Cross-evaluation of measurements of peatland methane emissions on microform and ecosystem scales using high-resolution landcover classification and source weight modelling

Abstract. Understanding methane emissions from the Arctic, a fast warming carbon reservoir, is important for projecting changes in the global methane cycle under future climate scenarios. Here we optimize Arctic methane emissions with a nested-grid high-resolution inverse model by assimilating both high-precision surface measurements and column-average SCIAMACHY satellite retrievals of methane mole fraction. For the first time, methane emissions from lakes are integrated into an atmospheric transport and inversion estimate, together with prior wetland emissions estimated by six different biogeochemical models. We find that, the global methane emissions during July 2004–June 2005 ranged from 496.4 to 511.5 Tg yr−1, with wetland methane emissions ranging from 130.0 to 203.3 Tg yr−1. The Arctic methane emissions during July 2004–June 2005 were in the range of 14.6–30.4 Tg yr−1, with wetland and lake emissions ranging from 8.8 to 20.4 Tg yr−1 and from 5.4 to 7.9 Tg yr−1 respectively. Canadian and Siberian lakes contributed most of the estimated lake emissions. Due to insufficient measurements in the region, Arctic methane emissions are less constrained in northern Russia than in Alaska, northern Canada and Scandinavia. Comparison of different inversions indicates that the distribution of global and Arctic methane emissions is sensitive to prior wetland emissions. Evaluation with independent datasets shows that the global and Arctic inversions improve estimates of methane mixing ratios in boundary layer and free troposphere. The high-resolution inversions provide more details about the spatial distribution of methane emissions in the Arctic.

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Using satellites to uncover large methane emissions from landfills

10.31223/x5n33g ◽

2021 ◽

Author(s):

Joannes Maasakkers ◽

Daniel Varon ◽

Aldís Elfarsdóttir ◽

Jason McKeever ◽

Dylan Jervis ◽

...

Keyword(s):

High Resolution ◽

Hot Spots ◽

Buenos Aires ◽

Methane Emissions ◽

Emission Sources ◽

Emission Inventories ◽

Atmospheric Methane ◽

Target Mode ◽

Facility Level ◽

Methane Concentrations

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Landfills are responsible for large methane emissions that can be readily abated but have been sparsely observed. Here we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfill facilities. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots, and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this ‘tip and cue’ approach, we detect and analyze strongly emitting landfills (3-29 t hr−1) in Buenos Aires (Argentina), Delhi (India), Lahore (Pakistan), and Mumbai (India). We find that city-level emissions are 1.6-2.8 times larger than reported in commonly used emission inventories and that the landfills contribute 5-47% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane super-emitters at the facility-level.

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Monitoring of Methane Emissions in Oil and Gas from Space: Matching Needs with Satellite System Capability, and Advantages of High Resolution Monitoring

10.2118/207656-ms ◽

2021 ◽

Author(s):

Jean-Francois Gauthier

Keyword(s):

High Resolution ◽

Oil And Gas ◽

Detection Threshold ◽

Oil And Gas Industry ◽

Satellite System ◽

Methane Emissions ◽

Lessons Learned ◽

Gas Industry ◽

Satellite Systems ◽

The World

Abstract Satellites are a powerful tool in monitoring methane emissions around the world. In the last five years, many new systems have been both announced and deployed, each with different capabilities and designed for a specific purpose. With an increase in options also comes confusion as to how these systems can and should be used, especially in meeting the needs of the oil and gas industry. This paper will examine the different satellite systems available and explain what information they are best suited to provide. The performance parameters of several current and future satellite systems will be presented and supported with recent examples when available. For example, the importance of factors like frequency of revisit, detection threshold, and spatial resolution will be discussed and contrasted with the needs of the oil and gas industry in gaining a more complete understanding of its methane emissions and enabling action to mitigate them. Results from GHGSat's second generation of high-resolution satellites displaying measurements of methane plumes at oil and gas facilities around the world will be presented to demonstrate some of the advantages of the technology. These two satellites, GHGSat-C1 and C2 (Iris and Hugo), were launched in September 2020 and January 2021 respectively and have started delivering a tenfold improvement in performance after incorporating the lessons learned from their predecessor, GHGSat's demonstration satellite Claire. Finally, the ability of these systems to work together and complement each other's capabilities to provide actionable insight to the oil and gas industry will be discussed.

