Methane Mapping with Future Satellite Imaging Spectrometers, by Alana K. Ayasse et al. alanaayasse@ucsb.edu. Also Dar Robers, dar@geog.ucsb.edu, Philip Dennison, dennison@geog.utah.edu, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA: Andrew.K.Thorpe@jpl.nasa.gov (A.K.T.); robert.o.green@jpl.nasa.gov (R.O.G.); riley.m.duren@jpl.nasa.gov (R.M.D.); David.R.Thompson@jpl.nasa.gov (D.R.T.) and at the Scientific Computing and Imaging Institute, Univ of Utah, SLC markus.foote@utah.edu (M.F.); sjoshi@sci.utah.edu (S.J.) https://www.mdpi.com/2072-4292/11/24/3054/pdf
In the coming decade, there is strong interest in a new generation of Earth observing satellites
equipped with Visible and Shortwave Infrared (VSWIR) imaging spectrometers [15]. VSWIR imaging
spectrometers typically measure between 380 and about 2500 nm with between 5 to 10 nm spectral
spacing [6,16–19]. These imaging spectrometers are sensitive to gas absorption features, which allows
for the detection and quantitative mapping of methane, carbon dioxide, and water vapor [16,20–22].
In 2019, the Italian space agency launched the Hyperspectral Precursor and Application Mission
(PRISMA), a medium resolution imaging spectrometer [23,24]. In 2020, the German space agency
plans to launch the Environmental Mapping and Analysis Program (EnMAP) [25] and the Japanese
government plans to launch the Hyperspectral Imager Suite (HISUI) [26], both of which are spaceborne
imaging spectrometers.The National Aeronautics and Space Administration (NASA) plans to launch
the Earth Surface Mineral Dust Source Investigation (EMIT) to the International Space Station (ISS) in
2021. NASA’s Surface Biology and Geology investigation is likely to include an imaging spectrometer
that could launch as early as 2025 [27]. None of these sensors are being designed for gas mapping, but
if these instruments can be used for methane mapping, the global monitoring of greenhouse gases will
drastically improve.
Recent work with a current satellite VSWIR imaging spectrometer and airborne imaging
spectrometer data have provided a testbed for evaluating point source methane detection and
measurements with these types of instruments [16,18,20,22,28]. The Hyperion imaging spectrometer
on board the EO-1 satellite successfully detected the accidental methane release at Aliso Canyon [28],
despite the age of the sensor and low signal-to-noise ratio (SNR). The Airborne Visible and Infrared
Imaging Spectrometer Next Generation instrument (AVIRIS-NG), detected, geolocated, and quantified
over 500 methane point sources throughout California from the oil and gas, manure management, and
waste management sectors [7]. Across each sector, a small number of point sources contributed the
majority of observed methane emissions, demonstrating the importance of observing point sources.
2.3. Matched Filter Methane Retrieval
The methane enhancement above the background for each pixel was estimated using an iterative
matched filter technique tuned with a methane unit absorption spectrum [19,35–37]. This iterative
method included additional optimization factors that correct for spatially varying surface albedo.
In addition, a `1 sparsity prior was included to reflect the expectation that methane enhancement is a
rare occurrence within an image. This sparse approach is similar to methods employed in compressed
sensing techniques for the reduction of acquisition time or improved resolution in fields as varied as
medical imaging, radio astronomy, and electron microscopy [38–42]. The iterative technique required
for solving this sparse optimization problem additionally produces the explicit removal of any methane
absorption signal from the background covariance matrix; failure to remove this signal is detrimental
to matched filter techniques [36,37,43,44]
This iterative matched filter with sparsity and albedo correction was applied to the AVIRIS-NG
images and simulated 30 m and 60 m images. For all scenes, the sparse matched filter was run
for 30 iterations, which includes an additional 50% margin over the 20 iterations that produced
initial observable convergence of the optimization energy. The number of iterations was based on
observations of convergence from a test dataset. The methane unit absorption spectrum for each scene
was computed from the change in radiance corresponding to a change in methane concentration in
MODTRAN6 with each scene’s sensor height and solar zenith angle. The albedo correction factor
was estimated by the ratio of a pixel’s radiance to the mean radiance in the scene. For the original
AVIRIS-NG data, the matched filter was applied to groups of five adjacent columns, corresponding to
adjacent detectors of the AVIRIS-NG instrument. This grouping improved the covariance estimate
used by the matched filter to describe the background by suppressing artifacts from non-uniformity
among detector elements [19]. For the simulated satellite images, column artifacts were averaged out
by spatial resampling, and therefore the matched filter was applied to the entire image.
