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Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications
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Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1825

Special Issue Editors

Dr. Ailin Liang

E-Mail Website
Guest Editor
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: lidar remote sensing; carbon dioxide; greenhouse gases; climate modeling; atmospheric pollution
Dr. Yawen Kong

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: GHG monitoring; inverse modelling of carbon sources and sinks; satellite-based GHG emission accounting
Prof. Dr. Simone Lolli

E-Mail Website
Guest Editor
CNR-IMAA, Consiglio Nazionale delle Ricerche, 85050 Potenza, Italy
Interests: radiative transfer; aerosols; lidar; image fusion; clouds; precipitations; interaction aerosol-clouds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Atmospheric greenhouse gases play a crucial role in the Earth's climate system. The accurate measurement and understanding of their concentrations, distributions, and temporal variations are essential for assessing the impact of human activities on climate change and for formulating effective mitigation strategies. Over the past few decades, significant progress has been made in both in situ and remote sensing techniques for observing atmospheric greenhouse gases. Ground-based stations have been continuously improving their precision and temporal resolution, while satellite-based remote sensing has enabled global-scale monitoring with increasing spatial and spectral resolution. These advancements have provided a wealth of data that contribute to our knowledge of the global carbon cycle, greenhouse gas sources and sinks, and climate change projections.

This Special Issue focuses on the recent progress in atmospheric greenhouse gas observation and remote sensing applications. It aims to collect studies that showcase the state-of-the-art technologies, methods, and scientific findings in this field.

Topics covered in this Special Issue may include, but are not limited to, the following:

  • Greenhouse gas concentration mapping and trend analysis;
  • Remote sensing sensor development and calibration;
  • Atmospheric transport and dispersion modelling of greenhouse gases;
  • Greenhouse gases data assimilation;
  • Greenhouse gas emissions estimation;
  • Validation and comparison of different observation techniques;
  • New algorithms and data processing techniques for greenhouse gas remote sensing;
  • Impacts of greenhouse gas changes on climate and ecosystems;
  • Case studies of regional or local greenhouse gas monitoring and analysis.

Dr. Ailin Liang
Dr. Yawen Kong
Prof. Dr. Simone Lolli
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

  • greenhouse gas remote sensing
  • carbon dioxide
  • methane
  • OCO-2/3 GOSAT TROPOMI TANSAT DQ-1
  • carbon flux
  • carbon assimilation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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16 pages, 11844 KiB  
Article
Deep Learning Methods for Inferring Industrial CO2 Hotspots from Co-Emitted NO2 Plumes
by Erchang Sun, Shichao Wu, Xianhua Wang, Hanhan Ye, Hailiang Shi, Yuan An and Chao Li
Remote Sens. 2025, 17(7), 1167; https://doi.org/10.3390/rs17071167 - 25 Mar 2025
Viewed by 329
Abstract
The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant potential to [...] Read more.
The “top-down” global stocktake (GST) requires the processing of vast volumes of hyperspectral data to derive emission information, placing greater demands on data processing efficiency. Deep learning, leveraging its strengths in the automated and rapid analysis of image datasets, holds significant potential to enhance the efficiency and effectiveness of data processing in the GST. This paper develops a method for detecting carbon dioxide (CO2) emission hotspots using a convolutional neural network (CNN) with short-lived and co-emitted nitrogen dioxide (NO2) as a proxy. To address the data gaps in model parameter training, we constructed a dataset comprising over 210,000 samples of NO2 plumes and emissions based on atmospheric dispersion models. The trained model performed well on the test set, with most samples achieving an identification accuracy above 80% and more than half exceeding 94%. The trained model was also applied to the NO2 column data from the TROPOspheric Monitoring Instrument (TROPOMI) for hotspot detection, and the detections were compared with the MEIC inventory. The results demonstrate that in high-emission areas, the proposed method successfully identifies emission hotspots with an average accuracy of over 80%, showing a high degree of consistency with the emission inventory. In areas with multiple observations from TROPOMI, we observed a high degree of consistency between high NO2 emission areas and high CO2 emission areas from the Global Open-Source Data Inventory for Anthropogenic CO2 (ODIAC), indicating that high NO2 emission hotspots can also indicate CO2 emission hotspots. In the future, as hyperspectral and high spatial resolution remote sensing data for CO2 and NO2 continue to grow, our methods will play an increasingly important role in global data preprocessing and global emission estimation. Full article
(This article belongs to the Special Issue Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications)
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20 pages, 5610 KiB  
Article
Calibration of Short-Wave Infrared Spectrometer for Atmosphere Methane Monitoring
by Haoran Li, Fuqi Si, Liang Xi, Fang Lin, Yu Jiang, Fenglei Liu, Yi Zeng, Yunkun Han and Kaili Wu
Remote Sens. 2025, 17(5), 851; https://doi.org/10.3390/rs17050851 - 28 Feb 2025
Viewed by 410
Abstract
The short-wave infrared (SWIR) grating imaging spectrometer based on indium gallium arsenide (InGaAs) material inverts the atmospheric methane concentration by measuring the scattered light signals in the sky. This study proposes spectral and radiometric calibration methods for the characteristics of the spectrometer, such [...] Read more.
The short-wave infrared (SWIR) grating imaging spectrometer based on indium gallium arsenide (InGaAs) material inverts the atmospheric methane concentration by measuring the scattered light signals in the sky. This study proposes spectral and radiometric calibration methods for the characteristics of the spectrometer, such as the small-area array, high signal-to-noise ratio, and high spectral resolution. Four spectral response function models, namely, the Gauss, Lorentz, Voigt and super-Gaussian models, were compared during spectral calibration. With a fitting residual of 0.032, the Gauss model was found to be the most suitable spectral response function for the spectrometer. Based on the spectral response function, the spectral range and spectral resolution of the spectrometer were determined to be 1592.4–1677.2 and 0.1867 nm, respectively. In addition, radiometric calibration of the spectrometer was achieved by combining an integrating sphere and linear measuring instrument. Moreover, absolute and relative radiometric calibrations of the spectrometer were performed. The low signal response problem caused by the quantum efficiency of the detector at long wavelength was corrected, and the uncertainty and non-stability uncertainty of absolute radiometric calibration were calculated to be less than 0.2%. Finally, the calibrated spectrometer was used to accurately measure the solar scattering spectrum in the SWIR band, and the solar spectrum was simulated by the radiative transfer model for verification; the measurement error was found to be 5%. Concurrently, a methane sample gas experiment was performed using the integrating-sphere light source, and the measurement error was less than 4%. This fully proves the effectiveness of the spectral and radiometric calibrations of the SWIR spectrometer and strongly guarantees a subsequent, rapid and accurate inversion of atmospheric methane concentration. Full article
(This article belongs to the Special Issue Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications)
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Other

