An Improved Method for Ionospheric TEC Estimation Using the Spaceborne GNSS-R Observations

Ren, Xiaodong; Liu, Huan; Zhang, Jincheng; Mei, Dengkui; Ban, Wei

doi:10.1109/tgrs.2022.3192983

Cited by 4 publications

(4 citation statements)

References 56 publications

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“…It found that areas with more dense LEO observations typically have better model accuracy, whereas areas with fewer LEO‐based observations will not be significantly improved. Therefore, the ionospheric TEC measurements that are derived from the Jason‐2 data as well as space‐borne GNSS‐R (GNSS Reflectometry) observations (Ren et al., 2022) can also be incorporated to reduce the data gaps over the ocean areas for global ionospheric tomography. On the other hand, the reconstructed 3D IEDs were further validated with the COSMIC profiles.…”

Section: Discussionmentioning

confidence: 99%

Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

et al. 2023

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Global monitoring and exploration of ionospheric space weather are highly demanded for accurate and precise representations of electron density (F. S. Prol, Kodikara, et al., 2021). There have been well-established techniques for measuring ionospheric electron density (IED), exemplified by ionosondes, incoherent scatter radars, and in situ probes (Peng et al., 2021;Smirnov et al., 2021). However, such measurements are typically constrained to a single place (Norberg et al., 2018;Pryse, 2003). Computerized ionospheric tomography (CIT), first proposed by Austen et al. (1988), has developed into a new approach for imaging the IED and comprehending ionospheric plasma dynamics. Although the Navy Navigational Satellite System's polar-orbiting satellites at an altitude of approximately 1,100 km can produce two-dimensional ionospheric images via radio tomography, these satellites can only be monitored via a series of ground receivers aligned in longitude but separated in latitude (Fremouw et al., 1992;Leitinger et al., 1997;Pryse & Kersley, 1992). Following that, it was demonstrated how to reconstruct the three-dimensional ionospheric structures using high-orbit satellites, specifically the global navigation satellite

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Section: Discussionmentioning

confidence: 99%

Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

et al. 2023

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“…Therefore, the regional model has been the mainstream of research in recent years, and the proposal of the regional model is also the inevitable demand of future communication system applications (Wang, Yang, & Yan, 2021). In decades, researchers have gradually proposed specialized TEC regional prediction models in China (Xiong et al., 2021), Japan (Mallika et al., 2019), India (Sivakrishna et al., 2022), Korean Peninsula (Jeong et al., 2022), South Africa (Ssessanga et al., 2019), Antarctic region (Yao et al., 2021), low latitude region (Zewdie et al., 2021), and ocean (Ren et al., 2022). On the other hand, from the perspective of the methods used for modeling, the spatial reconstruction of TEC can be divided into mathematical and machine learning‐based methods.…”

Section: Introductionmentioning

confidence: 99%

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Wang,

Liu,

Shi

2023

Space Weather

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In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. The core theory of this method is as follows: (a) Any surface can be uniquely determined by its first and second fundamental quantities; (b) By direct difference approximation, differential equations are transformed into algebraic equations to solve Gauss equations faster. At the same time, taking parts of Europe as an example, the proposed HASR method is used to determine the correlation coefficients and the number of iterations of the model by using the relative root mean square error (RRMSE) as the evaluation criterion. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations, and the prediction RRMSE is 9.75%. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved by 8.5%. We hope this method can provide ideas for the spatial reconstruction of other ionospheric parameters and further promote the realization of complete and accurate space weather forecast.

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“…Furthermore, with the change in geographical location and solar activity, the ionosphere presents the inherent periodic variation of day, month, season, year, and complex synoptic and climatological changes [1]. In general, when observational radio waves transmitted by space geodetic techniques such as global navigation satellite system (GNSS) [2] and very long baseline interferometry (VLBI) [3] pass through the ionosphere, their phase velocity and group velocity due to the collision of free electrons in the ionosphere will produce a certain degree of interference, which is approximately proportional to the total electron content (TEC) along the signal propagation path [4]. The resulting delay effect can be up to tens of meters [5], thus mastering TEC is very crucial for these systems such as GNSS and VLBI, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to achieve higher accuracy prediction of specific regions, TEC prediction models focusing on local regions have gradually become a research hotspot in recent years. Recent achievements have finished in Japan [35], China [36], Korean Peninsula [37], African Region [38], Antarctic Region [39], the low latitude region [2], the sea region [31], etc. For example, a European TEC prediction model [40] based on thin-plate splines (TPS) interpolation was self-calibrated, making the model better accurate during geomagnetic storms.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map

Liu

Wang²,

Yang³

et al. 2022

Remote Sensing

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In order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to establish an empirical prediction model of TEC for parts of Europe. The model has three main characteristics: (1) The principal component analysis (PCA) is used to separate TEC’s temporal and spatial variation characteristics and to establish its corresponding map, (2) the solar activity parameters of the 12-month mean flux of the solar radio waves at 10.7 cm (F10.712) and the 12-month mean sunspot number (R12) are introduced into the temporal map as independent variables to reflect the temporal variation characteristics of TEC, and (3) The modified Kriging spatial interpolation method is used to achieve the spatial reconstruction of TEC. Finally, the regression learning method is used to determine the coefficients and harmonic numbers of the model by using the root mean square error (RMSE) and its relative value (RRMSE) as the evaluation standard. Specially, the modeling process is easy to understand, and the determined model parameters are interpretable. The statistical results show that the monthly mean values of TEC predicted by the proposed model in this paper are highly consistent with the observed values curve of TEC, and the RRMSE of the predicted results is 12.76%. Furthermore, comparing the proposed model with the IRI model, it can be found that the prediction accuracy of TEC by the proposed model is much higher than that of the IRI model either with CCIR or URSI coefficients, and the improvement is 38.63% and 35.79%, respectively.

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An Improved Method for Ionospheric TEC Estimation Using the Spaceborne GNSS-R Observations

Cited by 4 publications

References 56 publications

Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map

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