The resulting methane enhancement image is measured in ppm-m, where ppm represents
concentration and m represents the path length over which absorption occurs. Due to this algorithm’s
sparsity prior, many pixels are reported to have no methane enhancement above the background.
This allows for interpreting the enhancement values as a detection result (presence/absence of methane
for a positive/zero enhancement value). We also consider the resulting images as a quantitative retrieval
directly from the per-pixel methane concentration. For more information on the retrieval algorithm
please see Foote et. al., In Review [45].
…The following sections present the matched filter methane retrieval
results for the AVIRIS-NG, 30 m, and 60 m satellite simulations.
3.1.1.
Petroleum and Natural Gas
The natural gas and petroleum industry represents about 30%–35% of all anthropogenic methane
emissions globally [2,4,48]. Methane emissions from this sector can be challenging to characterize, given
the significant variability in source type, plume shape and size, and intermittence. Here, we present
three examples from di
erent sources. The first is a gas storage facility that has two emission sources,
the second is a well with a methane plume, and the third is a leak from a natural gas distribution line
in a neighborhood [7]. All plumes were detectable from the 30 m and 60 m simulated satellite images.
Figure 3 depicts the results of two plumes at the Honor Ranch gas storage facility. The source
for the top plume is an emergency shutdown stack and for the bottom plume, it is a compressor unit.
In the AVIRIS-NG image, these two plumes were distinct, which allows the plumes to be attributed
to their exact sources. In the 30 m image, some of the detail of the plume is lost, but two distinct
plumes are still visible. At 60 m, the methane enhancement is visible, but the distinction between the
two plumes disappears. At the 30 m and 60 m resolution, we could easily attribute the plume to the
facility, but not to the exact locations within the facility as we could with the original AVIRIS-NG
image.
Figure 3. Matched filter methane retrieval for a plume from a 4 November, 2016 AVIRIS-NG image.
The plume is from the Honor Ranch gas storage facility, where there are two plumes, one from an
emergency shutdown stack and the other from a compressor unit. (A) Results from the original
AVIRIS-NG image. (B) Results from the 30 m simulated satellite image with ~200 SNR. (C) Results
from the 60 m simulated satellite image with about ~400 SNR. The left side of the scale bar are units
in parts per million per meter (ppm-m) above the background. The right side of the scale bar are
methane units in g/m2.
Figure 4 shows the matched filter results from a methane plume from a well. Given the sheer
quantity and high density of wells throughout the United States, a satellite system would be well
suited to identify those that are emitting methane. This particular example is from a hydrocarbon
extraction site within the Aliso Canyon gas storage facility. The details of the plume are most distinct
in the original AVIRIS-NG image. In the 30 m and 60 m satellite images, the plume is still clearly
detectable, but becomes increasingly less detailed.
Figure 4. Matched filter methane retrieval results from a plume from a 18 September, 2017 AVIRISNG
image. The plume is from an oil producing well in Aliso Canyon. (A) Results from the original
AVIRIS-NG image. (B) Results from the 30 m simulated satellite image with ~200 SNR. (C) Results
from the 60 m simulated satellite image with about ~400 SNR. The left side of the scale bar are units
in parts per million per meter (ppm-m) above the background. The right side of the scale bar are
methane units in g/m2.
One concern in the natural gas and petroleum sector are leaks and other fugitive emissions. Not
only do these emissions contribute to the overall greenhouse gas budget, but they also represent lost
Figure 3. Matched filter methane retrieval for a plume from a 4 November, 2016 AVIRIS-NG image.
