Multi-Sensor Fusion for Land Subsidence Monitoring: Integrating MT-InSAR and GNSS with Kalman Filtering and Feature Importance to Northern Attica, Greece

Abstract

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region.