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Remote Sensing
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Quantifying Greenhouse Gases Emissions from Remote Sensing Perspective
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Quantifying Greenhouse Gases Emissions from Remote Sensing Perspective

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 143

Special Issue Editor

Dr. Christoffer Karoff

E-Mail Website
Guest Editor
Department of Geoscience, Aarhus University, 8000 Aarhus, Denmark
Interests: astronomy; geoscience and space science

Special Issue Information

Dear Colleagues,

Greenhouse gas (GHG) emissions remain a primary driver of anthropogenic climate change, yet accurately quantifying their fluxes and distributions across diverse regions is a persistent challenge. Recent advances in remote sensing, encompassing satellite missions, airborne campaigns and ground-based instruments now provide unprecedented spatial and temporal resolution for monitoring key gases such as carbon dioxide, methane and nitrous oxide. This Special Issue aims to collect original research articles, methodological developments and comprehensive reviews focusing on the acquisition, processing and interpretation of remote sensing data for quantifying GHG emissions. We welcome submissions that address innovative sensor technologies, retrieval algorithms and assimilation frameworks, and modeling and inversion techniques that transform these observations into robust emissions estimates.

Contributions may discuss satellite-based observing systems, lidar and imaging spectroscopy approaches, machine learning applications or new data fusion methods. We especially encourage intercomparison studies that highlight uncertainties and best practices across regional to global scales and reports on the successful integration of remote sensing data into policy-relevant frameworks for climate mitigation.

Dr. Christoffer Karoff
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • greenhouse gas emissions
  • satellite remote sensing
  • atmospheric inversion
  • data assimilation
  • climate change monitoring
  • GHG retrieval algorithms
  • environmental policy

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Published Papers (1 paper)

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Research

20 pages, 2021 KiB  
Article
High-Resolution Inversion of GOSAT-2 Retrievals for Sectoral Methane Emission Estimates During 2019–2022: A Consistency Analysis with GOSAT Inversion
by Rajesh Janardanan, Shamil Maksyutov, Fenjuan Wang, Lorna Nayagam, Yukio Yoshida, Xin Lan and Tsuneo Matsunaga
Remote Sens. 2025, 17(17), 2932; https://doi.org/10.3390/rs17172932 (registering DOI) - 23 Aug 2025
Abstract
We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate [...] Read more.
We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate the consistency between emissions estimates derived from these two satellites and to ensure that GOSAT-2 data could seamlessly integrate with the existing data series without disrupting the continuity of flux estimates. This analysis, covering the period from 2019 to 2022, utilized prior anthropogenic emissions data mainly from EDGAR v6 and incorporated additional natural sources and sinks as outlined by global methane budget, 2020. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral basis, we found notable regional differences in the flux estimates. While GOSAT inversion estimates ~8 Tg a−1 more anthropogenic emissions for China and around 4 Tg a−1 more wetland emissions for Brazil and Indonesia, the posterior error distribution suggests that GOSAT-2 inversion is closer to surface observations over Asia. These discrepancies are found in regions with significant differences in XCH4 data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. These regional biases persist due to limited representative surface reference sites for Level 2 bias correction. The relatively lower data volume from GOSAT also introduces seasonal biases in the flux estimates when the quality filtering of Level 2 data persistently reduces usable observations during certain seasons, resulting in inadequate representation of the seasonal cycle in regions such as East Asia. Similarly, in tropical South America, where the model is relatively under-constrained by the limited surface observations, the lower data volume of GOSAT-2 suffers. While the two inversions exhibit consistent overall performance across North America and Europe, the GOSAT-2-based inversion demonstrates a better performance over East Asia. Therefore, while the two satellite datasets are broadly consistent, considering the fact that the biases in the XCH4 data overlap with regions under-constrained by surface observations, establishing additional surface reference measurement sites is desirable to ensure consistent inversion results. Full article
(This article belongs to the Special Issue Quantifying Greenhouse Gases Emissions from Remote Sensing Perspective)
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