Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Single-blind test of nine methane-sensing satellite systems from three continents
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
present address: Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
Sahar H. El Abbadi
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
present address: Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
Philippine M. Burdeau
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Zhan Zhang
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Zhenlin Chen
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Jeffrey S. Rutherford
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
present address: Highwood Emissions Management, Calgary, Alberta T2P 2V1, Canada
Yuanlei Chen
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
Adam R. Brandt
Department of Energy Science & Engineering, Stanford University, Stanford, California 94305, United States
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Cited articles
Bell, C., Rutherford, J., Brandt, A., Sherwin, E., Vaughn, T., and Zimmerle, D.: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR, Elementa, 10, 80, https://doi.org/10.1525/elementa.2022.00080, 2022.
Bell, C., Ilonze, C., Duggan, A., and Zimmerle, D.: Performance of Continuous Emission Monitoring Solutions under a Single-Blind Controlled Testing Protocol, Environ. Sci. Technol., 57, 5794–5805, https://doi.org/10.1021/acs.est.2c09235, 2023.
Bell, C. S., Vaughn, T. L., Zimmerle, D., Herndon, S. C., Yacovitch, T. I., Heath, G. A., Pétron, G., Edie, R., Field, R. A., Murphy, S. M., Robertson, A. M., and Soltis, J.: Comparison of methane emission estimates from multiple measurement techniques at natural gas production pads, Elementa, 5, 79, https://doi.org/10.1525/elementa.266, 2017.
Bell, C. S., Vaughn, T., and Zimmerle, D.: Evaluation of next generation emission measurement technologies under repeatable test protocols, Elementa, 8, 32, https://doi.org/10.1525/elementa.426, 2020.
Chen, Y., Sherwin, E. D., Berman, E. S. F., Jones, B. B., Gordon, M. P., Wetherley, E. B., Kort, E. A., and Brandt, A. R.: Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey, Environ. Sci. Technol., 56, 4317–4323, https://doi.org/10.1021/acs.est.1c06458, 2022.
Chulakadabba, A., Sargent, M., Lauvaux, T., Benmergui, J. S., Franklin, J. E., Chan Miller, C., Wilzewski, J. S., Roche, S., Conway, E., Souri, A. H., Sun, K., Luo, B., Hawthrone, J., Samra, J., Daube, B. C., Liu, X., Chance, K., Li, Y., Gautam, R., Omara, M., Rutherford, J. S., Sherwin, E. D., Brandt, A., and Wofsy, S. C.: Methane point source quantification using MethaneAIR: a new airborne imaging spectrometer, Atmos. Meas. Tech., 16, 5771–5785, https://doi.org/10.5194/amt-16-5771-2023, 2023.
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Olson-Duvall, W., Heckler, J., Chapman, J. W., Eastwood, M. L., Helmlinger, M. C., Green, R. O., Asner, G. P., Dennison, P. E., and Miller, C. E.: Intermittency of Large Methane Emitters in the Permian Basin, Environ. Sci. Tech. Let., 8, 567–573, https://doi.org/10.1021/acs.estlett.1c00173, 2021.
Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Yadav, V., Chapman, J. W., Eastwood, M. L., Green, R. O., Hmiel, B., Lyon, D. R., and Duren, R. M.: Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States, P. Natl. Acad. Sci. USA, 119, e2202338119, https://doi.org/10.1073/pnas.2202338119, 2022.
Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K., Frankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E., Green, R. O., and Miller, C. E.: California's methane super-emitters, Nature, 575, 180–184, https://doi.org/10.1038/s41586-019-1720-3, 2019.
El Abbadi, S. and esherwin: sahar-elabbadi/SU-Controlled-Releases-2022: First release (publish), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10149992, 2023.
El Abbadi, S., Chen, Z., Burdeau, P., Rutherford, J., Chen, Y., Zhang, Z., Sherwin, E., and Brandt, A.: Comprehensive evaluation of aircraft-based methane sensing for greenhouse gas mitigation, Engineering, Earth Arxiv [preprint], https://doi.org/10.31223/X51D4C, 2023.
EnMAP: EnMAP: Mission, EnMAP, EnMAP, https://www.enmap.org/mission/ (last access: 24 May 2023), 2023.
EPA: Standards of Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review, 40 CFR Part 60, 87, Docket Numbers: EPA-HQ-OAR-2021-0317, FRL-8510-04-OAR, https://www.federalregister.gov/documents/2022/12/06/2022-24675/standards-of-performance-for-new-reconstructed-and-modified-sources-and-emissions-guidelines-for Agency (last access: 30 March 2023), 2022.
