Remote Sensing and GIS Technology in Atmospheric Research

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 237

Special Issue Editors

School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: spatiotemporal modeling; atmospheric dispersion; numerical simulation; remote sensing observation; risk assessment and prediction
Special Issues, Collections and Topics in MDPI journals
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: atmospheric dispersion and simulation; public safety; emergency response
Special Issues, Collections and Topics in MDPI journals
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: 3D meteorological/GIS modeling; remote sensing observation; risk assessment
Special Issues, Collections and Topics in MDPI journals
College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China
Interests: satellite remote sensing; spatiotemporal modeling; risk assessment and prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Earth’s atmosphere is a dynamic and complex system profoundly influenced by natural processes and human activities. To address pressing challenges such as climate change, air pollution, and extreme weather events, innovative approaches integrating remote sensing and Geographic Information Systems (GISs) have emerged as transformative tools in atmospheric research. Specifically, remote sensing technologies—spanning satellite, airborne, and ground-based platforms—provide unparalleled spatial and temporal resolution for monitoring atmospheric parameters, while GISs enable robust data integration, analysis, and visualization. Together, they empower researchers to decode intricate, atmospheric processes, forecast environmental risks, and support evidence-based decision making. To highlight the critical importance of these tools in addressing global challenges, bridging gaps between theoretical research and real-world applications, and fostering collaboration across geoscience, climatology, and environmental engineering, this Special Issue of Atmosphere, titled "Remote Sensing and GIS Technology in Atmospheric Research", seeks to showcase cutting-edge advancements and interdisciplinary applications of these technologies to advance our understanding of atmospheric dynamics, environmental sustainability, and climate resilience. We invite original research articles, reviews, and case studies that explore but are not limited to the following themes:

  • Atmospheric composition analysis (e.g., greenhouse gases, aerosols, and pollutants);
  • Climate change monitoring and modeling (e.g., temperature trends and carbon flux);
  • Extreme weather prediction and disaster risk reduction (e.g., hurricanes and droughts);
  • Urban climate studies (e.g., heat islands and air quality in megacities);
  • Integration of machine learning/AI with remote sensing and GIS;
  • Multi-source data fusion for atmospheric process analysis;
  • Policy-relevant applications for environmental governance.

By contributing to this Special Issue, researchers will help shape a global knowledge base that drives innovation in atmospheric science and informs strategies for mitigating environmental and societal impacts. Submissions should emphasize methodological rigor, technological innovation, and practical relevance. Submit your work to illuminate the synergies between remote sensing, GIS, and atmospheric research and to support a sustainable future for our planet.

Dr. Hui Liu
Dr. Mei Li
Dr. Mingyue Lu
Dr. Hua Shao
Guest Editors

Manuscript Submission Information

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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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • remote sensing
  • geographic information systems (GISs)
  • atmospheric research
  • climate change
  • extreme weather events
  • environmental modeling

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Published Papers (1 paper)

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Research

23 pages, 5531 KiB  
Article
An Efficient Deep Learning Method for Typhoon Track Prediction Based on Spatiotemporal Similarity Feature Mining
by Kaiwen Lixia, Mingyue Lu, Yifei Lu, Hui Liu and Ping Li
Atmosphere 2025, 16(5), 565; https://doi.org/10.3390/atmos16050565 - 9 May 2025
Viewed by 171
Abstract
Typhoon is one of the most destructive natural disasters, and it affects human society significantly. To reduce the negative impacts, many deep learning models for predicting future typhoon tracks have appeared. However, most of these models use all of the data they obtain [...] Read more.
Typhoon is one of the most destructive natural disasters, and it affects human society significantly. To reduce the negative impacts, many deep learning models for predicting future typhoon tracks have appeared. However, most of these models use all of the data they obtain as input, which may cause the diversity of typhoon tracks to have a negative impact on the prediction outcomes. In this paper, a joint method is proposed. The method mainly includes two parts: First, use a spatiotemporal similarity feature mining model to find out paths that are similar to the ongoing typhoon. Second, a deep learning model for processing sequence data is trained by these similar paths and then used for predicting the future track points’ latitude and longitude. The joint method bridges the gap in deep learning models’ ability to process spatial information and the shortcomings of spatiotemporal similarity feature mining models in predicting future data. In the experiment, we use a spatiotemporal similarity feature mining model to generate different input datasets by changing the number of similar paths in it, which can compare the model’s accuracy in different inputs. Also, real typhoon data recorded in the North West Pacific Ocean are used in the experiment. Through a comparison between the real path and prediction results in longitude and latitude, we find that 100–250 similar typhoon tracks as input have the best prediction effect in different tasks and are more accurate in long-term prediction. Full article
(This article belongs to the Special Issue Remote Sensing and GIS Technology in Atmospheric Research)
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