The plume is from the Honor Ranch gas storage facility, where there are two plumes, one from an
emergency shutdown stack and the other from a compressor unit. (A) Results from the original
AVIRIS-NG image. (B) Results from the 30 m simulated satellite image with ~200 SNR. (C) Results
from the 60 m simulated satellite image with about ~400 SNR. The left side of the scale bar are units in
parts per million per meter (ppm-m) above the background. The right side of the scale bar are methane
units in g/m2.
Figure 4 shows the matched filter results from a methane plume from a well. Given the sheer
quantity and high density of wells throughout the United States, a satellite system would be well
suited to identify those that are emitting methane. This particular example is from a hydrocarbon
extraction site within the Aliso Canyon gas storage facility. The details of the plume are most distinct
in the original AVIRIS-NG image. In the 30 m and 60 m satellite images, the plume is still clearly
detectable, but becomes increasingly less detailed.
Remote Sens. 2019, 11, x FOR PEER REVIEW 7 of 19
Figure 3 depicts the results of two plumes at the Honor Ranch gas storage facility. The source
for the top plume is an emergency shutdown stack and for the bottom plume, it is a compressor unit.
In the AVIRIS-NG image, these two plumes were distinct, which allows the plumes to be attributed
to their exact sources. In the 30 m image, some of the detail
One concern in the natural gas and petroleum sector are leaks and other fugitive emissions.
Not only do these emissions contribute to the overall greenhouse gas budget, but they also represent
lost revenue and in suciently large amounts can pose a public safety hazard. The ability to detect revenue and in sufficiently large amounts can pose a public safety hazard. The ability to detect leaks
over large areas could help improve the mitigation of greenhouse gas emissions and hazards. Figure
5 is an example of an underground natural gas distribution line leak that was detected in 2016 in the
Chino Hills neighborhood, CA. This particular leak was detected using the real time methane
mapping capability of AVIRIS-NG and the results were shared with the local gas company who
quickly confirmed and repaired the leak [19]. The plume is clearly visible in the 30 m and 60 m simulated images.
Current and past satellite sensors, like SCIAMACY and TROPOMI, are able to detect regional methane enhancements over areas with heavy oil and gas production, like the methane anomaly over
the Four Corners areas in the Western United Sates [49], but cannot identify the exact location of
individual point sources. Currently, the only way to locate a specific source is through a ground or
airborne campaign, which can be costly, time consuming, and have a limited geographic extent [6].
Therefore, a higher spatial resolution satellite sensor that could identify point sources would help us
understand the dynamics of oil and natural gas methane emissions. Other studies have looked into
the possibility of using spaceborne sensors to do fine scale methane plume detection. Varon et al. [13]
performed a sensitivity study indicating that individual plumes could be mapped with a suciently
sensitive constellation of point source monitoring satellites, similar to the planned GHGSat fleet.
In addition, Cusworth et al. showed that plumes from oil/gas facilities could be detected with the
upcoming EnMAP mission [50]. The future generation of imaging spectrometers will help us to
improve our understanding of methane emissions from the oil and natural gas sectors.
Limitations
The satellite simulations indicate that point source methane mapping will be possible with
the future generation of satellite imaging spectrometers. However, there are some limitations
with the simulations presented here. While the specifications for the simulation represent the best
available knowledge about the design of a future spectrometer (EMIT), there remains a possibility
that specifications will change before launch. Other satellite designs might include di
erent spectral,
spatial, or SNR characteristics not modeled here, however these are unlikely to be drastically di
erent.
In addition, this study did not account for other factors associated with satellite sensors including
potential orbits, cloud cover, sun angles, and surface brightness. In particular, surface brightness has
proven to have a large influence on methane retrievals [50,57]. While there is inherent variability
in surface brightness in the AVIRIS-NG scenes, we were not able to control this variable. Finally,
while the flightlines from this study represent a range of emissions over variable surface terrain, more
challenging examples containing lower fluxes were not tested.
Other limitations in the study include the methods we used to create a plume mask and the
flux estimate. The plume mask is intended to isolate the plume and filter out spurious signals or
false positives. Spurious signals were present in both the airborne data and the satellite simulations
(Figures 3–10). For this study, we attempted to be as consistent as possible and therefore generally used
consistent thresholds to isolate the plume. It should be noted, however, that more tailored thresholding
could produce better results in future studies. The thresholding and segmentation can have a large
impact on the results, more work is needed to determine the best practices for how to approach this.
