High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery
This work addresses the critical need for precise satellite drag modeling in low Earth orbit, which is essential for tasks like collision avoidance and re-entry calculations, representing a domain-specific improvement.
The researchers tackled the problem of inaccurate thermospheric density estimation for low Earth orbit satellites by incorporating NASA's Solar Dynamics Observatory EUV spectral images into a neural model, resulting in significantly increased performance and higher temporal resolution compared to current operational models that rely on ground-based proxy indices.
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 and geomagnetic activity. Current operational models rely on ground-based proxy indices which imperfectly correlate with the complexity of solar outputs and geomagnetic responses. In this work, we directly incorporate NASA's Solar Dynamics Observatory (SDO) extreme ultraviolet (EUV) spectral images into a neural thermospheric density model to determine whether the predictive performance of the model is increased by using space-based EUV imagery data instead of, or in addition to, the ground-based proxy indices. We demonstrate that EUV imagery can enable predictions with much higher temporal resolution and replace ground-based proxies while significantly increasing performance relative to current operational models. Our method paves the way for assimilating EUV image data into operational thermospheric density forecasting models for use in LEO satellite navigation processes.