Special Issues

Applications of GNSS Remote Sensing

Submission Deadline: 30 September 2025 (closed) View: 829 Submit to Journal

Guest Editors

Dr. Kamil Maciuk

Email: maciuk@agh.edu.pl

Affiliation: Department of Integrated Geodesy and Cartography, AGH University of Krakow, Mickiewicza 30, 30349 Krakow, Poland

Homepage:

Research Interests: GNSS, Remote Sensing, GIS, Navigation, Positioning


Dr. Pawel Postek

Email: pawel.postek@up.lublin.pl

Affiliation: Faculty of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, 13 Akademicka Street, 20-950 Lublin, Poland

Homepage:

Research Interests: GNSS, spectral analysis, PPP, IDW, Power-law noise


Summary

Integration of Global Navigation Satellite Systems (GNSS) with remote sensing technologies has revolutionized the way we observe and interpret environmental and geophysical processes. GNSS remote sensing is rapidly evolving and plays a crucial role in monitoring atmospheric conditions, land deformation, hydrological cycles, disaster management, and many other applications. This research area deals with global challenges such as climate change, natural hazard mitigation, and sustainable development, providing innovative solutions through advanced geospatial technologies.


This Special Issue aims to bring together cutting-edge research and practical applications of GNSS remote sensing. This issue seeks to explore advancements in GNSS-based techniques, their integration with other remote sensing methods, and their real-world impact across various disciplines. The scope encompasses theoretical developments, technical innovations, and case studies that highlight the transformative potential of GNSS in addressing contemporary challenges.


Suggested themes for this issue include:

1) Weather prediction and climate change

2) GNSS reflectometry applications

3) Land deformation monitoring

4) Integration of GNSS with other remote sensing technologies

5) Disaster risk reduction and urban planning


Keywords

GNSS, GPS, Galileo, Beidou, GLONASS, Remote Sensing, GIS

Published Papers


  • Open Access

    ARTICLE

    Spatiotemporal Variability of Atmospheric Pollutants in Syria: A Multi-Year Assessment Using Sentinel-5P Data

    Almustafa Abd Elkader Ayek, Bilel Zerouali, Ankur Srivastava, Mohannad Ali Loho, Nadjem Bailek, Celso Augusto Guimarães Santos
    Revue Internationale de Géomatique, Vol.34, pp. 669-689, 2025, DOI:10.32604/rig.2025.067137
    (This article belongs to the Special Issue: Applications of GNSS Remote Sensing)
    Abstract This study investigates the spatial and temporal dynamics of key air pollutants—nitrogen dioxide (NO2), carbon monoxide (CO), methane (CH4), formaldehyde (HCHO), and the ultraviolet aerosol index (UVAI)—over the period 2019–2024. Utilizing high-resolution remote sensing data from the Sentinel-5 Precursor satellite and its TROPOspheric Monitoring Instrument (TROPOMI) processed via Google Earth Engine (GEE), pollutant concentrations were analyzed, with spatial visualizations produced using ArcGIS Pro. The results reveal that urban and industrial hotspots—notably in Damascus, Aleppo, Homs, and Hama—exhibit elevated NO2 and CO levels, strongly correlated with population density, traffic, and industrial emissions. Temporal trends indicate significant pollutant fluctuations More >

    Graphic Abstract

    Spatiotemporal Variability of Atmospheric Pollutants in Syria: A Multi-Year Assessment Using Sentinel-5P Data

  • Open Access

    ARTICLE

    Retrieval of Surface Soil Moisture Using Landsat 8 TIRS Data: A Case Study of Faisalabad

    Uzair Abbas, Zahid Maqbool, Muhammad Adnan Shahid, Muhammad Safdar, Saif Ullah Khan
    Revue Internationale de Géomatique, Vol.34, pp. 655-668, 2025, DOI:10.32604/rig.2025.064279
    (This article belongs to the Special Issue: Applications of GNSS Remote Sensing)
    Abstract This study was conducted to devise an integrated methodology for retrieval of surface soil moisture (SSM) using Landsat 8 TIRS data. For this purpose, Landsat 8 images of 15 May 2021 (pre-monsoon) and 20 November 2021 (post-monsoon) were processed for retrieval of soil moisture index (SMI) based on land surface temperature (LST). Moreover, field-based SM in the laboratory was also determined and correlated with satellite-based SMI. A moderate correlation between field-based SM and satellite-based SMI with R2 = 0.60 was obtained. Based on this relationship, SSM maps of Tehsil Faisalabad Saddar for the pre-and post-monsoon seasons… More >

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