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Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition)
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Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition)

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 5774

Special Issue Editors

Dr. Alexander Kokhanovsky

E-Mail Website
Guest Editor
German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Dmitry Efremenko

E-Mail Website
Guest Editor
Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Interests: radiative transfer; invariant imbedding; discrete ordinate method; synthetic iterations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Atmospheric remote sensing is an important branch of the modern remote sensing of our planet. The terrestrial atmosphere is composed of various atmospheric gases, particulate matter, water, ice, and mixed clouds. The monitoring of atmospheric composition using various techniques important for environmental studies, climate research, aerosol–trace gases-cloud interaction studies and hazard (smoke, dust storms, volcanic explosions, ozone holes, etc.) warnings.

This Special Issue is the second volume of the Issue “Feature Papers for Atmospheric Remote Sensing”. On the basis of the previous research results, this volume aims to present recent advances in atmospheric remote sensing derived from ground-based and spaceborne observations.

Papers related to the following topics are welcome:

  • Atmospheric radiative transfer;
  • Remote sensing of atmospheric aerosol;
  • Remote sensing of trace gases including CO2, O3, CH4, SO2, and NO2;
  • Remote sensing of terrestrial clouds;
  • Remote sensing of precipitation;
  • Precipitable water vapor retrieval techniques;
  • Study of aerosol–trace gases–clouds interactions;
  • Inversion theory;
  • Machine/Deep learning approach for atmospheric retrieval;
  • Validation of geophysical products.

Dr. Alexander Kokhanovsky
Dr. Dmitry Efremenko
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

  • terrestrial atmosphere
  • remote sensing
  • atmospheric aerosol
  • trace gases
  • clouds
  • precipitation

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

Published Papers (5 papers)

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Research

25 pages, 24943 KiB  
Article
Comparative Analysis and Optimal Selection of Calibration Functions in Pure Rotational Raman Lidar Technique
by Yinghong Yu, Siying Chen, Wangshu Tan, Rongzheng Cao, Yixuan Xie, He Chen, Pan Guo, Jie Yu, Rui Hu, Haokai Yang and Xin Li
Remote Sens. 2024, 16(19), 3690; https://doi.org/10.3390/rs16193690 - 3 Oct 2024
Viewed by 574
Abstract
The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte [...] Read more.
The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
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21 pages, 14549 KiB  
Article
Estimating Ground-Level NO2 Concentrations Using Machine Learning Exclusively with Remote Sensing and ERA5 Data: The Mexico City Case Study
by Jesus Rodrigo Cedeno Jimenez and Maria Antonia Brovelli
Remote Sens. 2024, 16(17), 3320; https://doi.org/10.3390/rs16173320 - 7 Sep 2024
Viewed by 839
Abstract
This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a [...] Read more.
This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model’s adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model’s performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
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20 pages, 20652 KiB  
Article
Three-Dimensional Structure and Transport Properties of Dust Aerosols in Central Asia—New Insights from CALIOP Observations, 2007–2022
by Jinglong Li, Qing He, Yonghui Wang, Xiaofei Ma, Xueqi Zhang and Yongkang Li
Remote Sens. 2024, 16(12), 2049; https://doi.org/10.3390/rs16122049 - 7 Jun 2024
Viewed by 862
Abstract
Central Asia (CA) is one of the major sources of global dust aerosols. They pose a serious threat to regional climate change and environmental health and also make a significant contribution to the global dust load. However, there is still a gap in [...] Read more.
Central Asia (CA) is one of the major sources of global dust aerosols. They pose a serious threat to regional climate change and environmental health and also make a significant contribution to the global dust load. However, there is still a gap in our understanding of dust transport in this region. Therefore, this study utilizes Cloud–Aerosol LiDAR with Orthogonal Polarization (CALIOP) data from 2007 to 2022 to depict the three-dimensional spatiotemporal distribution of dust aerosols over CA and to analyze their transport processes. In addition, the Tropospheric Monitoring Instrument (TROPOMI) was employed to assist in monitoring the movement of typical dust events, and the trajectory model was utilized to simulate the forward and backward trajectories of a dust incident. Additionally, a random forest (RF) model was employed to rank the contributions of various environmental factors. The findings demonstrate that high extinction values (0.6 km−1) are mostly concentrated within the Tarim Basin of Xinjiang, China, maintaining high values up to 2 km in altitude, with a noticeable decrease as the altitude increases. The frequency of dust occurrences is especially pronounced in the spring and summer seasons, with dust frequencies in the Tarim Basin and the Karakum and Kyzylkum deserts exceeding 80%, indicating significant seasonal and regional differences. The high values of dust optical depth (DOD) in CA are primarily concentrated in the summer, concurrent with the presence of a stable aerosol layer of dust in the atmosphere with a thickness of 0.62 km. Furthermore, dust from CA can traverse the Tianshan mountains via the westerlies, transporting it eastward. Additionally, skin temperature can mitigate regional air pollution. Our results contribute to a deeper understanding of the dynamic processes of dust in CA and provide scientific support for the development of regional climate regulation strategies. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
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Graphical abstract

