We present a data-driven State-Space Model (SSM) for deriving monthly surface elevation changes of the Greenland Ice Sheet from CryoSat-2 radar altimetry (2011–2025). The model combines a Gaussian Markov Random Field for spatial dependence with an autoregressive process for temporal evolution, allowing seasonal cycles and long-term trends to emerge directly from the data.</p> <p>The resulting 5 km gridded dataset captures both large-scale and local variations, showing widespread thinning along the margins and near-stable conditions in the interior. Validation against ICESat-2, Operation IceBridge, automatic weather stations, and laser-altimetry-based time series shows strong agreement and a 40–45% reduction in noise after smoothing.</p> <p>This flexible and mission-independent approach provides monthly, uncertainty-quantified elevation change records that enhance understanding of Greenland Ice Sheet dynamics and support long-term, multi-sensor monitoring of its contribution to sea-level rise.