Additional work is required to assess the impact that these spurious signals may have on our IME and
flux estimates. This study also used a simple method to estimate the flux of the plumes. More robust
flux calculations could lead to more accurate results.
4. Conclusions
We tested the potential to use future Earth observing satellites such as EMIT or SBG to map
and measure methane plumes from the three main anthropogenic emission sectors. These particular
future satellites will measure in the SWIR with 7–10 nm spectral sampling, the spatial resolution
will range from 30 m to 60 m pixels, and will have a SNR in the SWIR between 200 and 400. To test
the potential of using these instruments for high resolution methane mapping, we simulated two
proposed satellite designs from the AVIRIS-NG data. The first was a 30 m pixel resolution with a ~200
SNR in the SWIR, the second was a 60 m pixel resolution with ~400 SNR in the SWIR.We then ran a
matched filter methane retrieval algorithm on the simulated satellite images and compared it to the
original AVIRIS-NG.
We found that for almost all plumes, the methane enhancement remained visible at the coarser
spatial resolutions. This indicated that most point source emissions from the largest anthropogenic
sources should be detectable, at minimum, in a 30 m resolution image. We found that when we
quantified the flux of the plume in the simulated images, it correlated well with the flux calculated
from the AVIRIS-NG results. In addition, for the controlled release experiment, the flux calculated
from the AVIRIS-NG data was similar to the known flux. Within the limits of our study, we also found
that the spatial resolution had a larger impact on the results than the SNR. We conclude that a satellite
system will be able to map and quantify the biggest of the point source emitters and should be able to
detect plumes as small as ~100 kg/h, although accurate quantification of flux may be more dicult.
These plumes may only be a small fraction of the total number of plumes, but likely represent up to
40% of emitted methane.
While the future generation of satellite imaging spectrometers were not designed for high
resolution gas mapping, this work indicates that they will be able to map methane plumes and quantify
emission rates. However, imaging spectrometer concepts have been developed that would utilize
finer spectral resolution to improve gas sensitivities [50,58]. Eventually, these sensors will become
integrated into larger greenhouse gas monitoring schemes and will help scientists better understand
global methane emissions.
2018 Ohio Blowout
https://www.nytimes.com/2019/12/16/climate/methane-leak-satellite.html The first satellite designed to continuously monitor the planet for methane leaks made a startling discovery last year: A little known gas-well accident at an Ohio fracking site was in fact one of the largest methane leaks ever recorded in the United States.
The findings by a Dutch-American team of scientists, published Monday in the Proceedings of the National Academy of Sciences, mark a step forward in using space technology to detect leaks of methane, a potent greenhouse gas that contributes to global warming, from oil and gas sites worldwide. “We’re entering a new era. With a single observation, a single overpass, we’re able to see plumes of methane coming from large emission sources,” said Ilse Aben, an expert in satellite remote sensing and one of the authors of the new research. “That’s something totally new that we were previously not able to do from space.”
Scientists also said the new findings reinforced the view that methane emissions from oil installations are far more widespread than previously thought.
The blowout, in February 2018 at a natural gas well run by an Exxon Mobil subsidiary in Belmont County, Ohio, released more methane than the entire oil and gas industries of many nations do in a year, the research team found. The Ohio episode triggered about 100 residents within a one-mile radius to evacuate their homes while workers scrambled to plug the well.
At the time, the Exxon subsidiary, XTO Energy, said it could not immediately determine how much gas had leaked. But the European Space Agency had just launched a satellite with a new monitoring instrument called Tropomi, designed to collect more accurate measurements of methane.
“We said, ‘Can we see it? Let’s look,’” said Steven Hamburg, a New York-based scientist with the Environmental Defense Fund, which had been collaborating on the satellite project with researchers at the Netherlands Institute for Space Research in Utrecht, the Netherlands.
