Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data
Open accessAbstract
Methane (CH4) display spectral features in several regions of the infrared range (0.75–14 µm), which can be used for the remote mapping of emission sources through the detection of CH4 plumes from natural seeps and leaks. Applications of hyperspectral remote sensing techniques for the detection of CH4 in the near and shortwave infrared (NIR-SWIR: 0.75–3 µm) and longwave infrared (LWIR: 7–14 µm) have been demonstrated in the literature with multiple sensors and scenarios. However, the acquisition and processing of hyperspectral data in the midwave infrared (MWIR: 3–5 µm) for this application is rather scarce. Here, a controlled field experiment was used to evaluate the potential for CH4 plume detection in the MWIR based on hyperspectral data acquired with the SEBASS airborne sensor. For comparison purposes, LWIR data were also acquired simultaneously with the same instrument. The experiment included surface and undersurface emission sources (ground stations), with flow rates ranging between 0.6–40 m3/h. The data collected in both ranges were sequentially processed using the same methodology. The CH4 plume was detected, variably, in both datasets. The gas plume was detected in all LWIR images acquired over nine gas leakage stations. In the MWIR range, the plume was detected in only four stations, wherein 18 m3/h was the lowest flux sensed. We demonstrate that the interference of target reflectance, the low contrast between plume and background and a low signal of the CH4 feature in the MWIR at ambient conditions possibly explain the inferior results observed for this range when compared to LWIR. Furthermore, we show that the acquisition time and weather conditions, including specific limits of temperature, humidity, and wind speed, proved critical for plume detection using daytime MWIR hyperspectral data.
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We can observe GHGs with hyperspectral cameras because they interact with sunlight.
“While GHGs are invisible to our eyes, they must by definition interact with sunlight.This means that we can observe these compounds with hyperspectral cameras [428,711].”
From Tackling climate change with machine learning (2023) · cites this paper as [711]
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Cites
The HITRAN2016 molecular spectroscopic database
Gordon, I. E., Rothman, L. S., Hill, C., Kochanov, R. V., Tan, Y., Bernath, P. F., Birk, M., Boudon, V., Campargue, A., Chance, K. V., et al. (2017) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2017.06.038 · Cited by 7,721 · see citing works
Harsanyi, J. C., & Chang, C.-I. (1994) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.298007 · Cited by 1,523 · cited 1× · see citing works
“A matched filter (MF) is a classification algorithm used for the detection of known targets in the scene through the estimate of the abundance of an endmember at the sub-pixel scale [30,31].”
MethodThe NIST Chemistry WebBook: A Chemical Data Resource on the Internet
Linstrom, P. J., & Mallard, W. G. (2001) · Journal of Chemical & Engineering Data · doi:10.1021/je000236i · Cited by 1,145 · see citing works
Mapping target signatures via partial unmixing of AVIRIS data
Boardman, J. W., Kruse, F. A., & Green, R. O. (1995) · NASA Technical Reports Server (NASA) · Cited by 1,099 · see citing works
Hyperspectral Image Processing for Automatic Target Detection Applications
Manolakis, D. G., Marden, D. B., & Shaw, G. A. (2003) · Cited by 762 · see citing works
Wavelet transform domain filters: a spatially selective noise filtration technique
Xu, Y., Weaver, J. B., Healy, D. M., & Lu, J. (1994) · IEEE Transactions on Image Processing · doi:10.1109/83.336245 · Cited by 747 · see citing works
Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms
Manolakis, D. G., Truslow, E., Pieper, M. L., COOLEY, T. W., & Brueggeman, M. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2278915 · Cited by 281 · cited 1× · see citing works
“, HC based plastic, paint, alphalt) or rare materials not related to the target occur in the image [33].”
BackgroundFiltering noise from images with wavelet transforms
Weaver, J. B., Xu, Y., Healy, D. M., & Cromwell, L. D. (1991) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910210213 · Cited by 276 · cited 1× · see citing works
“This product is obtained by (i) filtering the noise in the image; (ii) highlighting spectral features; (iii) decreasing the spectral continuum variability introduced from variations in illumination and topography; and (iv) minimization of residuals from atmospheric correction and temperature estimation [27,28].”