EPA: Greenhouse Gas Reporting Rule: Revisions and Confidentiality Determinations for Petroleum and Natural Gas Systems, 40 CFR Part 98, 88, https://www.govinfo.gov/content/pkg/FR-2023-08-01/pdf/2023-14338.pdf (last access: 29 September 2023), 2023.
ESA: eoPortal: PRISMA (Hyperspectral), European Space Agency, Paris, France, https://www.eoportal.org/satellite-missions/prisma-hyperspectral#launch (last access: 29 September 2023), 2012.
ESA: Sentinel-2, European Space Agency, Paris, France, https://sentinel.esa.int/web/sentinel/missions/sentinel-2 (last access: 21 February 2022), 2021a.
ESA: Sentinel-5P, European Space Agency, Paris, France, https://sentinel.esa.int/web/sentinel/missions/sentinel-5p (last access: 22 March 2022), 2021b.
ESA: About GHGSat, European Space Agency, Paris, France, https://earth.esa.int/eogateway/missions/ghgsat (last access: 21 February 2022), 2022a.
ESA: Earth Online: Worldview-3, European Space Agency, Paris, France, https://earth.esa.int/eogateway/missions/worldview-3 (last access: 21 February 2022), 2022b.
GHGSat: Global leader in remote sensing of greenhouse gas, GHGSat, Montreal, Canada, https://www.ghgsat.com/en/who-we-are/ (last access: 10 June 2022), 2022.
Gorroño, J., Varon, D. J., Irakulis-Loitxate, I., and Guanter, L.: Understanding the potential of Sentinel-2 for monitoring methane point emissions, Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, 2023.
Guanter, L., Irakulis-Loitxate, I., Gorroño, J., Sánchez-García, E., Cusworth, D. H., Varon, D. J., Cogliati, S., and Colombo, R.: Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer, Remote Sens. Environ., 265, 112671, https://doi.org/10.1016/j.rse.2021.112671, 2021.
Hayden, A. and Christy, J.: Maxar's WorldView-3 Enables Low-Concentration Methane Detection from Space, Earth Arxiv [preprint], https://doi.org/10.31223/X51T1C, 15 June 2023.
IMEO: Methane Alert and Response System (MARS), United Nations Environment Programme, International Methane Emissions Observatory, Paris, France, 2023.
Irakulis-Loitxate, I., Guanter, L., Liu, Y.-N., Varon, D. J., Maasakkers, J. D., Zhang, Y., Chulakadabba, A., Wofsy, S. C., Thorpe, A. K., Duren, R. M., Frankenberg, C., Lyon, D. R., Hmiel, B., Cusworth, D. H., Zhang, Y., Segl, K., Gorroño, J., Sánchez-García, E., Sulprizio, M. P., Cao, K., Zhu, H., Liang, J., Li, X., Aben, I., and Jacob, D. J.: Satellite-based survey of extreme methane emissions in the Permian basin, Sci. Adv., 7, eabf4507, https://doi.org/10.1126/sciadv.abf4507, 2021.
Irakulis-Loitxate, I., Gorroño, J., Zavala-Araiza, D., and Guanter, L.: Satellites Detect a Methane Ultra-emission Event from an Offshore Platform in the Gulf of Mexico, Environ. Sci. Tech. Let., 9, 520–525, https://doi.org/10.1021/acs.estlett.2c00225, 2022a.
Irakulis-Loitxate, I., Guanter, L., Maasakkers, J. D., Zavala-Araiza, D., and Aben, I.: Satellites Detect Abatable Super-Emissions in One of the World's Largest Methane Hotspot Regions, Environ. Sci. Technol., 56, 2143–2152, https://doi.org/10.1021/acs.est.1c04873, 2022b.
Jacob, D. J., Varon, D. J., Cusworth, D. H., Dennison, P. E., Frankenberg, C., Gautam, R., Guanter, L., Kelley, J., McKeever, J., Ott, L. E., Poulter, B., Qu, Z., Thorpe, A. K., Worden, J. R., and Duren, R. M.: Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane, Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, 2022.
Jervis, D., McKeever, J., Durak, B. O. A., Sloan, J. J., Gains, D., Varon, D. J., Ramier, A., Strupler, M., and Tarrant, E.: The GHGSat-D imaging spectrometer, Atmos. Meas. Tech., 14, 2127–2140, https://doi.org/10.5194/amt-14-2127-2021, 2021.
