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Advanced Remote Sensing Approaches for Multi-Scale Atmospheric Components Monitoring and Impact Assessment

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3610

Special Issue Editors

Dr. Shengcheng Cui

E-Mail Website
Guest Editor
1. Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
2. Nanhu Laser Laboratory, National University of Defense Technology, Changsha 410073, China
Interests: remote sensing; atmosphere; aerosols; geophysics; atmospheric optics; atmospheric radiation; atmospheric pollution; atmospheric modeling; intelligent forecasting
Dr. Bing Chen

E-Mail Website
Guest Editor
Department of Atmospheric Science, Yunnan University, Kunming 650500, China
Interests: climatology; atmosphere; meteorology; atmospheric physics; climate change; atmospheric environment; climate assessment
Prof. Dr. Yong Han

E-Mail Website
Guest Editor
Advanced Science & Technology of Space and Atmospheric Physics Group (ASAG), School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Interests: remote sensing; atmosphere; atmospheric physics; aerosol; cloud atmospheric components; atmospheric optics; atmospheric environments; atmospheric sounding
Dr. Zhen Wang

E-Mail Website
Guest Editor
Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: radiative transfer; cloud; inverse problems; atmospheric radiation; monte carlo simulation; atmosphere; clouds; meteorology; atmospheric physics; climatology; remote sensing; aerosols
Dr. Zhenzhu Wang

E-Mail
Guest Editor
1. Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
2. Nanhu Laser Laboratory, National University of Defense Technology, Changsha 410073, China
Interests: aerosols; lidar remote sensing; radiative forcing; cloud; atmospheric sciences; polarization; climate change; air quality; detectors; experimental physics; climatology; remote sensing; lidar; clouds; ozone; geophysics; optics; atmospheric physics; cirrus clouds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Atmospheric components such as aerosols, clouds, and trace gases play pivotal roles in radiative transfer simulations, atmospheric optical propagation effect assessments, and climate change studies. These constituents modulate the Earth’s energy balance through scattering, absorption, and emission processes, directly influencing weather patterns, air quality, and long-term climatic trends. Recent advancements in space- and ground-based active and passive remote sensing technologies—including lidar, hyperspectral imagers, and next-generation spectrometers—have enabled the unprecedented multi-scale monitoring of these components, offering critical insights into their spatiotemporal variability and interactions.

This Special Issue focuses on pioneering, innovative, and fundamental research on advanced retrieval algorithms, multi-source data fusion, multi-scale radiative transfer modeling, and impact assessment methodologies. By integrating cutting-edge observational techniques with theoretical and computational advances, this collection aims to address challenges in characterizing atmospheric dynamics, quantifying uncertainties, and improving predictive capabilities for environmental, climate, and optoelectronic engineering applications. The scope includes, but is not limited to, the following:

  • Novel sensor technologies and calibration methods for aerosol, cloud, and trace gas monitoring;
  • Advanced retrieval algorithms and modeling techniques to determine multi-scale atmospheric constituent distributions and their microphysical and optical properties;
  • Multi-source data fusion and assimilation methodologies to resolve spatiotemporal mismatches and enhance resolution;
  • Case studies linking remote sensing observations to radiative transfer, laser propagation, and climate model validation and impact assessments.

We look forward to receiving your valuable contributions.

Dr. Shengcheng Cui
Dr. Bing Chen
Prof. Dr. Yong Han
Dr. Zhen Wang
Dr. Zhenzhu 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 250 words) can be sent to the Editorial Office for assessment.