BackgroundHook, S. J., Gabell, A., Green, A. A., & Kealy, P. S. (1992) · Remote Sensing of Environment · doi:10.1016/0034-4257(92)90096-3 · Cited by 257 · see citing works
Emissivity of terrestrial materials in the 3–5 μm atmospheric window
Salisbury, J. W., & D'Aria, D. M. (1994) · Remote Sensing of Environment · doi:10.1016/0034-4257(94)90102-3 · Cited by 252 · cited 1× · see citing works
“This condition implies that the errors associated to the estimated temperature values increase toward higher wavelengths, that is, the relation T/ε is less susceptible to errors in the MWIR range than in the LWIR [15].”
BackgroundAn in‐scene method for atmospheric compensation of thermal hyperspectral data
Young, S. J., Johnson, B. R., & Hackwell, J. A. (2002) · Journal of Geophysical Research Atmospheres · doi:10.1029/2001jd001266 · Cited by 190 · cited 5× · see citing works
“HITRAN spectral lines of methane (CH4) at the temperature of 296 K [18].”
BackgroundMethodInfluentialLWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing
Hackwell, J. A., Warren, D. W., Bongiovi, R. P., Hansel, S. J., Hayhurst, T. L., Mabry, D. J., Sivjee, M. G., & Skinner, J. W. (1996) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.258057 · Cited by 178 · cited 2× · see citing works
“The Spatially-Enhanced Broadband Array Spectrograph System, SEBASS [13], manufactured by the Aerospace Corporation (El Segundo, CA, USA), is one of the few hyperspectral sensors that covers MWIR (2.5–5.2 µm) and LWIR (7.5–13.5 µm) wavelengths, being able to acquire information simultaneously in both ranges in a single…”
MethodInfluentialBoardman, J. W., & Kruse, F. A. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2161585 · Cited by 177 · cited 1× · see citing works
“A matched filter (MF) is a classification algorithm used for the detection of known targets in the scene through the estimate of the abundance of an endmember at the sub-pixel scale [30,31].”
MethodPhysical Principles of Remote Sensing
Rees, G. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139017411 · Cited by 175 · see citing works
Evaluation of Six Methods for Extracting Relative Emissivity Spectra from Thermal Infrared Images
Li, Z.-L., Becker, F., Stoll, M. P., & Wan, Z. (1999) · Remote Sensing of Environment · doi:10.1016/s0034-4257(99)00049-8 · Cited by 169 · see citing works
Thorpe, A. K., Frankenberg, C., & Roberts, D. A. (2014) · Atmospheric measurement techniques · doi:10.5194/amt-7-491-2014 · Cited by 125 · see citing works
Thorpe, A. K., Roberts, D. A., Bradley, E. S., Funk, C., Dennison, P. E., & Leifer, I. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.03.018 · Cited by 122 · cited 1× · see citing works
“[8] successfully mapped CH4 plumes using shortwave infrared (SWIR: 1.”
MethodFirst use of an airborne thermal infrared hyperspectral scanner for compositional mapping
Kirkland, L. E., Herr, K. C., Keim, E. R., Adams, P. M., Salisbury, J. W., Hackwell, J. A., & Treiman, A. H. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00323-6 · Cited by 120 · cited 1× · see citing works
“Firstly, atmospheric effects were removed from the images using an adaptation of the in-scene atmospheric compensation (ISAC) algorithm [17,18].”
MethodScafutto, R. D. M., Filho, C. R. D. S., & de Oliveira, W. J. ́. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.03.009 · Cited by 108 · cited 1× · see citing works
“In August 2010 a controlled field experiment was performed at the former Rocky Mountain Oilfield Testing Center (RMOTC), located north of Casper, Wyoming (EUA) [2,16].”