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High-resolution landcover classification using Random Forest

Remote Sensing Letters ◽

10.1080/2150704x.2014.882526 ◽

2014 ◽

Vol 5 (2) ◽

pp. 112-121 ◽

Cited By ~ 55

Author(s):

Matthew M. Hayes ◽

Scott N. Miller ◽

Melanie A. Murphy

Keyword(s):

High Resolution ◽

Random Forest ◽

Landcover Classification

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Confirmation of Elevated Methane Emissions in Utah's Uintah Basin With Ground‐Based Observations and a High‐Resolution Transport Model

Journal of Geophysical Research Atmospheres ◽

10.1002/2017jd027480 ◽

2017 ◽

Vol 122 (23) ◽

Cited By ~ 7

Author(s):

C. S. Foster ◽

E. T. Crosman ◽

L. Holland ◽

D. V. Mallia ◽

B. Fasoli ◽

...

Keyword(s):

High Resolution ◽

Transport Model ◽

Methane Emissions ◽

Uintah Basin

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The Research of LandCover Classification on Optical Image and Radar Image

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.330 ◽

2012 ◽

Vol 500 ◽

pp. 330-334

Author(s):

Yin Xuan Cao ◽

Zheng Zhao

Keyword(s):

High Resolution ◽

Classification Accuracy ◽

Radar Image ◽

Hengduan Mountains ◽

Fusion Image ◽

Fusion Algorithm ◽

Spectral Image ◽

Visual Effect ◽

Landcover Classification ◽

Better Than

This paper performed the fusion test using high resolution TerraSAR and ALOS optical multi-spectral image in Hengduan mountains area. The results of automatic classification compared to the visual effect for fusion image indicated that the classification accuracy by HPF is better than other fusion algorithm, which are superior to HPF in other application.

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2010–2015 North American methane emissions, sectoral contributions, and trends: a high-resolution inversion of GOSAT observations of atmospheric methane

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-4339-2021 ◽

2021 ◽

Vol 21 (6) ◽

pp. 4339-4356

Author(s):

Joannes D. Maasakkers ◽

Daniel J. Jacob ◽

Melissa P. Sulprizio ◽

Tia R. Scarpelli ◽

Hannah Nesser ◽

...

Keyword(s):

High Resolution ◽

Analytical Solution ◽

Oil And Gas ◽

Oil Production ◽

Methane Emissions ◽

Gas Production ◽

Anthropogenic Emissions ◽

Atmospheric Methane ◽

Oil And Gas Production ◽

Best Estimate

Abstract. We use 2010–2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5∘×0.625∘ resolution in concentrated source regions. The analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTs model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a−1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a−1). The main discrepancy is for the oil and gas production sectors, where we find higher emissions than the GHGI by 35 % and 22 %, respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production, and we find that these are lower than our estimate by a factor of 2. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a−1, on the low end of the prior WetCHARTs inventory uncertainty range (14.2 (3.3–32.4) Tg a−1), which calls for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a−1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil and gas production in the eastern US. We also find that oil and gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

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Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations

Remote Sensing ◽

10.3390/rs12030375 ◽

2020 ◽

Vol 12 (3) ◽

pp. 375 ◽

Cited By ~ 3

Author(s):

Rajesh Janardanan ◽

Shamil Maksyutov ◽

Aki Tsuruta ◽

Fenjuan Wang ◽

Yogesh K. Tiwari ◽

...

Keyword(s):

United States ◽

High Resolution ◽

The United States ◽

Methane Emissions ◽

Inverse Model ◽

Anthropogenic Emissions ◽

Observation Data ◽

Scale Analysis ◽

Uncertainty Range ◽

Natural Emissions

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

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