Smoothness-constrained Dynamic Parameter Estimation for InSAR Time Series

Urban resilience and decision-making rely on continuous monitoring of key safety indicators. The increasing availability of interferometric SAR (InSAR) observations offers a valuable opportunity for near real-time stability monitoring, particularly in the built environment. However, traditional InSAR time series methods use batch processing to estimate static displacement parameters, limiting early anomaly detection, computational efficiency, and use of ongoing SAR data. These methods also assume motion behavior remains constant over time. Here we introduce a new method-DYNamic parAMeter estimation of InSAR scaTterer motion in near-real timE (DYNAMITE) that enables instantaneous parameter estimation by capturing dynamic behavior in InSAR time series. The method uses a state-vector prediction model updated with new observations via recursive least squares, eliminating the need to store past data. It imposes a smoothness constraint on displacement based on an exponentially correlated velocity model assuming an Ornstein-Uhlenbeck process and uses normalized median amplitude dispersion as a quality metric. Smoothness is controlled by specifying the instantaneous velocity's standard deviation and decorrelation time. Results show the recursive approach matches batch methods in quality while better capturing dynamic behavior, supporting near real-time monitoring.