the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jun 2022
Variations in land types detected using methane retrieved from space-borne sensor
Abstract. Methane (CH4), a potent greenhouse gas, traps heat in the atmosphere and significantly contributes to global warming. Atmospheric CH4 comes from various natural and anthropogenic sources. CH4 emissions from the decomposition of organic material by bacteria in natural wetlands, other land types, agriculture, and waste management constitute the major component of global emissions. Although there is no clear evidence that CH4 emissions from wetlands and other natural sources have increased substantially in the last decade, uncertainties remain regarding sources and their spatial extent causing discrepancies between emission estimates from inventories/models and estimates inferred by an ensemble of atmospheric inversions. Here we show that satellite-based CH4 total column measurements along with surface albedo from Sentinel-5 Precursor (S-5p) show unique sensitivity to certain land types. Consequently, the areal extent of six land types (marsh, swamp, forest, grassland, cropland, and barren-land) could be identified with high overall accuracy by analysing S-5p data over Canada utilising our classification-segmentation algorithm. Monthly and yearly inventory maps were created, which can be used to validate or complement global models where data from other sources are missing and may help in further constraining the methane budget.
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RC1: 'Referee comment on bg-2022-88', Anonymous Referee #1, 08 Nov 2022
Bhatnagar et al., used the official operational methane data product from Sentinel-5-Presursor (S-5p) to detect land types in Canada using a machine learning algorithm. Their analysis shows (see Abstract) “unique sensitivity to certain land types”. They found (see Abstract) that “the areal extent of six land types (marsh, swamp, forest, grassland, cropland, and barren-land)” can be identified “with high overall accuracy by analysing S-5p data over Canada utilising” their classification-segmentation algorithm. For this purpose, they analysed retrieved methane and retrieved surface albedo individually and in combination. They summarized their results as follow: “Monthly and yearly inventory maps were created, which can be used to validate or complement global models where data from other sources are missing and may help in further constraining the methane budget”.
General:
I am very surprised by this study. I don’t think that the interpretation w.r.t. methane is correct. It is shown in several recent papers that the operational S-5p methane data product suffers from albedo related methane biases, e.g., Barré et al. (2021), Hachmeister et al., (2022), Lorente et al., (2022) explaining, for example, that the locally elevated methane feature discussed in Froitzheim et al., (2021) is a surface albedo related retrieval artifact. The latest version of the scientific retrieval algorithm of SRON (Lorente et al., 2022) and Univ. Bremen (Schneising et al., 2022) are also addressing this albedo (or spectral surface reflectivity related) issue. Bhatnagar et al. are not citing these papers although they are highly relevant for their work. As surface reflectivity related issues are not mentioned in Bhatnagar et al., I assume that they are not aware of this issue.
As a consequence, it appears that Bhatnagar et al. is misinterpreting the albedo related methane bias as a geophysically interesting methane signal, which can be exploited to get land type information. While it may be true that land type information can be obtained by exploiting the albedo related bias (including possibly also real methane variations related to land type dependent methane emissions), I doubt that their results will helps to “further constraining the methane budget” (as written in their Abstract). I see this study as a detailed and interesting investigation of albedo related biases but not as a study that contributes directly to improving our knowledge on methane sources.
I recommend that the authors carefully study the listed references, cite them and modify the paper accordingly (especially the methane related interpretation and conclusions). I also strongly recommend to analyse in addition the latest versions of the two alternative scientific S-5p XCH4 data products, namely the one from SRON (Lorente et al., 2022) and the one Univ. Bremen (Schneising et al., 2022) to find out to what extent the conclusions are robust w.r.t. the used data product. I expect that such an analysis would result in significantly different conclusions.
Specific:
Line 47: Unclear for me why a few km resolution atmospheric data product of a long-lived gas can be used to better define the areal extent of different land use types (compared to few 10 m resolution sensors optimized for land applications).
Line 60 following: The cited reference for the operational algorithm is the pre-launch description and does not reflect the latest version. Please cite also the latest (relevant) ATBD and explicitly mention which version number of the data product has been used.