MethodSelf-citationStandardization of near-infrared spectra in the wavelet domain
Walczak, B., Bouveresse, E., & Massart, D. L. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(96)00075-5 · Cited by 102 · see citing works
Airborne visualization and quantification of discrete methane sources in the environment
Tratt, D. M., Buckland, K. N., Hall, J. L., Johnson, P. D., Keim, E. R., Leifer, I., Westberg, K., & Young, S. J. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.08.011 · Cited by 97 · see citing works
Hulley, G., Duren, R., Hopkins, F. M., Hook, S. J., Vance, N., Guillevic, P., Johnson, W. R., Eng, B. T., Mihaly, J. M., Jovanovic, V. M., et al. (2016) · Atmospheric measurement techniques · doi:10.5194/amt-9-2393-2016 · Cited by 94 · see citing works
Gillespie, A. R., Matsunaga, T., Rokugawa, S., & Hook, S. J. (1996) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.255172 · Cited by 87 · see citing works
Mapping methane emissions from a marine geological seep source using imaging spectrometry
Roberts, D. A., Bradley, E. S., Cheung, R., Leifer, I., Dennison, P. E., & Margolis, J. S. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.10.015 · Cited by 85 · see citing works
Wavelet domain statistical hyperspectral soil texture classification
Zhang, X., Younan, N. H., & O’Hara, C. G. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.841476 · Cited by 69 · see citing works
The Infrared Absorption Bands of Methane
Nielsen, A. H., & Nielsen, H. H. (1935) · Physical Review · doi:10.1103/physrev.48.864 · Cited by 66 · see citing works
Multiscale volume representation by a DoG wavelet
Muraki, S. (1995) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.468408 · Cited by 63 · see citing works
Feng, J., Rogge, D. M., & Rivard, B. (2017) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2017.03.003 · Cited by 62 · cited 1× · see citing works
“This product is obtained by (i) filtering the noise in the image; (ii) highlighting spectral features; (iii) decreasing the spectral continuum variability introduced from variations in illumination and topography; and (iv) minimization of residuals from atmospheric correction and temperature estimation [27,28].”
BackgroundDetection of marine methane emissions with AVIRIS band ratios
Bradley, E. S., Leifer, I., Roberts, D. A., Dennison, P. E., & Washburn, L. (2011) · Geophysical Research Letters · doi:10.1029/2011gl046729 · Cited by 60 · see citing works
Scafutto, R. D. M., Filho, C. R. D. S., & Rivard, B. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.03.033 · Cited by 51 · cited 2× · see citing works
“Another advantage of the wavelets comprises the significant decrease of the noise in the image [35], which also has positive consequences on the CH4 spectral signatures (Figure 8).”
BackgroundSelf-citationMushkin, A., Balick, L. K., & Gillespie, A. R. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2005.06.003 · Cited by 46 · cited 2× · see citing works
“The few approaches found in the literature for atmospheric compensation of MWIR data [11,12] differ about the weight of reflected solar radiance (i.”
BackgroundScafutto, R. D. M., Filho, C. R. D. S., Riley, D. N., & de Oliveira, W. J. ́. (2017) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2017.07.002 · Cited by 32 · cited 6× · see citing works
“For comparison purposes, the same image processing techniques applied to the MWIR were also used to process SEBASS LWIR data acquired for the same experiment [2], intending to evaluate the efficacy in the detection of CH4 plumes in both spectral ranges.”
BackgroundMethodInfluentialSelf-citationHall, J. L., Boucher, R. H., Buckland, K. N., Gutierrez, D. J., Hackwell, J. A., Johnson, B. R., Keim, E. R., Moreno, N. M., Ramsey, M. S., Sivjee, M. G., et al. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2422817 · Cited by 27 · see citing works
Method of gas detection applied to infrared hyperspectral sensor
Kastek, M., Pia̧tkowski, T., Dulski, R., Chamberland, M., Lagueux, P., & Farley, V. (2012) · Photonics Letters of Poland · doi:10.4302/plp.2012.4.09 · Cited by 18 · see citing works
Griffin, M. K., Burke, H.-H. K., & Kerekes, J. P. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.543526 · Cited by 18 · cited 9× · see citing works
“[11] for atmospheric compensation and emissivity retrieval.”
