Satellite Remote Sensing of Quantifying Greenhouse Gases Emissions
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 75
Special Issue Editors
Interests: atmospheric CO2 fluxes; satellite remote sensing; greenhouse gas inversion modeling; machine learning; climate extremes
Interests: greenhouse gas emissions; inverse analysis; satellite methane observations
Special Issue Information
Dear Colleagues,
Satellite remote sensing plays an essential role in quantifying greenhouse gas (GHG) emissions, offering global-scale observations with increasing spatial, temporal, and spectral resolutions. The growing availability of satellite data from missions such as GOSAT, OCO-2, TROPOMI, and TanSat provides critical opportunities to track anthropogenic and natural sources and sinks of CO2, CH4, and other GHGs.
This Special Issue will highlight recent advances in satellite-based GHG monitoring, with a particular focus on innovative retrieval algorithms, inversion techniques, and emerging applications. We welcome contributions that introduce novel methods, improve accuracy in flux estimation, or explore under-represented regions and sectors (e.g., urban, agricultural, wetland, or wildfire emissions). Integration with in situ observations, ground-based networks, model simulations, or AI-driven analysis is also encouraged.
We especially invite submissions that showcase new applications, algorithmic improvements, multi-platform data fusion, or machine learning use to enhance GHG data interpretation. Our overarching goal is to foster progress in satellite-based emission monitoring and provide insights to support climate policy and mitigation strategies.
Dr. Ruixue Lei
Dr. Xueying Yu
Guest Editors
Dr. Leyang Feng
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- greenhouse gas emissions
- satellite remote sensing
- CO2, CH4 retrieval
- inversion algorithms
- data assimilation
- OCO-2, TROPOMI, GOSAT
- machine learning
- emission monitoring
- carbon flux estimation
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