Jia, M., Li, F., Zhang, Y., Wu, M., Li, Y., Feng, S., Wang, H., Chen, H., Ju, W., Lin, J., Cai, J., Zhang, Y., and Jiang, F.: The Nord Stream pipeline gas leaks released approximately 220,000 tonnes of methane into the atmosphere, Environ. Sci. Ecotechnol., 12, 100210, https://doi.org/10.1016/j.ese.2022.100210, 2022.
Jongaramrungruang, S., Thorpe, A. K., Matheou, G., and Frankenberg, C.: MethaNet – An AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery, Remote Sens. Environ., 269, 112809, https://doi.org/10.1016/j.rse.2021.112809, 2022.
Kayrros: A partner for today and the future, agile with technology and with a smarter approach to data, Kayrros, Paris, France, https://www.kayrros.com/who-are-we/ (last access: 10 June 2022), 2022.
Lauvaux, T., Giron, C., Mazzolini, M., d'Aspremont, A., Duren, R., Cusworth, D., Shindell, D., and Ciais, P.: Global Assessment of Oil and Gas Methane Ultra-Emitters, Science, 375, 557–561, https://doi.org/10.31223/X5NS54, 2022.
Liu, Y.-N., Zhang, J., Zhang, Y., Sun, W.-W., Jiao, L.-L., Sun, D.-X., Hu, X.-N., Ye, X., Li, Y.-D., Liu, S.-F., Cao, K.-Q., Chai, M.-Y., and Zhou, W.-Y.-N.: The Advanced Hyperspectral Imager: Aboard China's GaoFen-5 Satellite, IEEE Geosci. Remote Sens., 7, 23–32, https://doi.org/10.1109/MGRS.2019.2927687, 2019.
Luo, H., Li, Z., Wu, Y., Qiu, Z., Shi, H., Wang, Q., and Xiong, W.: Greenhouse Gases Monitoring Instrument on GaoFen-5 Satellite-II: Optical Design and Evaluation, Remote Sens., 15, 1105, https://doi.org/10.3390/rs15041105, 2023.
NASA: NASA Earth Observations Cloud Fraction (1 Month TERRA/MODIS), National Aeronotics and Space Administration, Washington, D.C., https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR&date=2022-06-01 (last access: 19 June 2023), 2023.
OHBI: Satellites & Missions: PRISMA, Orbitale Hochtechnologie Bremen Italia S.p.A., Milan, Italy, https://www.ohb-italia.it/satellites-missions/ (last access: 21 February 2022), 2022.
Orbio: Actionable Methane Intelligence: Filling the global methane gap with asset-level emissions data, Orbio, Köln, Germany, https://www.orbio.earth/ (last access: 25 February 2023), 2023.
Pandey, S., Gautam, R., Houweling, S., van der Gon, H. D., Sadavarte, P., Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., van Kempen, T., Hoogeveen, R., van Hees, R., Hamburg, S. P., Maasakkers, J. D., and Aben, I.: Satellite observations reveal extreme methane leakage from a natural gas well blowout, P. Natl. Acad. Sci. USA, 116, 26376–26381, https://doi.org/10.1073/pnas.1908712116, 2019.
Ravikumar, A. P., Sreedhara, S., Wang, J., Englander, J., Roda-Stuart, D., Bell, C., Zimmerle, D., Lyon, D., Mogstad, I., Ratner, B., and Brandt, A. R.: Single-blind Inter-comparison of Methane Detection Technologies – Results from the Stanford/EDF Mobile Monitoring Challenge, Elementa, 7, 29, https://doi.org/10.1525/elementa.373, 2019.
Roger, J., Irakulis-Loitxate, I., Valverde, A., Gorroño, J., Chabrillat, S., Brell, M., and Guanter, L.: High-resolution methane mapping with the EnMAP satellite imaging spectroscopy mission, Earth Arxiv [preprint], https://doi.org/10.31223/X5M65Z, 2023.
Rutherford, J., Sherwin, E., Chen, Y., Aminfard, S., and Brandt, A.: Evaluating methane emission quantification performance and uncertainty of aerial technologies via high-volume single-blind controlled releases, Oil, Gas, and Energy, Earth Arxiv [preprint], https://doi.org/10.31223/X5KQ0X, 2023.
Sánchez-García, E., Gorroño, J., Irakulis-Loitxate, I., Varon, D. J., and Guanter, L.: Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite, Environ. Sci. Technol., 56, 10517–10529, https://doi.org/10.1021/acs.est.1c08575, 2022.
