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Trend, Progress and Application of Remote Sensing for Atmospheric Environment...
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Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 4424

Special Issue Editors

Dr. Shaohua Zhao

E-Mail Website
Guest Editor
Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China
Interests: atmospheric remote sensing; global changes; environment of remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Xingying Zhang

E-Mail Website
Guest Editor
National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
Interests: space observation of atmospheric composition; validation and calibration of remote sensing; impacts on environment and climate change
Prof. Dr. Wei Xiong

E-Mail Website
Guest Editor
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: hyperspectral remote sensing; atmospheric inversion; carbon satellite payload technology
Prof. Dr. Zhongwei Huang

E-Mail Website
Guest Editor
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Interests: atmospheric physics; air pollution studies; aerosol science and technology; aerosol–cloud climate interactions; dust; bioaerosols
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Zhu

E-Mail Website
Guest Editor
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: atmospheric chemistry; remote sensing of trace gases; data assimilation; air quality; atmosphere-land-ocean interactions
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Qin

E-Mail Website
Guest Editor
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: atmospheric environment remote sensing; remote sensing information analysis; carbon monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongting Wang

E-Mail Website
Guest Editor
Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China
Interests: air quality remote sensing; radiative transfer modeling; remote sensing for aerosol; particle matter; greenhouse gases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The atmosphere is a dynamic component of Earth's system, playing a pivotal role in climate regulation, weather forecasting, and environmental health. With the increasing need to monitor atmospheric changes accurately and efficiently, remote sensing has emerged as a critical tool for capturing comprehensive data on atmospheric conditions across temporal and spatial scales.

This Special Issue will collate cutting-edge research and advancements in applications of remote sensing technologies in atmospheric studies. We invite researchers, scientists, and practitioners to contribute original research articles, reviews, and case studies that highlight innovative methodologies, applications, and insights in the field of atmospheric remote sensing. The goal is to foster a multidisciplinary dialogue that will enhance our understanding and capabilities when it comes to monitoring the atmospheric environment.

Topics of interest include, but are not limited to, the following:

  • Novel Remote Sensing Instruments and Sensors;
  • Satellite and Airborne Remote Sensing Techniques;
  • Atmospheric Composition Analysis;
  • Weather and Climate Modeling;
  • Air Quality Monitoring;
  • Greenhouse Gas Monitoring;
  • Aerosols and Cloud Studies;
  • Validation and Calibration;
  • Machine Learning Applications;
  • Data Fusion and Integration;
  • Atmospheric Environment Management.

We encourage submissions that not only present significant findings but also discuss their implications for future research and practical applications in atmospheric science. All manuscripts will undergo a rigorous peer review process to ensure high quality across the published contributions.

We look forward to your valuable contributions to this Special Issue, which will advance the field of atmospheric remote sensing and its applications in better understanding and protecting our environment.

Dr. Shaohua Zhao
Dr. Xingying Zhang
Prof. Dr. Wei Xiong
Prof. Dr. Zhongwei Huang
Dr. Lei Zhu
Prof. Dr. Kai Qin
Dr. Zhongting Wang
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

  • atmospheric monitoring
  • satellite observations
  • air quality
  • aerosols and clouds
  • greenhouse gas
  • climate change
  • data fusion
  • machine learning

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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Research

