Urban resilience and decision-making rely on continuous monitoring of key safety indicators. The increasing availability of interferometric synthetic aperture radar (InSAR) observations offers a valuable opportunity for near real-time stability monitoring, particularly in the built environment. Traditional InSAR time-series methods use batch processing of all available data at a particular moment in time to estimate static and global displacement parameters, describing the motion of the effective scatterer over the entire evaluated time period. This batch approach limits the agility of the method to adapt to a changing temporal behavior, early anomaly detection, computational efficiency, and the systematic inclusion of newly acquired SAR data. Here, we introduce a new method to capture the complex dynamic behavior of a scatterer by estimating its instantaneous state (IS) instead of using a time-invariant parametric description. The IS estimation and prediction model uses single new SAR acquisitions to provide time updates and measurement updates using a Kalman filter methodology. It imposes smoothness constraints on the displacement signal by modeling the velocity as an exponentially correlated, mean-reverting Ornstein-Uhlenbeck process, thereby enhancing the practicality of the method, and employs the normalized median amplitude dispersion as a proxy for phase quality. The results demonstrate that IS-InSAR matches the estimation quality of batch methods in non-dynamic circumstances while more effectively capturing dynamic behavior. Updating IS with single observations enables near real-time monitoring, and the explicit specification of smoothness parameters facilitates implicit phase unwrapping.

Interferometric Synthetic Aperture Radar (InSAR) has a wide range of applications, including the monitoring of solid-earth and cryospheric geophysical processes and the monitoring of the built environment. The use of InSAR for atmospheric applications is less thoroughly developed. To perform such analyses the atmospheric phase delay of the SAR signal between different overpasses is used, which needs to be disentangled from other phase constituents, such as displacements and topography, which requires stack processing of large data volumes. Typically, initial atmospheric delays are predicted using existing numerical weather prediction (NWP) models, but InSAR processing and NWP model delay estimation software are not well integrated. Here we present a pure Python-based software package that integrates the automatic downloading and processing of InSAR and NWP model data to create time-series of unwrapped InSAR interferograms and InSAR equivalent tropospheric delays from NWP models. By combining the geometry of the InSAR radar signals with different NWP model datasets the tropospheric delays can accurately be derived on a pixel by pixel basis.

Peat subsidence occurs when parts of the peat soil interact with air, usually due to water table lowering, then triggers peat consolidation, shrinkage, and oxidation, releasing substantial CO₂ emissions. Managing and mitigating these impacts requires a comprehensive understanding of the mechanisms and the spatio-temporal variations of the subsidence. Advanced space geodetic techniques, particularly InSAR, enable surface displacement monitoring. While time series InSAR analysis effectively estimates displacement, its precision, accuracy, and representativity are compromised by temporal decorrelation, noise, and dynamic soil movement, especially over pastures on peat soils. Moreover, loss-of-lock events caused by an irrecoverable loss of coherence disrupt the time series and introduce arbitrary unintelligible phase offsets. Strategies such as multilooking using contextual information have improved the reliability of the InSAR displacement estimates. However, more experience in the efficacy of InSAR-based surface dynamics assessments is required. This study estimates and analyzes surface motion in a regional peat area in Midden-Delfland, The Netherlands, using Sentinel-1 data and the SPAMS model. SPAMS incorporates precipitation and evapotranspiration information to estimate surface motion parameters, distinguishing between reversible and irreversible subsidence. The results reveal an average subsidence rate of −5.4±0.7 mm/year within the study area. Irreversible subsidence is strongly correlated with climatic conditions, with the most significant subsidence observed during a prolonged dry period in the summers of 2018 and 2022. Mitigating peatland subsidence includes preserving soil water content, especially during dry periods. Integrating InSAR and SPAMS provides a valuable tool for monitoring peat surface elevation, water management, and reducing peatland degradation.

The growing need for advanced research in the proactive management of civil engineering works has sparked increasing interest in the integration of geospatial technologies, information modeling, and virtual environments. In this context, satellite radar interferometry (InSAR) has proven to be a reliable tool for multi-Temporal surface displacement monitoring, which can then be used to infer patterns which can subsequently be analyzed to infer patterns of structural or ground deformation. However, the interpretability of InSAR processing is often hindered by two primary challenges: The spatial and directional relativity of the measurements, which are confined to the satellite's line-of-sight and are relative to a userdefined reference point; and the difficulty in precisely geolocating persistent scatterer (PS) points and semantically linking them to specific structural elements, a limitation that stems from undetermined geolocation precision and the lack of inherent contextual information in the InSAR data itself. This research introduces an integrated methodological approach combining high-resolution satellite InSAR observations, georeferenced Building Information Models (BIM) to enhance both spatial and semantic accuracy in PS analysis. A key element is the structured data integration between heterogeneous sources such as satellite observations, geospatial coordinates, and BIM geometry, enabled by the geodetic alignment of InSAR data with absolute terrestrial reference systems and the projection of PS within the BIM environment. This process enables more reliable association of PS with discrete construction elements. This semantic mapping, combined with three-dimensional representation, allows for a more comprehensive interpretation of detected displacements, supporting the identification of potential issues not directly linked to structural failures. The resultant BIM serves as the connection between infrastructure elements and the processed InSAR displacement estimates, thus improving the reliability of the analysis as well as promoting a push towards operational deployment for a Digital Twin system. The application in case studies demonstrates the potential of the dynamic and multimodal Digital Twin paradigm as an operational tool for decision support, predictive maintenance, and infrastructure resilience.

