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Remote Sensing
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Remote Sensing of Greenhouse Gas Emissions II
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Remote Sensing of Greenhouse Gas Emissions II

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

Deadline for manuscript submissions: 10 May 2024 | Viewed by 2133

Special Issue Editors

Dr. Aki Tsuruta

E-Mail Website
Guest Editor
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Interests: carbon cycle; remote sensing of greenhouse gases; atmospheric greenhouse gas transport; atmospheric inversion; atmospheric methane; greenhouse gas fluxes
Special Issues, Collections and Topics in MDPI journals
Dr. Tuula Aalto

E-Mail Website
Guest Editor
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Interests: atmospheric carbon dioxide and methane concentrations and ecosystem fluxes; greenhouse gas modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The remote sensing of atmospheric greenhouse gases (GHGs) and the Earth’s surface provides possibilities for quantifying GHG fluxes, as well as their regional and global budgets, trends, spatial distributions, and seasonality. Observations of GHGs and other atmospheric tracers enable the quantification and evaluation of these compounds, originating from both anthropogenic and natural processes, and inform atmospheric chemistry. Remote sensing observations of vegetation activities and hydrological and cryospheric status on land, such as vegetation type, greenness, leaf area, precipitation, inundation, soil moisture, and snow and ice, can provide valuable information about ecosystem states. Current developments can also reveal emissions due to human activities at high resolutions, identifying point sources. The assimilation of Earth Observation (EO) data into models opens possibilities for novel modelling approaches and avenues for reducing uncertainties in GHG flux estimates.

This Special Issue invites contributions that present remote sensing applications providing the means for GHG flux quantifications, including (but not limited to) GHG sources and sinks inferred from satellites’ GHG and EO data, the utilization of those data in process-based land ecosystems modelling and atmospheric inverse modelling, variations in the atmospheric abundance of carbon gases, and the application of multiple tracers from satellite platforms.

This Special Issue is the second edition of the Special Issue “Remote Sensing of Greenhouse Gas Emissions”.

Dr. Aki Tsuruta
Dr. Tuula Aalto
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

  • earth’s carbon cycle
  • greenhouse gas flux quantification
  • atmospheric greenhouse gases
  • ecosystem fluxes
  • land–climate interaction
  • earth observations
  • data assimilation
  • atmospheric inversion
  • terrestrial ecosystem modelling

Related Special Issue

Published Papers (3 papers)

