parameters a and b are already used in Eq 2 with a different meaning. Please change those notations (or those in Eq 2).
L233 - In Fig 9, please clarify what the normalized NEdL curve brings to the figure.
L284 - The parameters a,b,c in Eq.7 are already used in Eq.2 and Eq.6 with a different meaning. Please change those notations.
Citation: https://doi.org/10.5194/egusphere-2025-1917-RC1 -
AC1: 'Reply on RC1', Javier Roger, 03 Sep 2025
Thank you for the careful review of the manuscript. The main changes performed on the manuscript have been:
- Keep only those simulation datasets with a minimum time difference between adjacent plumes of 60 s. Then, re-calculate the POD models.
In addition to those major changes, a number of corrections and clarifications have been made throughout the text regarding other major and minor concerns. Please, find attached in a pdf file point-by-point responses (blue) to your comments and suggestions.
Kind regards,
Javier Roger, on behalf of the authors
-
AC1: 'Reply on RC1', Javier Roger, 03 Sep 2025
-
RC2: 'Comment on egusphere-2025-1917', Anonymous Referee #2, 22 Aug 2025
Satellite-based quantification of methane emissions in offshore areas continues to face challenges due to the interference of water vapor. This work by Roger et al. proposes a parametric probability of detection model (POD) to compare the performance of two hyperspectral instruments, EnMAP and EMIT, in detecting offshore methane emissions. The results present the effective application scenarios for different hyperspectral instruments, enhancing our understanding of the limitations and complementarity from different satellite instrument in quantifying methane emissions in offshore areas. The paper is well structured and written. I recommend making a few minor additions and corrections before this paper can be published.
- Does XCH4 refer to the methane column concentration enhancement in Line 78?
- What are the errors of retrieval methane concentration from different instruments? How to consider the impact of background concentration on methane plume detection in Line 83?
- How to consider the uncertainty of wind fields simulated by WRF-LES model in Line 106?
- Why the U10 is not obtained from the WRF in Line 111? not consistent with the Ueff.
- In Eq.6 and Eq.7, are the parameters same for the EnMAP and EMIT instruments?
Citation: https://doi.org/10.5194/egusphere-2025-1917-RC2 -
AC2: 'Reply on RC2', Javier Roger, 03 Sep 2025
Thank you for the careful review of the manuscript. A number of corrections and clarifications have been made throughout the text regarding some major and minor concerns. Please, find attached in a pdf file point-by-point responses (blue) to your comments and suggestions.
Kind regards,
Javier Roger, on behalf of the authors
Status: closed
-
RC1: 'Comment on egusphere-2025-1917', Anonymous Referee #1, 29 Jun 2025
Dear Authors,
Thank you for the opportunity to review your manuscript « Assessing the Detection of Methane Plumes in Offshore Areas Using High-Resolution Imaging Spectrometers ». This work studies the parameters that intervene in the detection of methane plumes in offshore areas. It also introduces a method to estimate the probability of detection of a given plume depending on the wind speed, the emission source rate and at-sensor radiances. The study is carried on simulated data but also real plume data mainly from EnMAP and EMIT. The manuscript is well written and provides in depth details on the methodology and the data used in the experiments.
I suggest some minor clarifications and corrections to be considered before I can recommend this work for publication.
L78 - The work presented here strongly relies on the quality of the retrieval method. What is the magnitude of the retrieval error? In particular, how does the retrieval error evolve with respect to the source rate? Please clarify this in the text. This is an important discussion as a high retrieval error could mean that some plumes might be detected with a more precise retrieval method for example. This directly impacts the estimation of Qmin.
L82 - It is common to measure the methane excess in ppm, however the total amount of gas also depends on the height of the column considered when estimating the concentration. Please add in the text the height of the column used for the retrieval. It will clarify the scale of Figure 6.
L140 - Is only one band considered or a combination of bands?
L170 - How are the simulations included in the L1 acquisitions? In particular, how is chosen the plume location in the image ? Is the source placed where the sun glint is the most pronounced? What happens if only the tail of the plume is visible where there is sun glint?
L189 - I appreciate that the authors acknowledged the limitations due to the dataset size. However, I believe that the biases linked to the small dataset size should be more extensively discussed. It is unlikely that 25 plumes can accurately represent all the possible scenarii for a given wind speed. However, this could be mitigated by a wise selection of the 25 plumes. Were the 25 plumes per wind speed sampled randomly or regularly during the LES procedure? What is the correlation between the samples? It might be interesting to compare the correlation between samples and their associated Qmin. To know if 25 samples are sufficient to estimate a POD I suggest to display the complete distribution of Qmin for different wind speeds.
L184 - This means that a Qmin distribution is sampled for a fixed background. It could be interesting to estimate a Qmin distribution using several background with close Rad values. It will enhance the robustness of the Qmin estimate.
L197 - In Eq.6 the parameters a and b are already used in Eq 2 with a different meaning. Please change those notations (or those in Eq 2).
L233 - In Fig 9, please clarify what the normalized NEdL curve brings to the figure.
L284 - The parameters a,b,c in Eq.7 are already used in Eq.2 and Eq.6 with a different meaning. Please change those notations.
Citation: https://doi.org/10.5194/egusphere-2025-1917-RC1 -
AC1: 'Reply on RC1', Javier Roger, 03 Sep 2025
Thank you for the careful review of the manuscript. The main changes performed on the manuscript have been:
- Keep only those simulation datasets with a minimum time difference between adjacent plumes of 60 s. Then, re-calculate the POD models.
In addition to those major changes, a number of corrections and clarifications have been made throughout the text regarding other major and minor concerns. Please, find attached in a pdf file point-by-point responses (blue) to your comments and suggestions.
Kind regards,
Javier Roger, on behalf of the authors
-
AC1: 'Reply on RC1', Javier Roger, 03 Sep 2025
-
RC2: 'Comment on egusphere-2025-1917', Anonymous Referee #2, 22 Aug 2025
Satellite-based quantification of methane emissions in offshore areas continues to face challenges due to the interference of water vapor. This work by Roger et al. proposes a parametric probability of detection model (POD) to compare the performance of two hyperspectral instruments, EnMAP and EMIT, in detecting offshore methane emissions. The results present the effective application scenarios for different hyperspectral instruments, enhancing our understanding of the limitations and complementarity from different satellite instrument in quantifying methane emissions in offshore areas. The paper is well structured and written. I recommend making a few minor additions and corrections before this paper can be published.
- Does XCH4 refer to the methane column concentration enhancement in Line 78?
- What are the errors of retrieval methane concentration from different instruments? How to consider the impact of background concentration on methane plume detection in Line 83?
- How to consider the uncertainty of wind fields simulated by WRF-LES model in Line 106?
- Why the U10 is not obtained from the WRF in Line 111? not consistent with the Ueff.
- In Eq.6 and Eq.7, are the parameters same for the EnMAP and EMIT instruments?
Citation: https://doi.org/10.5194/egusphere-2025-1917-RC2 -
AC2: 'Reply on RC2', Javier Roger, 03 Sep 2025
Thank you for the careful review of the manuscript. A number of corrections and clarifications have been made throughout the text regarding some major and minor concerns. Please, find attached in a pdf file point-by-point responses (blue) to your comments and suggestions.
Kind regards,
Javier Roger, on behalf of the authors
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