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Remote Sensing in Atmospheric Measurements

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 1849

Special Issue Editors

Dr. Feinan Chen

E-Mail Website
Guest Editor
1. Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
2. Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230031, China
Interests: light propagation; optical detection and imaging; remote sensor calibration and validation; atmospheric environment monitoring
Prof. Dr. Belay Demoz

E-Mail Website
Guest Editor
1. Department of Physics, University of Maryland，Baltimore County, Baltimore, MD 21250, USA
2. Director, Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Interests: atmospheric dynamics; lidar; climate observations; atmospheric instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has revolutionized atmospheric science, providing critical insights into atmospheric composition, dynamics, and processes. This Special Issue of the Sensors journal, titled "Remote Sensing in Atmospheric Measurements", aims to showcase cutting-edge research and advancements in the use of remote sensing technologies for atmospheric studies.

The scope of this Special Issue includes, but is not limited to, the following:

  • Innovative remote sensing techniques and instruments for atmospheric observation;
  • Applications of satellite, airborne, and ground-based remote sensing in monitoring atmospheric pollutants;
  • Remote sensing of greenhouse gases and their implications for climate change;
  • Advances in retrieval algorithms and data assimilation methods for atmospheric measurements;
  • Case studies demonstrating the integration of remote sensing data with other observational and modeling approaches to enhance our understanding of atmospheric processes;
  • The application of atmosphere correction methods and technology.

We welcome original research articles, reviews, and case studies that highlight the latest developments and applications of remote sensing in atmospheric measurements. Through this Special Issue, we aim to foster interdisciplinary collaboration and share knowledge that can contribute to the advancement of atmospheric science and the development of innovative solutions for environmental monitoring and climate change mitigation.

We look forward to your valuable contributions to this exciting and impactful field of research.

Dr. Feinan Chen
Prof. Dr. Belay Demoz
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. Sensors 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 2600 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 measurement
  • atmospheric dynamics
  • lidar
  • remote sensing
  • optical detection and imaging

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Published Papers (2 papers)

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Research

25 pages, 4786 KiB  
Article
Air Pollution Measurement and Dispersion Simulation Using Remote and In Situ Monitoring Technologies in an Industrial Complex in Busan, South Korea
by Naghmeh Dehkhoda, Juhyeon Sim, Juseon Shin, Sohee Joo, Sung Hwan Cho, Jeong Hun Kim and Youngmin Noh
Sensors 2024, 24(23), 7836; https://doi.org/10.3390/s24237836 - 7 Dec 2024
Viewed by 840
Abstract
Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent [...] Read more.
Rapid industrialization and the influx of human resources have led to the establishment of industrial complexes near urban areas, exposing residents to various air pollutants. This has led to a decline in air quality, impacting neighboring residential areas adversely, which highlights the urgent need to monitor air pollution in these areas. Recent advancements in technology, such as Solar Occultation Flux (SOF) and Sky Differential Optical Absorption Spectroscopy (SkyDOAS) used as remote sensing techniques and mobile extraction Fourier Transform Infrared Spectrometry (MeFTIR) used as an in situ technique, now offer enhanced precision in estimating the pollutant emission flux and identifying primary sources. In a comprehensive study conducted in 2020 in the Sinpyeong Jangrim Industrial Complex in Busan City, South Korea, a mobile laboratory equipped with SOF, SkyDOAS, and MeFTIR technologies was employed to approximate the emission flux of total alkanes, sulfur dioxide (SO2), nitrogen dioxide (NO2), formaldehyde (HCHO), and methane (CH4). Using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) diffusion model, pollutant dispersion to residential areas was simulated. The highest average daily emission flux was observed for total alkanes, with values of 69.9 ± 71.6 kg/h and 84.1 ± 85.8 kg/h in zones S1 and S2 of the Sinpyeong Jangrim Industrial Complex, respectively. This is primarily due to the prevalence of metal manufacturing and mechanical equipment industries in the area. The HYSPLIT diffusion model confirmed elevated pollution levels in residential areas located southeast of the industrial complex, underscoring the influence of the dominant northwesterly wind direction and wind speed on pollutant dispersion. This highlights the urgent need for targeted interventions to address and mitigate air pollution in downwind residential areas. The total annual emission fluxes were estimated at 399,984 kg/yr and 398,944 kg/yr for zones S1 and S2, respectively. A comparison with the Pollutant Release and Transfer Registers (PRTRs) survey system revealed that the total annual emission fluxes in this study were approximately 24.3 and 4.9 times higher than those reported by PRTRs. This indicates a significant underestimation of the impact of small businesses on local air quality, which was not accounted for in the PRTR survey system. Full article
(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)
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21 pages, 1556 KiB  
Article
Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links
by Giovanni Scognamiglio, Andrea Rucci, Attilio Vaccaro, Elisa Adirosi, Fabiola Sapienza, Filippo Giannetti, Giacomo Bacci, Sabina Angeloni, Luca Baldini, Giacomo Roversi, Alberto Ortolani, Andrea Antonini and Samantha Melani
Sensors 2024, 24(21), 6944; https://doi.org/10.3390/s24216944 - 29 Oct 2024
Viewed by 762
Abstract
Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. [...] Read more.
Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations—including SNR measurements paired with disdrometer and rain-gauge data—we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth–satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions. Full article
(This article belongs to the Special Issue Remote Sensing in Atmospheric Measurements)
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