Remote Sensing for Greenhouse Gases from Natural Sources
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".
Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 3141
Special Issue Editors
Interests: gas and energy release from volcanic and non-volcanic systems; natural degassing in seismic areas; gas hazard; dispersion of pollutants in atmosphere
Special Issue Information
Nowadays, the growth of greenhouse gases in the Earth’s atmosphere is one of the most impactful scientific research topics. Although most greenhouse gas emissions result from industrial and anthropogenic sources, a significant contribution also comes from natural sources. This is especially true for carbon dioxide (CO2) and methane (CH4), two of the main Earth greenhouse gases that are massively released in the terrestrial atmosphere by active and quiescent volcanoes, hydrothermal areas, and active tectonic structures.
The quantification of the emissions from these natural sources contributes to constrain the terrestrial C cycle, improving our knowledge about climate stability. Notwithstanding recent studies over the last 15–20 years that have highlighted the weight of natural sources, the current emission estimates are poorly constrained due to a lack of direct measurements and to the difficulty in modeling gas transport, dispersion, and radiative weight from the source to the atmosphere. In fact, measuring these gases in the subaerial environment is challenging due to their mixing with the atmosphere and then their rapid dilution. Proximal remote sensing techniques, performed by airborne and unmanned aerial vehicles (UAVs), would be the ideal approach to measure gas emissions close to the source (at the scale of the volcanic edifice or small-sized hydrothermal areas). On the other hand, data assimilation and remote sensing procedures from satellite platforms would be extremely useful to quantify the global natural budgets in larger zones or from stronger sources, where the amount of natural gas is sufficiently high above the background atmospheric content (e.g., crustal tectonic structures or major fault systems).
The aim of this Special Issue titled “Remote Sensing for Greenhouse Gases from Natural Sources” is to collect innovative studies to update the global estimates of greenhouse gas emissions, in particular of CO2 and CH4 as major contributors.
Dr. Domenico Granieri
Dr. Matteo Cerminara
Guest Editors
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
- Natural degassing
- C cycle
- Climate change
- Proximal remote sensing
- Unmanned aerial vehicles
- Radiative transfer models
- Radiative forcing
- Gas transport and diffusion models
- Data assimilation
