Daily 4D landslide movements monitoring via InSAR: A fusion framework integrating physics-based and data-driven models

Continuous monitoring of deep-seated slow-moving landslides is a critical measure to mitigate risks posed by catastrophic events, especially under increasing extreme weather conditions. As a non-contact, high-resolution measurement technique, space-borne interferometric synthetic aperture radar (InSAR) has been widely applied in landslide monitoring. However, when used for continuous monitoring, challenges remain, including incomplete monitoring dimensions, slow update efficiency, and insufficient temporal resolution. To address these, this paper proposes a fusion framework of physics-based and data-driven models, built on a Kalman Filter, to rapidly obtain daily 4D (spatial 3D with time) landslide movements based on movement characteristics and dependence on hydrometeorological factors. The method's performance was evaluated using both synthetic and real datasets. On synthetic data, RMSEs in the east, north, and vertical directions were 9.6 mm, 3.8 mm, and 1.4 mm, respectively. For real data, the daily 4D movements were projected onto the Line-of-Sight (LOS) directions of Sentinel-1 Track 11 and ALOS2 PALSAR2 for comparison, achieving sub-centimeter Root Mean Square Errors (RMSEs). These results confirm the accuracy of the estimated movements and demonstrate enhanced update efficiency enabled by the Kalman Filter, which allows rapid assimilation of new data without reprocessing the full historical archive. Additionally, by incorporating geophysical and geodynamic methods, we leveraged daily 4D movements to derive various landslide parameters to analyze the kinematics of the Chuwangjing landslide during 2016–2024. The findings indicate that daily 4D movements not only enhance InSAR's performance in continuous landslide monitoring but also provide additional derivative products, deepening the understanding of deep-seated landslide kinematics.