High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery

Accurate estimation of thermospheric density is critical for precise modeling of satellite drag forces in low Earth orbit (LEO). Improving this estimation is crucial to tasks such as state estimation, collision avoidance, and re-entry calculations. The largest source of uncertainty in determining thermospheric density is modeling the effects of space weather driven by solar activity. Current operational models rely on ground-based proxy indices which imperfectly correlate with the complexity of solar outputs. In this work, we directly incorporate NASA’s Solar Dynamics Observatory (SDO) extreme ultraviolet (EUV) spectral images into a neural thermospheric density model. We demonstrate that direct EUV imagery can replace proxies and enable predictions with much higher temporal resolution while significantly outperforming current operational models. Our method paves the way for assimilating direct EUV measurements into operational use for safer LEO satellite navigation.