Natural gas production has come under increased scrutiny because of the prevalence of leaks of methane
The satellite’s measurements showed that, in Ohio in the 20 days it took for Exxon to plug the well, about 120 metric tons of methane an hour were released. That amounted to twice the rate of the largest known methane leak in the United States, from an oil and gas storage facility in Aliso Canyon, Calif., in 2015, though that event lasted longer and had higher emissions overall. The Ohio blowout released more methane than the reported emissions of the oil and gas industries of countries like Norway and France, the researchers estimated. Scientists said the measurements from the Ohio site could mean that other large leaks are going undetected.
“When I started working on methane, now about a decade ago, the standard line was: ‘We’ve got it under control. We’re managing it,’” Dr. Hamburg said. “But in fact, they didn’t have the data. They didn’t have it under control, because they didn’t understand what was actually happening. And you can’t manage what you don’t measure.”
An Exxon spokesman, Casey Norton, said that the company’s own scientists had scrutinized images and taken pressure readings from the well to arrive at a smaller estimate of the emissions from the blowout. Exxon is in touch with the satellite researchers, Mr. Norton said, and has “agreed to sit down and talk further to understand the discrepancy and see if there’s anything that we can learn.”
“This was an anomaly,” he said. “This is not something that happens on any regular basis. And we do our very best to prevent this from ever happening.”
An internal investigation found that high pressure had caused the well’s casing, or internal lining, to fail, Mr. Norton said.
Miranda Leppla, head of energy policy at the Ohio Environmental Council, said there had been complaints about health issues — throat irritation, dizziness, breathing problems — among residents closest to the well.
“Methane emissions, unfortunately, aren’t a rare occurrence, but a constant threat that exacerbates climate change and can damage the health of Ohioans,” she said.
Scientists said that a critical task was now to be more quickly able to sift through the tens of millions of data points the satellite collects each day to identify methane hot spots. Studies of oil fields in the United States have shown that a small number of sites with high emissions are responsible for the bulk of methane releases.
So far, detecting and measuring methane leaks has involved expensive field studies using aircraft and infrared cameras that make the invisible gas visible. In a visual investigation published last week, The New York Times used airborne measurement equipment and advanced infrared cameras to expose six so-called super emitters in a West Texas oil field.
There are limitations to hunting for methane leaks with satellite technology. Satellites cannot see beneath clouds. Scientists must also do complex calculations to account for the background methane that already exists in the earth’s atmosphere.
Still, satellites will increasingly be able to both rapidly detect large releases and shed light on the rise in methane levels in the atmosphere, which has been particularly pronounced since 2007
Fracking natural-gas production, which accelerated just as atmospheric methane levels jumped, has been studied as one possible cause.
“Right now, you have one-off reports, but we have no estimate globally of how frequently these things happen,” Dr. Hamburg of the Environmental Defense Fund said. “Is this a once a year kind of event? Once a week? Once a day? Knowing that will make a big difference in trying to fully understand what the aggregate emissions are from oil and gas.”
For more climate news sign up for the Climate Fwd: newsletter or follow @NYTClimate on Twitter.
Hiroko Tabuchi is a climate reporter. She joined The Times in 2008, and was part of the team awarded the 2013 Pulitzer Prize for Explanatory Reporting. She previously wrote about Japanese economics, business and technology from Tokyo. @HirokoTabuchi • Facebook
A version of this article appears in print on Dec. 17, 2019, Section A, Page 15 of the New York edition with the headline: Methane Leak, Seen From Space, Proves to Be Far Larger Than Thought. Order Reprints | Today’s Paper | Subscribe
Satellite observations reveal extreme methane leakage from a natural gas well blowout
By Sudhanshu Pandey, Ritesh Gautam, Sander Houweling, Hugo Denier van der Gon, Pankaj Sadavarte, Tobias Borsdorff, Otto Hasekamp, Jochen Landgraf, Paul Tol, Tim van Kempen, Ruud Hoogeveen, Richard van Hees, Steven P. Hamburg, Joannes D. Maasakkers, and Ilse Aben
PNAS December 26, 2019 116 (52) 26376-26381; first published December 16, 2019 https://doi.org/10.1073/pnas.1908712116
Air Waste Manag Assoc. 2020 Apr;70(4):410-424. doi: 10.1080/10962247.2020.1728423. Epub 2020 Mar 9.