Line 65 following: The sparse TCCON network does not permit to validate the accuracy of spatial XCH4 maps and, therefore, the listed results in terms of systematic uncertainty may be too optimistic for the application addressed in this publication. I recommend to add this caveat.
Line 93: Please explain “producer accuracy” and “user accuracy”.
Equation (2): Please explain all abbreviations (TP, FN, …).
Line 154: Please explain “kappa value”.
Section 3.3: Please provide a more detailed explanation of the error metric (J, A, O, E) including how the results are to be interpreted when presenting Table 3.
Figure 2: Very nice and informative !
Figures 3, 5, 6: Please explain better the various curves shown in Figure 3 (how have they been computed, what do they show, interpretation for the purpose of the presented study; I recommend to use one or two cases (e.g., BOG and GRASSLAND) to explain as clearly as possible).
Typos etc.:
Line 50: Replace S5 by S-5p.
References:
Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., Engelen, R., Inness, A., Lorente, A., McNorton, J., Peuch, V.-H., Radnoti, G., and Ribas, R.: Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts, Atmos. Chem. Phys., 21, 5117–5136, https://doi.org/10.5194/acp-21-5117-2021, 2021.
Froitzheim, N., Majka, J., and Zastrozhnov, D.: Methane release from carbonate rock formations in the Siberian permafrost area during and after the 2020 heat wave, P. Natl. Acad. Sci. USA, 118, e2107632118, https://doi.org/10.1073/pnas.2107632118, 2021.
Hachmeister, J., Schneising, O., Buchwitz, M., Lorente, A., Borsdorff, T., Burrows, J. P., Notholt, J., and Buschmann, M.: On the influence of underlying elevation data on Sentinel-5 Precursor satellite methane retrievals over Greenland, Atmos. Meas. Tech., 15, 4063–4074, https://doi.org/10.5194/amt-15-4063-2022, 2022.
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., and Landgraf, J.: Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021.
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., and Landgraf, J.: Accounting for surface reflectance spectral features in TROPOMI methane retrievals, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-255, in review, 2022.
Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving methane and carbon monoxide from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-258, in review, 2022.
Citation: https://doi.org/10.5194/bg-2022-88-RC1 - AC1: 'Reply on RC1', Mahesh Kumar Sha, 25 Jul 2023
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EC1: 'Comment on bg-2022-88', Jamie Shutler, 31 Jan 2023
Editor comment, 30 January 2023
I read your paper with interest as it is excellent to see satellite column integrated gas observations being used within a biogeoscience study. I have had significant issues in identifying reviewers for your manuscript, having invited 21 reviewers, 4 of which accepted, but then only 1 reviewer submitted a report. Hence I am now submitting this editor comment so that we can allow this review process to proceed. I realise that I have previously reviewed your paper prior to its publication within the discussion forum and that you revised your work addressing my earlier comments. So my comments below focus mainly on the major points raised by the single reviewer.
Its clear from the reviewer’s comment that your manuscript has suffered from some unfortunate timing in relation to your analysis and then the subsequent release of an updated Sentinel 5P methane dataset. The production of this revised Sentinel 5P methane dataset was triggered by an error (regional bias) that was identified within these data (as presented most recently within Lorente et al., 2022, but also studied within the three other references identified by the reviewer). And it appears that this bias likely forms part of the signal identified within your analysis and manuscript. And you have (not surprisingly) attributed the signal to a change in the natural system, whereas it seems highly likely that at least a part of the signal you identify is due to the error within the Sentinel 5P methane data dataset. The updates and changes in this underlying Sentinel 5P dataset are likely to significantly impact your results and therefore the conclusions from your work are also likely to change.
In light of this, its clear that you should at least repeat your analysis using the updated datasets (i.e. those provided by the reviewer) and then revise your manuscript following the results of this new analysis. I therefore conclude that major revisions are required.
You can re-submit your analysis that use the most recent datasets, revise your conclusions and you may have to revise your paper title. If you choose to perform these major revisions you will need to make sure that you fully account for the new revised Sentinel 5P data along with the associated data uncertainties and make sure that you show how these uncertainties likely impact your results. This will help to illustrate how robust your findings are to the underlying uncertainties of the Sentinel 5P dataset. This issue of unfortunate timing highlights the need to include the data version numbers and sources for all data (so authors can trace which datasets were used) so please make sure you include this information within your revised work.