BackgroundMethodInfluential
Flagged first, then by citations
Research works printed but not linked: 1
- Scafutto, R.D.P.M.; Souza Filho, C.R.d.; Rivard, B. Characterization of mineral substrates impregnated with crude oils using proximal infrared hyperspectral imaging. Remote Sens. Environ. 2016, 179, 116-130.
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Colloidal quantum dots for thermal infrared sensing and imaging
Hafiz, S. B., Scimeca, M. R., Sahu, A., & Ko, D. (2019) · Nano Convergence · doi:10.1186/s40580-019-0178-1 · Cited by 108 · see citing works
Advanced Leak Detection and Quantification of Methane Emissions Using sUAS
Hollenbeck, D., Zulevic, D., & Chen, Y. (2021) · Drones · doi:10.3390/drones5040117 · Cited by 49 · see citing works
Călin, M. A., Călin, A. C., & Nicolae, D. N. (2020) · Applied Spectroscopy Reviews · doi:10.1080/05704928.2020.1774381 · Cited by 33 · see citing works
“This is assumed by Scafutto and Filho[72] to be due to the low contrast between the plume and the background and the low signal of the CH4 feature in the MWIR spectral range under ambient conditions.”
BackgroundMethodInfluentialKrauz, L., Páta, P., Bednář, J., & Klíma, M. (2021) · Optics Express · doi:10.1364/oe.420571 · Cited by 26 · see citing works
Mid-Infrared Compressive Hyperspectral Imaging
Yang, S., Yan, X., Qin, H., Zeng, Q., Liang, Y., Argüello, H., & Yuan, X. (2021) · Remote Sensing · doi:10.3390/rs13040741 · Cited by 23 · see citing works
Scafutto, R. D. M., van der Werff, H. M. A., Bakker, W. H., van der Meer, F. D., & Filho, C. R. D. S. (2020) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102233 · Cited by 22 · see citing works
Self-citationXiao, C., Fu, B., Shui, H., Guo, Z., & Zhu, J. (2020) · Remote Sensing · doi:10.3390/rs12030537 · Cited by 18 · see citing works
“A number of approaches have been developed to retrieve XCH4, which can be achieved by applying radiative transfer models [14], such as the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) [15], and Iteratively Maximum a Posteriori DOAS (IMAP-DOAS) algorithms [13].”
MethodMultianalyte Detection with Metasurface-Based Midinfrared Microspectrometer
Tan, H., Meng, J., & Crozier, K. B. (2024) · ACS Sensors · doi:10.1021/acssensors.4c01220 · Cited by 15 · see citing works
Scafutto, R. D. M., Lievens, C., Hecker, C., van der Meer, F. D., & Filho, C. R. D. S. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112323 · Cited by 15 · see citing works
Self-citationScafutto, R. D. M., & Filho, C. R. D. S. (2019) · Fuel · doi:10.1016/j.fuel.2018.12.127 · Cited by 11 · see citing works
Self-citationMa, P., Zhuo, Y., Chen, G., & Burken, J. G. (2024) · Remote Sensing · doi:10.3390/rs16061029 · Cited by 10 · see citing works
Guo, Y., Qian, Y. X., Gao, Y., Liu, X., & Li, C. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100775 · Cited by 8 · see citing works
Gao, L., Chen, H., Chen, G., & Deng, J. (2023) · Remote Sensing · doi:10.3390/rs15205038 · Cited by 7 · see citing works
Rani, K., Dewangan, P., Sriram, G., Dorepalli, P. M., & Gaonkar, U. U. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.06.057 · Cited by 5 · see citing works
Scafutto, R. D. M., & Filho, C. R. D. S. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8900556 · Cited by 3 · see citing works
“Recently, the applications of MWIR airborne data for the detection of CH4 plumes were demonstrated by [16].”
