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

Deadline for manuscript submissions: 10 March 2026 | Viewed by 6034

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Special Issue Editors

Dr. Ailin Liang

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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

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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

Dr. Simone Lolli

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Guest Editor

Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Contrada S. Loja, 85050 Tito Scalo, PZ, Italy
Interests: lidar; physics of the atmosphere; boundary layer meteorology; cirrus remote sensing
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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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Published Papers (7 papers)

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20 pages, 2674 KB

Open AccessArticle

Estimating Methane Emissions by Integrating Satellite Regional Emissions Mapping and Point-Source Observations: Case Study in the Permian Basin

by Mozhou Gao and Zhenyu Xing

Remote Sens. 2025, 17(18), 3143; https://doi.org/10.3390/rs17183143 - 10 Sep 2025

Viewed by 271

Abstract

Methane (CH₄) is known as the most potent greenhouse gas in the short term. With the growing urgency of mitigating climate change and monitoring CH₄ emissions, many emerging satellite systems have been launched in the past decade to observe CH₄ and other greenhouse gases from space. These satellites are either capable of pinpointing and quantifying super emitters or deriving regional emissions with a more frequent revisit time. This study aims to reconcile emissions estimated from point source satellites and those from regional mapping satellites, and to investigate the potential of integrating point-based quantification and regional-based quantification techniques. To do that, we quantified CH₄ emissions from the Permian Basin separately by applying the divergence method to the TROPOMI Level-2 data product, as well as an event-based approach using CH₄ plumes quantified by Carbon Mapper systems. The resulting annual CH₄ emissions estimates from the Permian Basin in 2024 are 1.83 ± 0.96 Tg and 1.26 [0.78, 2.02] Tg for divergence and event-based methods, respectively. The divergence-based emissions estimate shows a more comprehensive spatial distribution of emissions across the Permian Basin, whereas the event-based approach highlights the grid cells with the short-duration super-emitters. The emissions from grids with detectable emissions under both methods show strong agreement (R² ≈ 0.642). After substituting the overlap cells’ values from divergence-based emissions estimation with those from event-based estimation, the combined emissions estimate is 2.68 [1.88, 3.54] Tg, which is reconciled with Permian Basin emissions estimates from previous studies. We found that CH₄ emissions from the Permian Basin gradually reduced over the past five years. Furthermore, this case study indicates the potential for integrating estimations from both methods to generate a more comprehensive regional emissions estimate. 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, 16139 KB

Open AccessArticle

XCH₄ Spatiotemporal Variations in a Natural-Gas-Exploiting Basin with Intensive Agriculture Activities Using Multiple Remote Sensing Datasets: Case from Sichuan Basin, China

by Tengnan Wang and Yunpeng Wang

Remote Sens. 2025, 17(15), 2695; https://doi.org/10.3390/rs17152695 - 4 Aug 2025

Viewed by 422

Abstract

The Sichuan Basin is a natural-gas-exploiting area with intensive agriculture activities. However, the spatial and temporal distribution of atmospheric methane concentration and the relationships with intensive agriculture and natural gas extraction activities are not well investigated. In this study, a long-term (2003–2021) dataset of column-averaged dry-air mole fraction of methane (XCH₄) over the Sichuan Basin and adjacent regions was built by integrating multi-satellite remote sensing data (SCIAMACHY, GOSAT, Sentinel-5P), which was calibrated using ground station data. The results show a strong correlation and consistency (R = 0.88) between the ground station and satellite observations. The atmospheric CH₄ concentration of the Sichuan Basin showed an overall higher level (around 20 ppb) than that of the whole of China and an increasing trend in the rates, from around 2.27 ppb to 10.44 ppb per year between 2003 and 2021. The atmospheric CH₄ concentration of the Sichuan Basin also exhibits clear seasonal changes (higher in the summer and autumn and lower in the winter and spring) with a clustered geographical distribution. Agricultural activities and natural gas extraction contribute significantly to atmospheric methane concentrations in the study area, which should be considered in carbon emission management. This study provides an effective way to investigate the spatiotemporal distribution of atmospheric CH₄ concentration and related factors at a regional scale with natural and human influences using multi-source satellite remote sensing data. Full article

(This article belongs to the Special Issue Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications)

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18 pages, 2395 KB

Open AccessArticle

Theoretical Potential of TanSat-2 to Quantify China’s CH₄ Emissions

by Sihong Zhu, Dongxu Yang, Liang Feng, Longfei Tian, Yi Liu, Junji Cao, Minqiang Zhou, Zhaonan Cai, Kai Wu and Paul I. Palmer

Remote Sens. 2025, 17(13), 2321; https://doi.org/10.3390/rs17132321 - 7 Jul 2025

Viewed by 623

Abstract

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

(This article belongs to the Special Issue Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications)

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16 pages, 11844 KB

Open AccessArticle

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

Cited by 1 | Viewed by 915

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 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 KB

Open AccessArticle

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 935

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 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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16 pages, 4727 KB

Open AccessTechnical Note

Exploitation of OCO-3 Satellite Data to Analyse Carbon Dioxide Emissions from the Mt. Etna Volcano

by Vito Romaniello and Gaetana Ganci

Remote Sens. 2025, 17(11), 1918; https://doi.org/10.3390/rs17111918 - 31 May 2025

Viewed by 1207

Abstract

The Orbiting Carbon Observatory-3 (OCO-3) mission provides a new perspective for studying atmospheric carbon dioxide (CO₂). Here we assess the potentiality of OCO-3 satellite acquisitions to analyse and monitor the CO₂ emissions from Mt. Etna volcano. While OCO-3 data are well-suited for gas analysis on a regional spatial scale, they have not yet been widely utilised for studying volcanic carbon dioxide emissions. The Snapshot Area Map (SAM) acquisition mode enables the capture of targeted snapshots over volcanic regions, allowing for the measurement of CO₂ concentrations in the vicinity of volcanic structures. In this work, we analyse 62 OCO-3 images acquired between 2020 and 2023, focusing on measurements within a 20 km radius of Mt. Etna’s summit, where the main craters are located. Atmospheric CO₂ concentrations are examined as a function of distance from the summit, and assuming a linear decreasing trend, the angular coefficient is computed. Lower angular coefficient values may indicate a stronger volcanic CO₂ contribution. Considering both the number of sampled pixels in each OCO-3 snapshot and the associated uncertainties in the angular coefficient calculation, we identify five days with potentially significant CO₂ emissions from Mt. Etna, likely associated with specific volcanic activity phases. The eruptive activity on these five days is further investigated, revealing a possible correlation between elevated gas emissions and intense volcanic phenomena, such as lava fountains. This assessment is supported by thermal activity analyses using SEVIRI, MODIS, and VIIRS satellite data. Full article

(This article belongs to the Special Issue Advances in Atmospheric Greenhouse Gases Observation and Remote Sensing Applications)

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18 pages, 3442 KB

Open AccessTechnical 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

Cited by 2 | Viewed by 937

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 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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