Spatiotemporal Dynamics and Anomaly Detection of Land Subsidence in Delta Areas: A Case Study from the Po River Delta Using InSAR Time Series

Deltas are particularly susceptible to extensive land subsidence and consequently face heightened flood risks. Interferometric synthetic aperture radar (InSAR) techniques allow for multi-decadal detection of land surface displacements with high accuracy, demonstrating their power in revealing dynamics of land subsidence in delta areas. However, achieving a comprehensive understanding of the spatial and temporal patterns of the measured land displacements in these areas and assessing their potential impacts remain a challenge due to the superposition of various drivers of land movement and the high compressibility of the deposits forming these landforms. In this study, taking the Po Delta (Italy) as a case study, we analyze the spatiotemporal trend, seasonality, and abrupt changes in InSAR time series from the European Ground Motion Service (EGMS), derived from Sentinel-1 images spanning 2018-2023. Particular attention has been given to the displacements affecting the extensive levee system that shields the territory from flooding. Temporally, a regression model is applied to the InSAR time series to track their trend and seasonality, while the Bayesian estimator of abrupt change, seasonality, and trend (BEAST) algorithm is used to detect their abrupt changes. Spatially, we propose a robust estimation method for modeling spatial distribution of land subsidence associated with Holocene sediment ages. Four models, i.e., power, logarithmic, hyperbolic, and exponential functions, have been tested. The results reveal that, in the temporal domain, the behavior of the time series can be classified into trend dominated, seasonal dominated, and irregular patterns. Seasonality mainly characterizes the deformation of bridges and transmission towers and shows either a positive or negative correlation with the land surface temperature. Abrupt changes in the time series can be effectively detected, thereby enhancing alerts of levee failures. In the spatial domain, anomalous subsidence, which typically results from recent anthropogenic activities, can be identified by comparing the observed and modeled subsidence. The proposed framework shows its potential in improving the organization and interpretation of InSAR time series and thus provides comprehensive spatiotemporal analysis and direct detection of anomalies, helping identify critical areas and plan appropriate mitigation measures.