A Review of Machine Learning‐Based Ionospheric Spatial and Temporal Modeling

AbstractThe ionosphere introduces disturbances and errors in radio signals for satellite communication and navigation, requiring precise modeling to mitigate these effects. However, accurate ionospheric modeling faces challenges due to the complex spatio‐temporal variations caused by intricate coupling with the lower atmosphere and Earth's magnetic field, as well as the influence of space weather events. As a powerful tool capable of uncovering nonlinear relationships between inputs and outputs, machine learning (ML) approaches have been increasingly applied to ionospheric modeling, showing great potential. In this review, we explore studies from the past decades on the application of various ML methods in both spatial and temporal ionospheric modeling. The ionospheric parameters involved include vertical total electron content (VTEC), F2‐layer critical frequency (foF2), and virtual height (hmF2). By synthesizing findings from various studies, we present a comprehensive overview of the accuracy achieved by different ML‐based ionospheric models. Additionally, we summarize commonly employed data sources, ML algorithms, and strategies for best practices, offering insights and guidance