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

  • aerosols
  • clouds
  • trace gases
  • multi-scale remote sensing
  • lidar
  • satellite
  • radiative transfer
  • atmospheric inversion
  • multi-source data fusion

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Published Papers (4 papers)

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Research

20 pages, 3790 KB  
Article
Characteristics of Planetary Boundary Layer Height (PBLH) over Shenzhen, China: Retrieval Methods and Air Pollution Conditions
by Yaqi Zhou, Yong Han, Zhiyuan Hu, Qicheng Zhou, Yan Liu, Li Dong and Peng Xiao
Remote Sens. 2025, 17(24), 3937; https://doi.org/10.3390/rs17243937 - 5 Dec 2025
Viewed by 182
Abstract
The PBLH affects the intensity of the surface turbulence and the state of pollutant dispersion. Current research on PBLH characteristics and their relationship with pollution in coastal megacities remains insufficient. Moreover, existing studies rarely evaluate the consistency of various boundary layer solution methods, [...] Read more.
The PBLH affects the intensity of the surface turbulence and the state of pollutant dispersion. Current research on PBLH characteristics and their relationship with pollution in coastal megacities remains insufficient. Moreover, existing studies rarely evaluate the consistency of various boundary layer solution methods, making it difficult to identify deviations in single methods. So, we conducted enhanced observation experiments in Shenzhen, a megacity in China, between March and July 2023. The characteristics of the PBLH was analyzed by five months of observations from Micro-Pulse Lidar (MPL) and Microwave Radiometer (MWR). Five retrieval methods (Parcel, GRA, STD, WCT, and Theta) were applied for comparative assessment. The results shows that all methods captured similar diurnal patterns. During daytime, the PBLH ranged from 512 to 1345 m, with Theta yielding the highest and STD the lowest average values. At night, PBLH decreased overall, and method-dependent differences persisted. Under different pollution levels, this study also discussion the properties of PBLH using MPL and microwave radiometer. And aerosol optical depth (AOD) and PBLH showed a strong negative correlation, indicating aerosol-induced suppression of boundary layer growth. The study of boundary layer characteristics in coastal megacities can provide reference for atmospheric physics research in economically developed coastal areas. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Approaches for Multi-Scale Atmospheric Components Monitoring and Impact Assessment)
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25 pages, 10489 KB  
Article
An SSA-SARIMA-GSVR Hybrid Model Based on Singular Spectrum Analysis for O3-CPM Prediction
by Chaoli Tang, Wenlong Liu, Yuanyuan Wei and Yue Pan
Remote Sens. 2025, 17(23), 3826; https://doi.org/10.3390/rs17233826 - 26 Nov 2025
Viewed by 210
Abstract
Ozone density at cold-point mesopause (O3-CPM) can provide information on long-term atmospheric trends. Compared to ground-level ozone, O3-CPM is not only adversely affected by chemical substances emitted from human activities but is also regulated by solar radiation. Therefore, an accurate prediction of O3-CPM [...] Read more.
Ozone density at cold-point mesopause (O3-CPM) can provide information on long-term atmospheric trends. Compared to ground-level ozone, O3-CPM is not only adversely affected by chemical substances emitted from human activities but is also regulated by solar radiation. Therefore, an accurate prediction of O3-CPM is necessary. However, it is difficult for traditional forecasting methods to predict the main trends and seasonal characteristics of ozone time series while capturing the random components and noise of O3-CPM. In order to improve the prediction accuracy of O3-CPM, this paper proposes a hybrid SSA-SARIMA-GSVR model based on the Singular Spectrum Analysis (SSA) method, which combines the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) and the Gray Wolf Algorithm Optimized Support Vector Regression Algorithm (GSVR). First, the O3-CPM sequence is decomposed using SSA, and the concept of reconstruction threshold (RT) is introduced to categorize the decomposed singular values into two classes. The categorized RT reconstructed sequences containing periodic features and major trends are fed into the SARIMA model for prediction, and the N-RT reconstructed sequences (original sequence N minus RT reconstructed sequence) containing stochastic components and nonlinear features are fed into the GSVR model for prediction. The final prediction results are obtained by superimposing the outputs of these two models. The results confirm that, compared to various commonly used time series forecasting models such as Long Short-Term Memory (LSTM), Informer, SVR, SARIMA, GSVR, SSA-GSVR, and SSA-SARIMA models, the proposed SSA-SARIMA-GSVR hybrid prediction model has the lowest error evaluation metrics, enabling accurate and efficient prediction of the O3-CPM time series. Specifically, the proposed model achieved an RMSE of 0.26, MAE of 0.212, and R2 of 0.987 on the test set, outperforming the best baseline model (SARIMA) by 45.8%, 42.1%, and 3.1%, respectively. