Methane emissions are one of the largest contributors to today’s climate change and global warming. Methane is in big part responsible for the formation of ground-level ozone, a hazardous air pollutant and greenhouse gas. Even though CO2 has a longer-lasting effect on the environment, methane effects are almost immediate and effect us in the near-term. With that, reducing fossil methane emissions are an important step toward mitigating climate change and should be within focus of policy makers globally.
According to a study carried out by EDF, the oil and gas industry is one of biggest methane producer — emitting 13 million metric tons of methane a year, which constitute about 24% of the global emissions of methane and a larger fraction of the anthropogenic emissions.
Traditionally, measurements of methane emissions in oil and gas production areas have been made primarily using ground-based measurements. This has changed in the recent years thanks to development of new ways in which satellite and geospatial imagery could be used to tackle this issue. In fact, the International Energy Agency estimates that worldwide, the oil and gas industry can achieve a 75% reduction using technologies available today — two-thirds of it at no net cost.
Let’s take a closer look at ways in which remote sensing data could be used to track methane emissions.
Remote Sensing of Methane, Physical Basis
Methane, water vapour and carbon dioxide are the dominant greenhouse gases in the Earth’s atmosphere. The free gases are colourless therefore they can not be detected in a visible (VIS) part of the electromagnetic spectrum. However, they are detectable in the short wave infrared (SWIR), the part of the spectrum invisible to the human eye.
Figure 1. Methane (CH4), CO2, and water vapour (H2O) slant column optical depths in the 1500–2500 nm SWIR spectral range, based on absorption line strengths from the HITRAN2016 database sampled at 20 pm spectral resolution. Values are for the US Standard Atmosphere (Anderson et al., 1986), with surface concentrations adjusted to 1875 ppb for methane and 410 ppm for CO2. The slant optical depth calculation is done for a solar zenith angle of 40∘ and satellite viewing angle of 0∘.The grey shaded areas are the spectral ranges of bands 11 and 12 for Sentinel-2A (solid) and Sentinel-2B (hatched).
Source: (Varon et al. 2021, 3)
The measurement
All satellites that have the capability to observe the Earth in SWIR can be potentially used for Methane observation. Nonetheless, if the spectral band of the satellite measurement corresponds with the absorption bands of CH4 the measurement is more accurate.
Procedure:
- Only cloud-free cloud free scenes are selected
- Positions of the surface object, the sensor and The Sun are noted.
- For the given geometry from point 2, the sensor reading (the radiance) is predicted according to the transmission model of the atmosphere.
- The Discrepancies between the predicted and registered measurements are attributed to the different constituents of the atmosphere eg. CH4 (uncertainties of the measurement are taken into account)
- The result of the point 4 is the parameter called “optical depth” that translates to column methane content in mol/m²
- The methane source intensity (t/h) can be estimated from column methane content with the integrated methane enhancement (IME) method (wind must be taken into account) ( Varon et al. 2018)
where Q is the source rate (t/h), IME is the integrated column methane content over scene plume (kg), and
Ueff=0.33U10+0.45(m/s), and U10 is the wind speed at 10 metres, which can be obtained from weather forecasts.
Limitations:
- Clear sky conditions
- Assumptions on surface reflective properties
- Assumptions on the atmospheric aerosols properties
- Wind data needed for source size estimation
Effects on vegetation
Methane emissions are harmful to plants because the decomposing CH4 yields ozone that causes chlorosis, or a yellowing of the leaves. Methane reacts with the hydroxyl radical (OH), light and other atmospheric constituents. The net result is production of O3 that harms the chlorophyll — chlorosis. (“Atmospheric methane”, n.d.) [NET: CH4 + 4O2 → HCHO + 2O3 + H2O]
The yellowing of the leaves caused by chlorosis can be observed with VIS and NIR (near infrared) sensors by analysing the vegetation indexes: the vegetation index (VI), the normalised vegetation index (NDVI), Photochemical Reflectance Index (PRI) and the yellowness index (YI). (Adams, Philpot, and Norvell 1999). These methods are most suited for prolonged exposure to CH4, and are more indicative of the relative magnitude of the source (minor, medium, large) rather than absolute volumes released.