Reference
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., and Landgraf, J.: Accounting for surface reflectance spectral features in TROPOMI methane retrievals, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-255, in review, 2022.
Citation: https://doi.org/10.5194/bg-2022-88-EC1 - AC2: 'Reply on EC1', Mahesh Kumar Sha, 25 Jul 2023
Status: closed
-
RC1: 'Referee comment on bg-2022-88', Anonymous Referee #1, 08 Nov 2022
Bhatnagar et al., used the official operational methane data product from Sentinel-5-Presursor (S-5p) to detect land types in Canada using a machine learning algorithm. Their analysis shows (see Abstract) “unique sensitivity to certain land types”. They found (see Abstract) that “the areal extent of six land types (marsh, swamp, forest, grassland, cropland, and barren-land)” can be identified “with high overall accuracy by analysing S-5p data over Canada utilising” their classification-segmentation algorithm. For this purpose, they analysed retrieved methane and retrieved surface albedo individually and in combination. They summarized their results as follow: “Monthly and yearly inventory maps were created, which can be used to validate or complement global models where data from other sources are missing and may help in further constraining the methane budget”.
General:
I am very surprised by this study. I don’t think that the interpretation w.r.t. methane is correct. It is shown in several recent papers that the operational S-5p methane data product suffers from albedo related methane biases, e.g., Barré et al. (2021), Hachmeister et al., (2022), Lorente et al., (2022) explaining, for example, that the locally elevated methane feature discussed in Froitzheim et al., (2021) is a surface albedo related retrieval artifact. The latest version of the scientific retrieval algorithm of SRON (Lorente et al., 2022) and Univ. Bremen (Schneising et al., 2022) are also addressing this albedo (or spectral surface reflectivity related) issue. Bhatnagar et al. are not citing these papers although they are highly relevant for their work. As surface reflectivity related issues are not mentioned in Bhatnagar et al., I assume that they are not aware of this issue.
As a consequence, it appears that Bhatnagar et al. is misinterpreting the albedo related methane bias as a geophysically interesting methane signal, which can be exploited to get land type information. While it may be true that land type information can be obtained by exploiting the albedo related bias (including possibly also real methane variations related to land type dependent methane emissions), I doubt that their results will helps to “further constraining the methane budget” (as written in their Abstract). I see this study as a detailed and interesting investigation of albedo related biases but not as a study that contributes directly to improving our knowledge on methane sources.
I recommend that the authors carefully study the listed references, cite them and modify the paper accordingly (especially the methane related interpretation and conclusions). I also strongly recommend to analyse in addition the latest versions of the two alternative scientific S-5p XCH4 data products, namely the one from SRON (Lorente et al., 2022) and the one Univ. Bremen (Schneising et al., 2022) to find out to what extent the conclusions are robust w.r.t. the used data product. I expect that such an analysis would result in significantly different conclusions.
Specific:
Line 47: Unclear for me why a few km resolution atmospheric data product of a long-lived gas can be used to better define the areal extent of different land use types (compared to few 10 m resolution sensors optimized for land applications).
Line 60 following: The cited reference for the operational algorithm is the pre-launch description and does not reflect the latest version. Please cite also the latest (relevant) ATBD and explicitly mention which version number of the data product has been used.
Line 65 following: The sparse TCCON network does not permit to validate the accuracy of spatial XCH4 maps and, therefore, the listed results in terms of systematic uncertainty may be too optimistic for the application addressed in this publication. I recommend to add this caveat.
Line 93: Please explain “producer accuracy” and “user accuracy”.
Equation (2): Please explain all abbreviations (TP, FN, …).
Line 154: Please explain “kappa value”.
Section 3.3: Please provide a more detailed explanation of the error metric (J, A, O, E) including how the results are to be interpreted when presenting Table 3.
Figure 2: Very nice and informative !
Figures 3, 5, 6: Please explain better the various curves shown in Figure 3 (how have they been computed, what do they show, interpretation for the purpose of the presented study; I recommend to use one or two cases (e.g., BOG and GRASSLAND) to explain as clearly as possible).