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18 pages, 3442 KiB  
Technical Note
Towards the Optimization of TanSat-2: Assessment of a Large-Swath Methane Measurement
by Sihong Zhu, Dongxu Yang, Liang Feng, Longfei Tian, Yi Liu, Junji Cao, Kai Wu, Zhaonan Cai and Paul I. Palmer
Remote Sens. 2025, 17(3), 543; https://doi.org/10.3390/rs17030543 - 5 Feb 2025
Viewed by 601
Abstract
To evaluate the potential of an upcoming large-swath satellite for estimating surface methane (CH₄) fluxes at a weekly scale, we report the results from a series of observing system simulation experiments (OSSEs) that use an established modeling framework that includes the GEOS-Chem 3D [...] Read more.
To evaluate the potential of an upcoming large-swath satellite for estimating surface methane (CH₄) fluxes at a weekly scale, we report the results from a series of observing system simulation experiments (OSSEs) that use an established modeling framework that includes the GEOS-Chem 3D atmospheric transport model and an ensemble Kalman filter. These experiments focus on the sensitivity of CH₄ flux estimates to systematic errors (μ) and random errors (σ) in the column average methane (XCH4) measurements. Our control test (INV_CTL) demonstrates that with median errors (μ = 1.0 ± 0.9 ppb and σ = 6.9 ± 1.6 ppb) in XCH₄ measurements over a 1000 km swath, global CH4 fluxes can be estimated with an accuracy of 5.1 ± 1.7%, with regional accuracies ranging from 3.8% to 21.6% across TransCom sub-continental regions. The northern hemisphere mid-latitudes show greater reliability and consistency across varying μ and σ levels, while tropical and boreal regions exhibit higher sensitivity due to limited high-quality observations. In σ-sensitive regions, such as the North American boreal zone, expanding the swath width from 1000 km to 3000 km significantly reduces discrepancies, while such adjustments provide limited improvements for μ-sensitive regions like North Africa. For TanSat-2 mission, with its elliptical medium Earth orbit and 1500 km swath width, the global total estimates achieved an accuracy of 3.1 ± 2.2%. Enhancing the swath width or implementing a dual-satellite configuration is proposed to further improve TanSat-2 inversion performance. Full article
(This article belongs to the Special Issue Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications)
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