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Research

22 pages, 1478 KiB  
Article
Assessing Greenhouse Gas Monitoring Capabilities Using SolAtmos End-to-End Simulator: Application to the Uvsq-Sat NG Mission
by Cannelle Clavier, Mustapha Meftah, Alain Sarkissian, Frédéric Romand, Odile Hembise Fanton d’Andon, Antoine Mangin, Slimane Bekki, Pierre-Richard Dahoo, Patrick Galopeau, Franck Lefèvre, Alain Hauchecorne and Philippe Keckhut
Remote Sens. 2024, 16(8), 1442; https://doi.org/10.3390/rs16081442 - 18 Apr 2024
Viewed by 371
Abstract
Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a [...] Read more.
Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a constellation of small satellites equipped with miniaturized spectrometers having a spectral resolution of a few nanometers. The objective of this study is to describe expected results that can be obtained with a single satellite named Uvsq-Sat NG. The SolAtmos end-to-end simulator and its three tools (IRIS, OptiSpectra, and GHGRetrieval) were developed to evaluate the performance of the spectrometer of the Uvsq-Sat NG mission, which focuses on measuring the main GHGs. The IRIS tool was implemented to provide Top-Of-Atmosphere (TOA) spectral radiances. Four scenes were analyzed (pine forest, deciduous forest, ocean, snow) combined with different aerosol types (continental, desert, maritime, urban). Simulated radiance spectra were calculated based on the wavelength ranges of the Uvsq-Sat NG, which spans from 1200 to 2000 nm. The OptiSpectra tool was used to determine optimal observational settings for the spectrometer, including Signal-to-Noise Ratio (SNR) and integration time. Data derived from IRIS and OptiSpectra served as input for our GHGRetrieval simulation tool, developed to provide greenhouse gas concentrations. The Levenberg–Marquardt algorithm was applied iteratively to fine-tune gas concentrations and model inputs, aligning observed transmittance functions with simulated ones under given environmental conditions. To estimate gas concentrations (CO2, CH4, O2, H2O) and their uncertainties, the Monte Carlo method was used. Based on this analysis, this study demonstrates that a miniaturized spectrometer onboard Uvsq-Sat NG is capable of observing different scenes by adjusting its integration time according to the wavelength. The expected precision for each measurement is of the order of a few ppm for carbon dioxide and less than 25 ppb for methane. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions II)
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15 pages, 10469 KiB  
Article
Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data
by Hongzhou Wang, Xiangtao Fan, Hongdeng Jian and Fuli Yan
Remote Sens. 2024, 16(6), 1023; https://doi.org/10.3390/rs16061023 - 14 Mar 2024
Viewed by 699
Abstract
Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the [...] Read more.
Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the use of a matched filter, developing a novel methane detection algorithm for Sentinel-2 imagery. Compared to existing algorithms, this algorithm does not require selecting methane-free images from historical imagery in methane-sensitive bands, but estimates the background spectral information across the entire scene to extract methane gas signals. We tested the algorithm using simulated Sentinel-2 datasets. The results indicated that the newly proposed algorithm effectively reduced artifacts and noise. It was then validated in a known methane emission point source event and a controlled release experiment for its ability to quantify point source emission rates. The average estimated difference between the new algorithm and other algorithms was about 34%. Compared to the actual measured values in the controlled release experiment, the average estimated values ranged from −48% to 42% of the measurements. These estimates had a detection limit ranging from approximately 1.4 to 1.7 t/h and an average error percentage of 19%, with no instances of false positives reported. Finally, in a real case scenario, we demonstrated the algorithm’s ability to precisely locate the source position and identify, as well as quantify, methane point source emissions. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions II)
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23 pages, 1476 KiB  
Article
Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling
by Anttoni Erkkilä, Maria Tenkanen, Aki Tsuruta, Kimmo Rautiainen and Tuula Aalto
Remote Sens. 2023, 15(24), 5719; https://doi.org/10.3390/rs15245719 - 13 Dec 2023
Viewed by 708
Abstract
Drivers of natural high-latitude biogenic methane fluxes were studied by combining atmospheric inversion modelling results of methane fluxes (CTE-CH4 model) with datasets on permafrost (ESA Permafrost CCI), climate (Köppen–Geiger classes) and wetland classes (BAWLD) and seasonality of soil freezing (ESA SMOS F/T) for [...] Read more.
Drivers of natural high-latitude biogenic methane fluxes were studied by combining atmospheric inversion modelling results of methane fluxes (CTE-CH4 model) with datasets on permafrost (ESA Permafrost CCI), climate (Köppen–Geiger classes) and wetland classes (BAWLD) and seasonality of soil freezing (ESA SMOS F/T) for the years 2011–2019. The highest emissions were found in the southern parts of the study region, while areas with continuous permafrost, tundra climate, and tundra wetlands had the lowest emissions. The magnitude of the methane flux per wetland area followed the order of permafrost zones excluding non-permafrost, continuous permafrost having the smallest flux and sporadic the largest. Fens had higher fluxes than bogs in the thaw period, but bogs had higher fluxes in the colder seasons. The freezing period when the soil status is between complete thaw and frozen contributed to annual emissions more in the warmest regions studied than in other regions. In the coldest areas, freezing period fluxes were lower and closer to wintertime values than elsewhere. Emissions during freezing periods were smaller than those during winter periods, but were of comparable magnitude in warm regions. The contribution of the thaw period to the total annual emission varied from 86% in warmest areas to 97% in the coldest areas, suggesting that the longest winter periods did not contribute significantly to the annual budget. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gas Emissions II)
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