Limitations
- Clear sky conditions
- Different plants have variable tolerance for CH4
- Absolute volumes released are hard to estimate
Current and future options
Only missions with short-wave infrared (SWIR) sensors can detect methane emissions. As for May 2022, a number of small satellite companies announced their plans to launch methane detecting missions. Only three missions were so far successfully used for broad methane leaks detection: GHGSat, Sentinel 2, Sentinel 5. However, Sentinel 2 was not specifically designed for this purpose and Sentinel 2 requires custom processing for individual applications as no consistent “methane product” is provided. Furthermore, Sentinel 5 resolution of 7 km is not suitable for detecting CH4 leaks other than events of great magnitude (Q>10000 kg/h). The GHGSat constellation has been tested in the context of multiple sites monitoring ( Varon et al. 2019) (Irakulis-Loitxate et al. 2021). The minimal size of the detected leak with GHGSat was Q>=100kg/h.
Notable additions to this list are WorldView-3 and PRISMA missions (Guanter et al. 2021), (Sánchez-García et al. 2022). In recent studies it was demonstrated that both satellites can detect CH4 leaks of approximately Q=100kg/h.
As off May 2022, no large-scale studies involving methane leak detection by vegetation state were conducted. To our knowledge, only local case studies or laboratory tests were conducted. Of these, most were concentrated on landfill re cultivation. Therefore it is hard to estimate the feasibility of this method for pipeline and gas infrastructure monitoring. Probably only the most severe prolonged leaks could be detected by this method.
GHGSat-D observations of methane plumes in the Korpezhe oil/gas field of western Turkmenistan. (a–h) Plumes observed near the Korpezhe gas compressor station (38.499°N, 54.199°E, at sea level) .
References
Adams, M. L., W. D. Philpot, and W. A. Norvell. 1999. “Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation.” International Journal of Remote Sensing 20 (18): 3663–3675. 10.1080/014311699211264.
“Atmospheric methane.” n.d. Wikipedia. Accessed May 22, 2022. https://en.wikipedia.org/wiki/Atmospheric_methane.
Guanter, Luis, Itziar Irakulis-Loitxate, Javier Gorroño, Elena Sánchez-García, Daniel H. Cusworth, Daniel J. Varon, Sergio Cogliati, and Roberto Colombo. 2021. “Mapping methane point emissions with the PRISMA spaceborne imaging spectrometer.” Remote Sensing of Environment 265, no. 112671 (November). https://doi.org/10.1016/j.rse.2021.112671.
Hasekamp, Otto, Alba Lorente, Haili Hu, Andre Butz, Joost van de Brugh, and Jochen Landgraf. n.d. Algorithm Theoretical Baseline Document for Sentinel-5 Precursor Methane Retrieval. SRON-S5P-LEV2-RP-001 ed. N.p.: ESA. https://sentinel.esa.int/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-Methane-retrieval.
Irakulis-Loitxate, Itziar, Luis Guanter, Yin-Nian Liu, Daniel J. Varon, Joannes D. Maasakkers, Yuzhong Zhang, Apisada Chulakadabba, et al. 2021. “Satellite-based survey of extreme methane emissions in the Permian basin.” Science Advances 7 (27): eabf4507. 10.1126/sciadv.abf4507.
“Methane emissions.” 2021. Energy. https://energy.ec.europa.eu/topics/oil-gas-and-coal/methane-emissions_en#eu-methane-strategy.
Sánchez-García, Elena, Javier Gorroño, Itziar Irakulis-Loitxate, Daniel J. Varon, and Luis Guanter. 2022. “Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite.” Atmospheric Measurement Techniques 15 (March): 1657–1674. https://doi.org/10.5194/amt-15-1657-2022.
Varon, Daniel J., Daniel J. Jacob, Jason McKeever, Dylan Jervis, Berke O. Durak, Yan Xia, and Yi Huang. 2018. “Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes.” Atmospheric Measurement Techniques 11:5673–5686. https://doi.org/10.5194/amt-11-5673-2018.
Varon, Daniel J., Dylan Jervis, Jason McKeever, Ian Spence, David Gains, and Daniel J. Jacob. 2021. “High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations.” Atmospheric Measurement Techniques 14, no. 4 (April): 2771–2785. 10.5194/amt-14–2771–2021.
Varon, D. J., J. McKeever, D. Jervis, J. D. Maasakkers, S. Pandey, S. Houweling, I. Aben, T. Scarpelli, and D. J. Jacob. 2019. “Satellite Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production.” Geophysical Research Letters 46 (22): 13507–13516. https://doi.org/10.1029/2019GL083798.