Typos etc.:
Line 50: Replace S5 by S-5p.
References:
Barré, J., Aben, I., Agustí-Panareda, A., Balsamo, G., Bousserez, N., Dueben, P., Engelen, R., Inness, A., Lorente, A., McNorton, J., Peuch, V.-H., Radnoti, G., and Ribas, R.: Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts, Atmos. Chem. Phys., 21, 5117–5136, https://doi.org/10.5194/acp-21-5117-2021, 2021.
Froitzheim, N., Majka, J., and Zastrozhnov, D.: Methane release from carbonate rock formations in the Siberian permafrost area during and after the 2020 heat wave, P. Natl. Acad. Sci. USA, 118, e2107632118, https://doi.org/10.1073/pnas.2107632118, 2021.
Hachmeister, J., Schneising, O., Buchwitz, M., Lorente, A., Borsdorff, T., Burrows, J. P., Notholt, J., and Buschmann, M.: On the influence of underlying elevation data on Sentinel-5 Precursor satellite methane retrievals over Greenland, Atmos. Meas. Tech., 15, 4063–4074, https://doi.org/10.5194/amt-15-4063-2022, 2022.
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., aan de Brugh, J., Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi, K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke, T., and Landgraf, J.: Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021.
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., and Landgraf, J.: Accounting for surface reflectance spectral features in TROPOMI methane retrievals, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-255, in review, 2022.
Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving methane and carbon monoxide from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-258, in review, 2022.
Citation: https://doi.org/10.5194/bg-2022-88-RC1 - AC1: 'Reply on RC1', Mahesh Kumar Sha, 25 Jul 2023
-
EC1: 'Comment on bg-2022-88', Jamie Shutler, 31 Jan 2023
Editor comment, 30 January 2023
I read your paper with interest as it is excellent to see satellite column integrated gas observations being used within a biogeoscience study. I have had significant issues in identifying reviewers for your manuscript, having invited 21 reviewers, 4 of which accepted, but then only 1 reviewer submitted a report. Hence I am now submitting this editor comment so that we can allow this review process to proceed. I realise that I have previously reviewed your paper prior to its publication within the discussion forum and that you revised your work addressing my earlier comments. So my comments below focus mainly on the major points raised by the single reviewer.
Its clear from the reviewer’s comment that your manuscript has suffered from some unfortunate timing in relation to your analysis and then the subsequent release of an updated Sentinel 5P methane dataset. The production of this revised Sentinel 5P methane dataset was triggered by an error (regional bias) that was identified within these data (as presented most recently within Lorente et al., 2022, but also studied within the three other references identified by the reviewer). And it appears that this bias likely forms part of the signal identified within your analysis and manuscript. And you have (not surprisingly) attributed the signal to a change in the natural system, whereas it seems highly likely that at least a part of the signal you identify is due to the error within the Sentinel 5P methane data dataset. The updates and changes in this underlying Sentinel 5P dataset are likely to significantly impact your results and therefore the conclusions from your work are also likely to change.
In light of this, its clear that you should at least repeat your analysis using the updated datasets (i.e. those provided by the reviewer) and then revise your manuscript following the results of this new analysis. I therefore conclude that major revisions are required.
You can re-submit your analysis that use the most recent datasets, revise your conclusions and you may have to revise your paper title. If you choose to perform these major revisions you will need to make sure that you fully account for the new revised Sentinel 5P data along with the associated data uncertainties and make sure that you show how these uncertainties likely impact your results. This will help to illustrate how robust your findings are to the underlying uncertainties of the Sentinel 5P dataset. This issue of unfortunate timing highlights the need to include the data version numbers and sources for all data (so authors can trace which datasets were used) so please make sure you include this information within your revised work.
Reference
Lorente, A., Borsdorff, T., Martinez-Velarte, M. C., and Landgraf, J.: Accounting for surface reflectance spectral features in TROPOMI methane retrievals, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-255, in review, 2022.
Citation: https://doi.org/10.5194/bg-2022-88-EC1 - AC2: 'Reply on EC1', Mahesh Kumar Sha, 25 Jul 2023
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