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@ARTICLE{2026JGRA..13134593Z,
       author = {{Zhou}, Hao and {Yang}, Jipeng and {Li}, Yaozong and {Li}, Xinshang and {Qing}, Tiantian and {Xia}, Mingyang and {Luo}, Zhicai},
        title = "{Orbital Decay Prediction in Low Earth Orbit: Integrating Along-Track Density Observations With Machine Learning}",
      journal = {Journal of Geophysical Research (Space Physics)},
     keywords = {GRACE and GRACE follow-on, accelerometer, thermospheric density, low earth orbit, machine learning, orbital decay},
         year = 2026,
        month = mar,
       volume = {131},
       number = {3},
          eid = {e2025JA034593},
        pages = {e2025JA034593},
     abstract = "{Fluctuations in thermospheric neutral density affect the operational
        stability and lifetime of low Earth orbit (LEO) satellites.
        Solar activity and geomagnetic disturbances induce substantial
        density variations in the thermosphere, thereby impacting
        critical satellite operations such as orbit control, attitude
        maneuvers, and collision avoidance. However, existing empirical
        models fail to accurately capture these localized thermospheric
        density oscillations. To date, effective methods for the high-
        precision prediction of LEO satellite orbital decay under
        varying geomagnetic conditions remain underdeveloped. This study
        proposes a machine learning-enhanced method for predicting
        orbital decay at specified altitudes within the LEO region by
        making use of Gravity Recovery and Climate Experiment Level-1B
        observations and integrating along-track high-precision
        thermospheric density, aerodynamic coefficients, and satellite
        mass parameters. During the 9─11 May 2024 storm event, along-
        track thermospheric density surged, resulting in a 48-hr semi-
        major-axis decay of approximately 168 m before stabilizing at
        around 83 m thereafter. For the 24 August 2005 interplanetary
        coronal mass ejection (ICME) case, the cumulative decay
        ({\ensuremath{-}}45.4 m) showed close alignment with the
        observed orbital data ({\ensuremath{-}}40.4 m). When
        independently tested across 113 ICME events, the random forest
        model accounted for 85\% of the variance in orbital decay,
        achieving a test R$^{2}$ of 0.749 during all geomagnetically
        periods in 2005. The results demonstrate that our proposed
        approach delivers significantly improved prediction accuracy of
        satellite orbital decay across varying geomagnetic conditions
        compared with empirical models. This work provides novel
        insights into thermospheric disturbance impacts on satellite
        orbits and offers essential theoretical support for LEO mission
        planning and orbital management.}",
          doi = {10.1029/2025JA034593},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026JGRA..13134593Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}