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Approaches for Multi-Scale Atmospheric Components Monitoring and Impact Assessment)
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20 pages, 7412 KB  
Article
Limitations of Polar-Orbiting Satellite Observations in Capturing the Diurnal Variability of Tropospheric NO2: A Case Study Using TROPOMI, GOME-2C, and Pandora Data
by Yichen Li, Chao Yu, Jing Fan, Meng Fan, Ying Zhang, Jinhua Tao and Liangfu Chen
Remote Sens. 2025, 17(16), 2846; https://doi.org/10.3390/rs17162846 - 15 Aug 2025
Viewed by 1010
Abstract
Nitrogen dioxide (NO2) plays a crucial role in environmental processes and public health. In recent years, NO2 pollution has been monitored using a combination of in situ measurements and satellite remote sensing, supported by the development of advanced retrieval algorithms. [...] Read more.
Nitrogen dioxide (NO2) plays a crucial role in environmental processes and public health. In recent years, NO2 pollution has been monitored using a combination of in situ measurements and satellite remote sensing, supported by the development of advanced retrieval algorithms. With advancements in satellite technology, large-scale NO2 monitoring is now feasible through instruments such as GOME-2C and TROPOMI. However, the fixed local overpass times of polar-orbiting satellites limit their ability to capture the complete diurnal cycle of NO2, introducing uncertainties in emission estimation and pollution trend analysis. In this study, we evaluated differences in NO2 observations between GOME-2C (morning overpass at ~09:30 LT) and TROPOMI (afternoon overpass at ~13:30 LT) across three representative regions—East Asia, Central Africa, and Europe—that exhibit distinct emission sources and atmospheric conditions. By comparing satellite-derived tropospheric NO2 column densities with ground-based measurements from the Pandora network, we analyzed spatial distribution patterns and seasonal variability in NO2 concentrations. Our results show that East Asia experiences the highest NO2 concentrations in densely populated urban and industrial areas. During winter, lower boundary layer heights and weakened photolysis processes lead to stronger accumulation of NO2 in the morning. In Central Africa, where biomass burning is the dominant emission source, afternoon fire activity is significantly higher, resulting in a substantial difference (1.01 × 1016 molecules/cm2) between GOME-2C and TROPOMI observations. Over Europe, NO2 pollution is primarily concentrated in Western Europe and along the Mediterranean coast, with seasonal peaks in winter. In high-latitude regions, weaker solar radiation limits the photochemical removal of NO2, causing concentrations to continue rising into the afternoon. These findings demonstrate that differences in polar-orbiting satellite overpass times can significantly affect the interpretation of daily NO2 variability, especially in regions with strong diurnal emissions or meteorological patterns. This study highlights the observational limitations of fixed-time satellites and offers an important reference for the future development of geostationary satellite missions, contributing to improved strategies for NO2 pollution monitoring and control. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Approaches for Multi-Scale Atmospheric Components Monitoring and Impact Assessment)
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21 pages, 10526 KB  
Article
Long-Term Spatiotemporal Variability and Source Attribution of Aerosols over Xinjiang, China
by Chenggang Li, Xiaolu Ling, Wenhao Liu, Zeyu Tang, Qianle Zhuang and Meiting Fang
Remote Sens. 2025, 17(13), 2207; https://doi.org/10.3390/rs17132207 - 26 Jun 2025
Cited by 1 | Viewed by 929
Abstract
Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis [...] Read more.
Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM2.5 and PM10 concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Approaches for Multi-Scale Atmospheric Components Monitoring and Impact Assessment)
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