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June 18, 2026Remote Sensing0 citationsOpen Access

Four-Dimensional Ionospheric Electron Density Modeling Using Deep Learning Approaches

Four-Dimensional Topside Electron Density Modeling Using Multi-Stage Deep Learning Approaches

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Authors

CHChangyong HéAHAndong HuHCHan Cai

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Overview

Randomized trial models ionospheric electron density, suggesting enhancements in positioning accuracy.

Key Points

  • The study aims to develop a four-dimensional topside electron density model to enhance GNSS positioning and understand upper-atmosphere dynamics.
  • Developed a model using global GNSS radio occultation data with an L2-regularized artificial neural network.
  • Constructed two sub-models to estimate NmF2 and hmF2 with unavailable direct measurements.
  • Evaluated the full model against COSMIC-1, GRACE, and ISR datasets.
  • Sub-models reduced relative errors by 4.5% for hmF2 and 11.0% for NmF2 compared to IRI-2016.
  • The full topside Ne model showed improvements of 35%, 36%, and 53% against IRI-2016, evaluated with COSMIC-1, GRACE, and ISR datasets, respectively.
  • The model accurately represented key ionospheric features, including EIA and MSNA, across different solar activity conditions.
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Cite This Study

Hé et al. (2026) studied this question.

synapsesocial.com/papers/6a338de8630953a74978ea04https://doi.org/10.3390/rs18122002
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