Radio Sounding and Tomography of Ionosphere based on GNSS

Barzegar, S.; Khoshsima, M.

doi:10.22061/jrsgr.2024.11166.1081

Document Type : Review Paper

Authors

S. Barzegar ¹
M. khoshsima ²

¹ Department of space science, Institute of Geophysics, University of Tehran, Tehran, Iran

² Iranian Space Research Center, Tehran, Iran

https://doi.org/10.22061/jrsgr.2024.11166.1081

Abstract

Background and Objectives: Radio sounding and tomography techniques play a crucial role in studying the structure and dynamics of the ionosphere. Specifically, tomography is an advanced method for creating three-dimensional models of electron density within the ionospheric layer. By utilizing observational data, such as GPS measurements, tomography generates accurate maps of electron distribution. Ionospheric tomography provides high-precision insights into temporal and spatial variations in electron density. This precision is essential for applications like satellite navigation, radio communications, and meteorological predictions. Researchers focus on the upper layers of Earth’s atmosphere, using specialized radars called ionosondes to obtain precise information about electron density and the structure of ionized layers. Tomography, an imaging technique, relies on radio wave propagation through the ionosphere. It produces two- or three-dimensional images of electron distribution within this layer. Widely used in weather forecasting, radio communications, and space studies, tomography significantly advances our understanding of ionospheric phenomena. Technological advancements, including satellite-based measurements, enable even more accurate analyses, ultimately enhancing global communication and aviation safety. In this paper, the existing method for how to obtain the electron density change of the ionosphere layer based on the total electron content (TEC) parameter by using the phase difference analysis created in the communication signal of the global navigation satellite system GNSS when passing through different layers of the ionosphere has been investigated and studied. For this purpose, communication signals from low-orbit and high-orbit satellites were studied, and the method of obtaining TEC from phase difference was explained for each. Then, we studied the existing methods and algorithms for converting TEC (Total Electron Content) data into tomographic images. At the end of this article, as an example, we implemented the radio tomography method to visualize plasma bubbles in the equatorial region and compared the results with images taken from optical instruments. It was shown that radio tomography can be used as an accurate method for visualizing the structure of plasma bubbles. At the end of this article, we compared the method studied here with methods such as all-sky imaging, incoherent scatter radars, etc., and discussed the advantages and disadvantages of these methods relative to each other.
Methods: In current research on ionospheric sounding and tomography, significant progress has been made using the Global Navigation Satellite System (GNSS). Recent studies indicate that GNSS can model the ionospheric structure in three dimensions with high precision. Electron distribution in the ionosphere is analyzed using radio data obtained from satellites at Low Earth Orbit (LO) and High Earth Orbit (HO). Collecting ionospheric information via GNSS is a complex and precise process that relies on advanced technology to measure and analyze various ionospheric parameters. These systems, which include Earth-orbiting satellites, transmit signals to ground-based receiver stations. These signals contain precise temporal and spatial information about the satellites, allowing accurate determination of receiver positions on Earth. The distribution of electron density in the ionospheric layer directly affects the propagation of GNSS radio waves, including their path, shape, and phase. Any disruption in the ionospheric layer significantly impacts satellite communications, precise navigation, and long-range communications. In fact, GNSS utilizes this capability to measure the Total Electron Content (TEC) of the ionosphere, a key indicator for understanding its state. This process occurs through signals transmitted from satellites to ground stations. As these signals pass through the ionosphere, they are influenced by electron density variations, which can be measured with high accuracy.
Findings: In this comprehensive study, current research on ionospheric radio tomography using Total Electron Content (TEC) measurements from GNSS has been conducted. The concept of TEC and its impact on the phase and shape of signals received from the examined satellites has been explored. The application and methodology of using Low Earth Orbit (LEO) and High Earth Orbit (HEO) satellite data to obtain detailed TEC information are described. The validation and accuracy assessment of satellite data in ionospheric radio tomography, which is crucial for the reliability of the final product and the production process, have been addressed. Finally, a technique for reconstructing tomographic images using TEC measurements via GNSS signals is reviewed. It has been demonstrated that this reconstruction technique works well for imaging plasma bubbles. Horizontal distributions obtained from Vertical TEC (VTEC) depletions are compared with images captured by optical instruments, yielding similar results. Even in regions where GNSS signals are weak, this method can yield good outcomes if the bubble structures are sufficiently large.
Conclusion: In summary, GNSS tomography represents a dynamic and evolving field with significant potential for improving accuracy and efficiency in weather predictions. As we continue our research and development efforts, we anticipate the emergence of new methods and technologies that can address existing challenges and enhance the quality and precision of tomographic models. These advancements hold promise for diverse applications of GNSS tomography, including meteorology, climate change studies, and disaster management.

Keywords

Main Subjects

Remote Sensing

COPYRIGHTS
© 2024 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)

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