Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches.
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| Title: | Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches. |
|---|---|
| Authors: | Alem, Belachew Beyene1,2,3 (AUTHOR), Chen, Baozhang1,2,4,5 (AUTHOR) baozhang.chen@igsnrr.ac.cn, Zhang, Huifang1,3 (AUTHOR), Iqbal, Umar1,2,4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1336. 32p. |
| Subjects: | Satellite-based remote sensing, Atmospheric methane, Climate change mitigation, Remote sensing, Research methodology, Machine learning, Emission inventories |
| Abstract: | Highlights: What are the main findings? A focused comparative review of remote sensing-based top-down, bottom-up, and integrated approaches for global methane budget estimation. Top-down inversions provide observationally constrained total emissions but struggle with source attribution, with uncertainties of ±5–10% globally. Bottom-up inventories offer sector-specific detail but often miss 20–50% of emissions from super-emitters, particularly in the fossil fuel and waste sectors. The discrepancy between approaches is largest for natural sources (e.g., wetlands: 115–230 Tg/yr bottom-up vs. 159–200 Tg/yr top-down), highlighting a critical area for methodological improvement. What are the implications of the main findings? Integrated approach that synergizes satellite area flux mappers (e.g., TROPOMI) with point source imagers (e.g., GHGSat) and AI-driven inversion techniques are essential for reducing global budget uncertainties to ±15–20%. Advances in satellite remote sensing and machine learning enable the detection and attribution of super-emitters, supporting transparent, near-real-time emission monitoring for climate mitigation policies. Methane (CH4) is a potent greenhouse gas, and accurately estimating its global budget is essential for climate change mitigation. This review provides a comparative synthesis of top-down, bottom-up, and integrated approaches for quantifying methane emissions and sinks, with a particular focus on the role of remote sensing. Top-down methods, leveraging satellite observations from instruments like GOSAT and TROPOMI within atmospheric inversion frameworks (Bayesian, 4D-Var), provide observationally constrained, spatially integrated fluxes, reducing global budget uncertainty to ±5–10%. However, they face challenges in source attribution and rely heavily on transport model accuracy. Conversely, bottom-up approaches, including process-based models (e.g., CLM, DNDC) and emission inventories (e.g., EDGAR), offer detailed, sector-specific insights but are prone to underestimating emissions from super-emitters and diffuse sources like wetlands, with uncertainties often exceeding ±20–40% for individual sectors. Key persistent discrepancies between the two approaches are largest for natural sources (e.g., a 20–40 Tg yr−1 gap for tropical wetlands). Integrated approaches, which synergize top-down atmospheric constraints with bottom-up inventory data, are emerging as the most robust methodology, effectively narrowing the global budget gap and improving confidence. Recent advancements in satellite missions (e.g., MethaneSAT), machine learning algorithms for plume detection, and high-resolution inversion models are transforming monitoring capabilities. However, challenges remain in harmonizing datasets, representing complex microbial processes in models, and expanding observational coverage in data-scarce tropical regions. This review concludes by outlining a future path centered on hybrid inversion frameworks, AI-driven source attribution, and cross-disciplinary collaboration to deliver the actionable methane budgets needed for effective climate policy. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 193715367 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alem%2C+Belachew+Beyene%22">Alem, Belachew Beyene</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Baozhang%22">Chen, Baozhang</searchLink><relatesTo>1,2,4,5</relatesTo> (AUTHOR)<i> baozhang.chen@igsnrr.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huifang%22">Zhang, Huifang</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Iqbal%2C+Umar%22">Iqbal, Umar</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1336. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+methane%22">Atmospheric methane</searchLink><br /><searchLink fieldCode="DE" term="%22Climate+change+mitigation%22">Climate change mitigation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Emission+inventories%22">Emission inventories</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A focused comparative review of remote sensing-based top-down, bottom-up, and integrated approaches for global methane budget estimation. Top-down inversions provide observationally constrained total emissions but struggle with source attribution, with uncertainties of ±5–10% globally. Bottom-up inventories offer sector-specific detail but often miss 20–50% of emissions from super-emitters, particularly in the fossil fuel and waste sectors. The discrepancy between approaches is largest for natural sources (e.g., wetlands: 115–230 Tg/yr bottom-up vs. 159–200 Tg/yr top-down), highlighting a critical area for methodological improvement. What are the implications of the main findings? Integrated approach that synergizes satellite area flux mappers (e.g., TROPOMI) with point source imagers (e.g., GHGSat) and AI-driven inversion techniques are essential for reducing global budget uncertainties to ±15–20%. Advances in satellite remote sensing and machine learning enable the detection and attribution of super-emitters, supporting transparent, near-real-time emission monitoring for climate mitigation policies. Methane (CH4) is a potent greenhouse gas, and accurately estimating its global budget is essential for climate change mitigation. This review provides a comparative synthesis of top-down, bottom-up, and integrated approaches for quantifying methane emissions and sinks, with a particular focus on the role of remote sensing. Top-down methods, leveraging satellite observations from instruments like GOSAT and TROPOMI within atmospheric inversion frameworks (Bayesian, 4D-Var), provide observationally constrained, spatially integrated fluxes, reducing global budget uncertainty to ±5–10%. However, they face challenges in source attribution and rely heavily on transport model accuracy. Conversely, bottom-up approaches, including process-based models (e.g., CLM, DNDC) and emission inventories (e.g., EDGAR), offer detailed, sector-specific insights but are prone to underestimating emissions from super-emitters and diffuse sources like wetlands, with uncertainties often exceeding ±20–40% for individual sectors. Key persistent discrepancies between the two approaches are largest for natural sources (e.g., a 20–40 Tg yr−1 gap for tropical wetlands). Integrated approaches, which synergize top-down atmospheric constraints with bottom-up inventory data, are emerging as the most robust methodology, effectively narrowing the global budget gap and improving confidence. Recent advancements in satellite missions (e.g., MethaneSAT), machine learning algorithms for plume detection, and high-resolution inversion models are transforming monitoring capabilities. However, challenges remain in harmonizing datasets, representing complex microbial processes in models, and expanding observational coverage in data-scarce tropical regions. This review concludes by outlining a future path centered on hybrid inversion frameworks, AI-driven source attribution, and cross-disciplinary collaboration to deliver the actionable methane budgets needed for effective climate policy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091336 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 1336 Subjects: – SubjectFull: Satellite-based remote sensing Type: general – SubjectFull: Atmospheric methane Type: general – SubjectFull: Climate change mitigation Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Emission inventories Type: general Titles: – TitleFull: Comparative Review of Global Methane Budget Estimation: Top-Down, Bottom-Up, and Integrated Approaches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alem, Belachew Beyene – PersonEntity: Name: NameFull: Chen, Baozhang – PersonEntity: Name: NameFull: Zhang, Huifang – PersonEntity: Name: NameFull: Iqbal, Umar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
