Integrating bottom-up and satellite methods to quantify methane emissions in South America
Sarah Hancock
Understanding methane processes
Reducing methane emissions
South America is underrepresented in top-down methane research
*Simple search using OpenAlex: any papers with "methane” and “inversion”/"inverse modeling" /"top-down" in the title
Number of top-down methane manuscripts from 2015-2025 by continent
South American countries among highest emitters
South America is a key player in the global methane budget
East et al., 2025
Brazil
Venezuela
Colombia
Argentina
Large contributor to recent global methane surge
He et al., 2025
Methane mitigation requires region-specific strategies
South America
United States
Feed additives
Manure digesters
Higher cattle productivity
Silvopasture
Feedlots
Grazing cattle
Satellite observations of methane concentrations can improve emission estimates
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#
X
CH4
=
Total
CH4
Analytical inversion provides top-down emission estimate based on satellite observations
Prior estimate
Predicted concentrations
Observed atmospheric concentrations
Improved emission estimate
Relate emissions to bottom-up processes
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#
X
CH4
=
Total
CH4
Analytical inversion provides top-down emission estimate based on satellite observations
Prior estimate
Predicted concentrations
Observed atmospheric concentrations
Improved emission estimate
Relate emissions to bottom-up processes
Emissions are underestimated in almost all sectors, �but especially livestock
9
Two different high-resolution inversions over South America find similar results
| Hancock et al. (2025) | East et al. (2025) |
Prior estimate of anthropogenic emissions | UNFCCC reports spatially distributed using EDGARv6, GFEIv2 | UNFCCC reports spatially distributed using EDGARv8, GFEIv3 |
Satellite observations | GOSAT and blended TROPOMI | Blended TROPOMI |
Year of emission estimate | 2021 | 2023 |
Number of optimized elements | ~600 | ~1300 |
Integrated Methane Inversion (IMI) version | 1.1 | 2.0 |
Prior estimate of wetland emissions | WetCHARTs, LPJ | LPJ |
Hancock et al.
(2025)
East et al.
(2025)
How much methane do countries emit, and from what sectors?
10
What’s missing in our bottom-up estimates of emissions?
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#
X
CH4
=
Total
CH4
South America is complex and heterogeneous
Inversion resolution is limited at continental-scale
Local expertise is needed to link results to infrastructure
TROPOMI and GOSAT
12x12 km2 resolution
IMEO baseline science studies integrate top-down and bottom-up methods
to improve national emission inventories
Led by Colombian researchers
Rodrigo Jimenez
Universidad Nacional de Colombia
Hancock et al. (2025), submitted to ACP
Collaborating with local scientists to improve emission inventories so we can better relate results to bottom-up processes: case study over Colombia
12
Cattle vaccination records used to improve livestock inventory
High-resolution surface water extent map (GLWDv2) used to improve wetland inventory
Hancock et al. (2025), submitted to ACP
Higher resolution results and local infrastructure data allow us to provide stronger recommendations for improving the national inventories
Hancock et al. (2025), submitted to ACP
Top-down emissions
Top-down
Bottom-up
Recommending larger emission factors for Colombian open-pit coal mines
Hancock et al. (2025), submitted to ACP
Top-down
Bottom-up
Assessing livestock emissions and intensity by province
15
Hancock et al. (2025), submitted to ACP
Livestock measurement team led by Ciniro Costa Jr
Top-down
Bottom-up
Satellite observations are useful, but local measurements and bottom-up calculations are essential for understanding methane and driving policy change
16
In-situ measurements
Area flux satellite observations
Point source observations
Bottom-up calculations
Measurements sites from NOAA CarbonTracker-CH4
NOAA Global Greenhouse Gas Reference Network
Conclusions