11 pages, 3477 KiB  
Communication
Separation of Rapidly-Varying and Slowly-Varying Processes and Development of Diffraction Decomposition Order Method in Radiative Transfer
by Meng Zhang, Chenxu Gao, Bingqiang Sun and Yijun Zhang
Remote Sens. 2024, 16(7), 1300; https://doi.org/10.3390/rs16071300 - 7 Apr 2024
Viewed by 1075
Abstract
Single scattering in radiative transfer is separated into rapidly-varying and slowly-varying processes, where the rapidly-varying process (RVP) is mainly contributed by the diffraction effect. Accordingly, the diffraction decomposition order (DDO) method is developed to solve the vector radiative transfer equation (VRTE). Instead of [...] Read more.
Single scattering in radiative transfer is separated into rapidly-varying and slowly-varying processes, where the rapidly-varying process (RVP) is mainly contributed by the diffraction effect. Accordingly, the diffraction decomposition order (DDO) method is developed to solve the vector radiative transfer equation (VRTE). Instead of directly solving the original VRTE, we decompose it into a series of order equations, where the zeroth-order equation replaces the RVP with a δ-function while the high-order equations are the same as the zeroth-order one, except that the high decomposition orders of the RVP are used as driven sources. In this study, the DDO method is numerically realized using the successive order of the scattering method. The DDO is computationally efficient and accurate. More importantly, all physical processes in the VRTE are fully decomposed due to the order decomposition of the RVP and can be straightforwardly discussed. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
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9 pages, 3151 KiB  
Communication
High Precision Measurements of Resonance Frequency of Ozone Rotational Transition J = 61,5–60,6 in the Real Atmosphere
by Mikhail Yu. Kulikov, Alexander A. Krasil’nikov, Mikhail V. Belikovich, Vitaly G. Ryskin, Alexander A. Shvetsov, Natalya K. Skalyga, Lev M. Kukin and Alexander M. Feigin
Remote Sens. 2023, 15(9), 2259; https://doi.org/10.3390/rs15092259 - 25 Apr 2023
Cited by 2 | Viewed by 1181
Abstract
Ground-based passive measurements of downwelling atmospheric radiation at ~110.836 GHz allow extracting the spectra of ozone self-radiation (rotational transition J = 61,5–60,6) coming from the low stratosphere–mesosphere and retrieving vertical profiles of ozone concentration at these altitudes. There is [...] Read more.
Ground-based passive measurements of downwelling atmospheric radiation at ~110.836 GHz allow extracting the spectra of ozone self-radiation (rotational transition J = 61,5–60,6) coming from the low stratosphere–mesosphere and retrieving vertical profiles of ozone concentration at these altitudes. There is a notable (several hundred kHz) ambiguity in the determination of the resonance frequency of this important ozone line. We carried out long-term ground-based measurements of atmospheric microwave radiation in this range using upgraded apparatus with high technical accuracy and spectral resolution (~12 kHz). The obtained brightness temperature spectra allowed us to determine the frequency of this ozone line to be 110,835.909 ± 0.016 MHz. We verified that the Doppler frequency shift by horizontal wind as well as the variations of the tropospheric absorption had little effect on the obtained result. The found value was 131 ± 16 kHz less than that measured in the laboratory and differed from modern model calculations. At the same time, it was close to the results of early semiempirical calculations made more than 40 years ago. The applications where precise knowledge about the resonance frequency of this ozone line can be important were discussed in this paper. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
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