Scott, W.: Mapping Methane Emissions Using Maxar's WorldView-3 Satellite, Maxar, Ann Arbor, MI, USA, https://blog.maxar.com/earth-intelligence/2022/mapping-methane-emissions-using-maxars-worldview-3-satellite (last access: 25 May 2023), 2022.
Sherwin, E. D., Chen, Y., Ravikumar, A. P., and Brandt, A. R.: Single-blind test of airplane-based hyperspectral methane detection via controlled releases, Elementa, 9, 00063, https://doi.org/10.1525/elementa.2021.00063, 2021.
Sherwin, E. D., Rutherford, J. S., Chen, Y., Aminfard, S., Kort, E. A., Jackson, R. B., and Brandt, A. R.: Single-blind validation of space-based point-source detection and quantification of onshore methane emissions, Sci. Rep., 13, 3836, https://doi.org/10.1038/s41598-023-30761-2, 2023.
Sherwin, E. D., Rutherford, J. S., Zhang, Z., Chen, Y., Wetherley, E. B., Yakovlev, P. V., Berman, E. S. F., Jones, B. B., Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Duren, R. M., and Brandt, A. R.: US oil and gas system emissions from nearly a million aerial site measurements, Nature, https://doi.org/10.21203/rs.3.rs-2406848/v1, in press, 2024.
Song, Q., Ma, C., Liu, J., and Wei, H.: Quantifying ocean surface green tides using high-spatial resolution thermal images, Opt. Express, 30, 36592, https://doi.org/10.1364/OE.472479, 2022.
Sun, Y., Szücs, G., and Brandt, A. R.: Solar PV output prediction from video streams using convolutional neural networks, Energy Environ. Sci., 8, 1811–1818, https://doi.org/10.1039/C7EE03420B, 2018.
USGS: Landsat 8, United States Geological Survey, Washington, D.C., https://www.usgs.gov/landsat-missions/landsat-8 (last access: 21 February 2022), 2022.
Varon, D. J., Jacob, D. J., McKeever, J., Jervis, D., Durak, B. O. A., Xia, Y., and Huang, Y.: Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes, Atmos. Meas. Tech., 11, 5673–5686, https://doi.org/10.5194/amt-11-5673-2018, 2018.
Varon, D. J., McKeever, J., Jervis, D., Maasakkers, J. D., Pandey, S., Houweling, S., Aben, I., Scarpelli, T., and Jacob, D. J.: Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production, Geophys. Res. Lett., 46, 13507–13516, https://doi.org/10.1029/2019GL083798, 2019.
Varon, D. J., Jervis, D., McKeever, J., Spence, I., Gains, D., and Jacob, D. J.: High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations, Atmos. Meas. Tech., 14, 2771–2785, https://doi.org/10.5194/amt-14-2771-2021, 2021.
Wang, A. and Lee, J. J.: Methane “Super-Emitters” Mapped by NASA's New Earth Space Mission, National Aeronotics and Space Administration, Pasadena, California, USA, https://www.nasa.gov/feature/jpl/methane-super-emitters-mapped-by-nasa-s-new-earth-space-mission (last access: 25 May 2023), 2022.
Xinhua: China launches new remote sensing satellite, XinhuaNet, 9th December, https://english.news.cn/20221209/55ef410290954ce1a77f34a6be9beb64/c.html (last access: 24 May 2023), 2022.
Zhang, B., Guo, B., Zou, B., Wei, W., Lei, Y., and Li, T.: Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China, Environ. Pollut., 300, 118981, https://doi.org/10.1016/j.envpol.2022.118981, 2022.
Zhong, B., Yang, A., Liu, Q., Wu, S., Shan, X., Mu, X., Hu, L., and Wu, J.: Analysis Ready Data of the Chinese GaoFen Satellite Data, Remote Sens., 13, 1709, https://doi.org/10.3390/rs13091709, 2021.
Zimmerle, D.: METEC Controlled Test Protocol: Survey Emission Detection And Quantification, Colorado State University, Fort Collins, CO, USA, https://doi.org/10.25675/10217/235363, 2022.
Short summary
Countries and companies increasingly rely on a growing fleet of satellites to find large emissions of climate-warming methane, particularly from oil and natural gas systems across the globe. We independently assessed the performance of nine such systems by releasing controlled, undisclosed amounts of methane as satellites passed overhead. The tested systems produced reliable detection and quantification results, including the smallest-ever emission detected from space in such a test.
Countries and companies increasingly rely on a growing fleet of satellites to find large...