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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 703

Special Issue Editors


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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

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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 (1 paper)

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Research

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 533
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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