Comparing estimates of fugitive landfill methane emissions using inverse plume modeling obtained with Surface Emission Monitoring (SEM), Drone Emission Monitoring (DEM), and Downwind Plume Emission Monitoring (DWPEM)
Nizar Bel Hadj Ali 1, Tarek Abichou 2, Roger Green 3
Affiliations expand
- PMID: 32043942
- DOI: 10.1080/10962247.2020.1728423
AbstractAs part of the global effort to quantify and manage anthropogenic greenhouse gas emissions, there is considerable interest in quantifying methane emissions in municipal solid waste landfills. A variety of analytical and experimental methods are currently in use for this task. In this paper, an optimization-based estimation method is employed to assess fugitive landfill methane emissions. The method combines inverse plume modeling with ambient air methane concentration measurements. Three different measurement approaches are tested and compared. The method is combined with surface emission monitoring (SEM), above ground drone emission monitoring (DEM), and downwind plume emission monitoring (DWPEM). The methodology is first trialed and validated using synthetic datasets in a hand-generated case study. A field study is also presented where SEM, DEM and DWPEM are tested and compared. Methane flux during two-days measurement campaign was estimated to be between 228 and 350 g/s depending on the type of measurements used. Compared to SEM, using unmanned aerial systems (UAS) allows for a rapid and comprehensive coverage of the site. However, as showed through this work, advancement of DEM-based methane sampling is governed by the advances that could be made in UAS-compatible measurement instrumentations. Downwind plume emission monitoring led to a smaller estimated flux compared with SEM and DEM without information about positions of major leak points in the landfill. Even though, the method is simple and rapid for landfill methane screening. Finally, the optimization-based methodology originally developed for SEM, shows promising results when it is combined with the drone-based collected data and downwind concentration measurements. The studied cases also discovered the limitations of the studied sampling strategies which is exploited to identify improvement strategies and recommendations for a more efficient assessment of fugitive landfill methane emissions.Implications: Fugitive landfill methane emission estimation is tackled in the present study. An optimization-based method combined with inverse plume modeling is employed to treat data from surface emission monitoring, drone-based emission monitoring and downwind plume emission monitoring. The study helped revealing the advantages and the limitations of the studied sampling strategies. Recommendations for an efficient assessment of landfill methane emissions are formulated. The method trialed in this study for fugitive landfill methane emission could also be appropriate for rapid screening of analogous greenhouse gas emission hotspots.
Methodologies for measuring fugitive methane emissions from landfills – A review.
Mønster J, Kjeldsen P, Scheutz C., Waste Manag. 2019 Mar 15;87:835-859. doi: 10.1016/j.wasman.2018.12.047. Epub 2019 Jan 16., PMID: 30660403 Review.
Kormi T, Mhadhebi S, Bel Hadj Ali N, Abichou T, Green R.
Waste Manag. 2018 Feb;72:313-328. doi: 10.1016/j.wasman.2016.11.024. Epub 2016 Nov 22.
PMID: 27887773
Riddick SN, Hancock BR, Robinson AD, Connors S, Davies S, Allen G, Pitt J, Harris NRP.
Waste Manag. 2018 Mar;73:210-219. doi: 10.1016/j.wasman.2016.12.006. Epub 2016 Dec 18.
PMID: 28003116
Allen G, Hollingsworth P, Kabbabe K, Pitt JR, Mead MI, Illingworth S, Roberts G, Bourn M, Shallcross DE, Percival CJ.
Waste Manag. 2019 Mar 15;87:883-892. doi: 10.1016/j.wasman.2017.12.024. Epub 2018 Jan 9.
PMID: 29329657
Bogner J, Pipatti R, Hashimoto S, Diaz C, Mareckova K, Diaz L, Kjeldsen P, Monni S, Faaij A, Gao Q, Zhang T, Ahmed MA, Sutamihardja RT, Gregory R; Intergovernmental Panel on Climate Change (IPCC) Working Group III (Mitigation).
Waste Manag Res. 2008 Feb;26(1):11-32. doi: 10.1177/0734242X07088433.
PMID: 18338699 Review.