BackgroundSelf-citationDetection of Methane Emissions Using Pattern Recognition
Ouerghi, E., Ehret, T., Facciolo, G., Meinhardt-Llopis, E., Morel, J., de Franchis, C., & Lauvaux, T. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553897 · Cited by 3 · see citing works
“In [5], a CH 4 -specific technique is developed where the background is removed by precise atmosphere modeling.”
BackgroundMethodRemote Detection of Emergency Emissions and Gas Leaks
Rodionov, I. D., Gomorev, M. A., Петровна, Р. И., Rodionov, A. I., Shapovalov, V. L., Shestakov, D. V., & Golubkov, M. G. (2024) · Russian Journal of Physical Chemistry B · doi:10.1134/s1990793124701124 · Cited by 2 · see citing works
Zhang, Z., Sargent, M., Manninen, E., Warren, J., Chulakadabba, A., Russi, M., Ayvazov, S., Benmergui, J., Knapp, M., Kyzivat, E. D., et al. (2026) · Atmospheric measurement techniques · doi:10.5194/amt-19-4637-2026 · Cited by 1 · see citing works
“Scafutto and De Souza Filho (2018) applied a Difference of Gaussians (DoG) wavelet to reduce noise in methane emissivity imagery derived from midwave infrared (MWIR) airborne hyperspectral data.”
Assessing Scientific and Industry Grade SWIR Airborne Imaging Spectrometers for CH4 Mapping
Scafutto, R. D. M., van der Werff, H. M. A., Bakker, W. H., van der Meer, F. D., & Filho, C. R. D. S. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9554544
Self-citationKim, E., Heo, S., Jo, Y., Bang, H., & Kim, S. (2026) · Journal of Institute of Control Robotics and Systems · doi:10.5302/j.icros.2026.25.0334
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“Subsequent MWIR work leverages hot exhaust plumes [2], [3], but subpixel mixing between reflected environment and intense plumes hampers accurate retrieval over cooler fuselage regions [18].”
Daun, K. J., Patel, O., & Moghaddam, S. T. (2026) · Remote Sensing of Environment · doi:10.1016/j.rse.2026.115556
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Hatim, S., Guezennec, T., Eremeev, K., Bae, J. E., Loiko, P. A., Tyazhev, A., Idlahcen, S., Godin, T., Hanzard, P.-H., Berthelot, T., et al. (2026) · APL Photonics · doi:10.1063/5.0342329
Olga, B., & Bednařík, A. (2026) · Environmental Technology & Innovation · doi:10.1016/j.eti.2026.105187
Peng, 宋. S., Yao, B., Hongcai, 丁. D., Fei, 王. W., Yan, 孙. S., Wenpei, 郑. Z., Xiaoben, 刘. L., Honghao, 岳. Y., & Junyan, 刘. L. (2026) · Infrared and Laser Engineering · doi:10.3788/irla20260045
- Authors
- Scafutto, R. D. P. M., & De Souza Filho, C. R.
- Affiliations
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Institute of Geosciences, UNICAMP, University of Campinas, P.O. Box 6152, Campinas SP 13083-970, Brazil
- Corresponding author
- Scafutto, Rebecca Del’ Papa Moreira
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-08-07
- Volume
- 10
- Issue
- 8
- Pages
- 1237
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2887205778
- Cited by
- 27
- References
- 35
- Citations by year
- 2026: 7 · 2025: 1 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 6 · 2020: 3 · 2019: 3
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2015/19842-7)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Physical Sciences › Chemistry › Spectroscopy › Spectroscopy and Laser Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- methane plume detection, mid-wave infrared, hyperspectral remote sensing, airborne hyperspectral sensor, long-wave infrared, gas leakage, methane seepage, acquisition time, weather conditions, wind speed, humidity, temperature
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