21 pages, 5200 KiB  
Article
GNSS Precipitable Water Vapor Prediction for Hong Kong Based on ICEEMDAN-SE-LSTM-ARIMA Hybrid Model
by Jie Zhao, Xu Lin, Zhengdao Yuan, Nage Du, Xiaolong Cai, Cong Yang, Jun Zhao, Yashi Xu and Lunwei Zhao
Remote Sens. 2025, 17(10), 1675; https://doi.org/10.3390/rs17101675 - 9 May 2025
Viewed by 189
Abstract
Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with [...] Read more.
Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters. Full article
(This article belongs to the Special Issue Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change)
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21 pages, 12319 KiB  
Article
Aerosol Retrieval Method Using Multi-Angle Data from GF-5 02 DPC over the Jing–Jin–Ji Region
by Zhongting Wang, Shikuan Jin, Cheng Chen, Zhen Liu, Siyao Zhai, Hui Chen, Chunyan Zhou, Ruijie Zhang and Huayou Li
Remote Sens. 2025, 17(8), 1415; https://doi.org/10.3390/rs17081415 - 16 Apr 2025
Viewed by 329
Abstract
The Directional Polarimetric Camera (DPC) aboard the Chinese GaoFen-5 02 satellite is designed to monitor aerosols and particulate matter (PM). In this study, we retrieved the aerosol optical depth (AOD) over the Jing–Jin–Ji (JJJ) region using multi-angle data from the DPC, employing a [...] Read more.
The Directional Polarimetric Camera (DPC) aboard the Chinese GaoFen-5 02 satellite is designed to monitor aerosols and particulate matter (PM). In this study, we retrieved the aerosol optical depth (AOD) over the Jing–Jin–Ji (JJJ) region using multi-angle data from the DPC, employing a combination of dark dense vegetation (DDV) and multi-angle retrieval methods. The added value of our method included novel hybrid methodology and good practical performance. The retrieval process involves three main steps: (1) deriving AOD from DPC data collected at the nadir angle using linear parameters of land surface reflectance between the blue and red bands from the MOD09 surface product; (2) after performing atmospheric correction with the retrieved AOD, calculating the variance of the normalized reflectance at all observation angles; and (3) leveraging the calculated variance to obtain the final AOD values. AOD images over the JJJ region were successfully retrieved from DPC data collected between January and June 2022. To validate the retrieval method, we compared our results with aerosol products from the AErosol RObotic NETwork (AERONET) Beijing-RADI site, as well as aerosol data from MODerate-resolution Imaging Spectroradiometer (MODIS) and the generalized retrieval of atmosphere and surface properties (GRASP)/models over the same site. In terms of validation metrics, the correlation coefficient (R2) and root mean square error (RMSE) indicated that our method achieved high accuracy, with an R2 value greater than 0.9 and an RMSE below 0.1, closely aligning with the performance of GRASP. Full article
(This article belongs to the Special Issue Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change)
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17 pages, 4838 KiB  
Article
XCO2 Data Full-Coverage Mapping in China Based on Random Forest Models
by Ruizhi Chen, Zhongting Wang, Chunyan Zhou, Ruijie Zhang, Huizhen Xie and Huayou Li
Remote Sens. 2025, 17(1), 48; https://doi.org/10.3390/rs17010048 - 27 Dec 2024
Viewed by 856
Abstract
Carbon dioxide (CO2) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO2 levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO2 concentrations [...] Read more.
Carbon dioxide (CO2) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO2 levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO2 concentrations and to support the development of climate policies, this study proposes a method based on random forest models to generate a continuous monthly dataset of CO2 column concentration (XCO2) across the entire Chinese region from 2004 to 2023. The study integrates XCO2 satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO2 profile data. Using the random forest algorithm, a complex relationship model was established between XCO2 concentrations and various environmental variables. The goal of this model is to provide XCO2 estimates with enhanced spatial coverage and accuracy. The XCO2 concentrations predicted by the model show a high level of consistency with satellite observations, achieving a correlation coefficient (R-value) of 0.9959 and a root mean square error (RMSE) of 1.1631 ppm. This indicates that the model offers strong predictive accuracy and generalization ability. Additionally, ground-based validation further confirmed the model’s effectiveness, with a correlation coefficient (R-value) of 0.956 when compared with TCCON site observation data. Full article
(This article belongs to the Special Issue Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change)
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17 pages, 10261 KiB  
Article
Characteristics and Source Analysis of Ozone Pollution in Tianjin from 2013 to 2022
by Shuo Dong, Pengfei Ma, Xingchuan Yang, Nana Luo, Linhan Chen, Lili Wang, Hanyang Song, Shaohua Zhao and Wenji Zhao
Remote Sens. 2024, 16(21), 3970; https://doi.org/10.3390/rs16213970 - 25 Oct 2024
Viewed by 1023
Abstract
This study has analyzed ozone pollution in Tianjin from 2013 to 2022, focusing on the relationships between ozone distribution, meteorological conditions, and precursor substances. A method for identifying high-value areas of ozone precursors using the Ozone Sensitivity Factor (FNR) has been introduced. Results [...] Read more.
This study has analyzed ozone pollution in Tianjin from 2013 to 2022, focusing on the relationships between ozone distribution, meteorological conditions, and precursor substances. A method for identifying high-value areas of ozone precursors using the Ozone Sensitivity Factor (FNR) has been introduced. Results show that the average ozone concentration in Tianjin has been 100.608 µg/m3, with an annual growth rate of 2.84 µg·m⁻3·yr⁻¹. Tianjin has ranked among the top provinces and urban agglomerations in China for both ozone concentration and growth rate. Ozone levels have peaked in summer, followed by spring, autumn, and winter, while the growth rate has been highest in spring. This indicates that ozone pollution extends from summer into spring and autumn. An analysis of six ozone pollution events reveals significant regional transmission impacts from northern Hebei and Inner Mongolia, contributing over 30%, with additional significant contributions from southern and southwestern Hebei and western Shandong. In terms of controlling ozone precursors, high-HCHO-value areas have been identified. The correlation between areas of high HCHO values and ground-level ozone concentrations was 0.56339 during the ozone season and 0.2214 during the non-ozone season, both of which improved identification accuracy to varying degrees, suggesting that targeting precursor emissions in these areas could enhance pollution mitigation efforts. Full article
(This article belongs to the Special Issue Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change)
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18 pages, 6507 KiB  
Article
Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite
by Ruijie Zhang, Hui Chen, Ruizhi Chen, Chunyan Zhou, Qing Li, Huizhen Xie and Zhongting Wang
Remote Sens. 2024, 16(21), 3944; https://doi.org/10.3390/rs16213944 - 23 Oct 2024
Cited by 2 | Viewed by 1099
Abstract
The use of satellite data to estimate PM2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived [...] Read more.
The use of satellite data to estimate PM2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM2.5 retrieval. We identified the optimal conditions for the effective estimation of PM2.5. The quantitative results indicated that, under these optimal conditions, the PM2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R2 of 0.9272 and an RMSE of 7.38 µg·m−3. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R2 of 0.9099 and RMSE of 7.42 µg·m−3, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R2 of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM2.5 retrieval and providing support for PM2.5 estimation through multi-angle polarized data. Full article
(This article belongs to the Special Issue Trend, Progress and Application of Remote Sensing for Atmospheric Environment and Climate Change)
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