Subsidence is a complex problem, both in a technical sense and in governance. This is particularly the case in the Netherlands, which is a low-lying and densely populated country where various causes of subsidence interfere with each other. Coping with subsidence in the Netherlands started already woo years ago. This long history of subsidence, however, along with its slow manifestation, has resulted in a tendency to adaptation rather than mitigation. There is a growing awareness that this focus on adaptation is actually excluding alternative solutions. Potentially cheaper or more effective options may be unknown and not even considered. At the same time, Dutch society is becoming more aware of the severity of human-induced subsidence as it is one of the most prominent current geological hazards. What is needed therefore, is a sound knowledge base facilitating the exploration of solutions outside the traditional way of thinking. Here we present the different knowledge and governance issues at stake. We start with the description of the natural processes that cause subsidence, and the human-induced causes like groundwater management and exploitation of deep geological resources. Then we elaborate how subsidence can be estimated from measurements. We pay specific attention to the utilization of modern ensemble-based techniques to integrate multiple models and data. The objective is to avoid deterministic predictions and instead produce a range of subsidence forecasts with confidence intervals that are in agreement with observational data and their uncertainties. Finally, we describe how technical knowledge can be integrated in decision making by estimating the costs and benefits of different scenarios, thereby offering an array of options for decision makers. Subsidence will keep playing a role in shaping the future of the Netherlands. Human-induced subsidence will continue with new subsurface activities directed towards the energy transition. Incorporating the grim sea level rise predictions, the issue becomes even more serious. It is therefore of paramount importance to maintain and further develop the current knowledge position and to develop proactive mitigation activities.

We present the preliminary results of an InSAR analysis of peatland surface motion covering a large spatial and temporal extent. This work is the first large scale analysis of the Dutch Green Heart region, and is made possible using a novel distributed scatter (DS) InSAR processing method. This method is designed to handle breakages in the observed interferometric phase time series which occur due to temporal decorrelation, which we designate with the term loss-of-lock.

We introduce SARXarray and STMtools, two python libraries designed to support the exploitation of modern Interferometric Synthetic Aperture Radar (InSAR) data by enabling handling of larger-than memory datasets and the incorporation and fusion with relevant contextual information.The libraries are developed upon two innovative and well-established open-source Python libraries:Xarray [5] and Dask [6]. They are implemented as Xarray extensions. SARXarray is designed to manipulate and operate on larger-than-memory coregistered raster stacks such as Single-look Complex images (SLC) or interferograms, performing scatterer selection and producing STM objects. STMtools leverage the Space-Time Matrix (STM) concept [3,4] and provides functionalities to process STM and perform enrichment/data fusion with other data sources.Both libraries are built on the Xarray library, providing support for a wide range of data formats, and utilize Dask for parallel computation, making them scalable for distributed computation infrastructures. By enabling InSAR data analysis incorporating contextual information, the two libraries enhance the potential to uncover underlying mechanisms driving deformation phenomena.

Due to its sensitivity to water vapor, high resolution, and global availability, interferometric satellite radar (InSAR) has a large but unexploited potential for the improvement of regional NWP models. A relatively straightforward approach is to exploit the exact instantaneous character of the InSAR data in data assimilation to improve the timing of NWP model realizations. Here we show the potential impact of InSAR data on the NWP model timing and subsequently on improved model performance. By time-shifting the model to find the best match with the InSAR data we show that we can achieve a model error reduction (one-sigma) of up to 40% in cases where weather fronts are present, while other cases show more modest improvements. Most model performance gain due to time-shifts can therefore be achieved in cases where weather fronts are present over the study area. The model-timing errors related to the maximum model error reduction for these cases are in the order of (Formula presented.) 30 min.

The growing availability of SAR data offers a real-time deformation monitoring opportunity, but data utilization can be inefficient. Our study introduces a mathematical framework using recursive least-squares and the wrapped phase, allowing efficient updates when new data arrives. This method also incorporates prior knowledge about signal smoothness for non-linear displacement estimation. Compared to the batch solution, our recursive approach achieves parameter estimation without storing past measurements while respecting signal smoothness constraints.

InSAR enables the estimation of displacements of (objects on) the earth's surface. To provide reliable estimates, both a stochastic and mathematical model are required. However, the intrinsic problem of InSAR is that both are unknown. Here we derive the Variance-Covariance Matrix (VCM) for double differenced phase observations for an arc, i.e., the phase difference between two points relative to a reference epoch. Using the Normalized Amplitude Dispersion we subdivide the time series in multiple partitions. The method results in a more realistic stochastic model, and consequently more realistic and reliable displacement parameters. The stochastic model also allows to make statements on the precision and reliability of the estimated parameters.

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

We introduce the term loss-of-lock to describe a specific form of coherence loss that results in the breakage of a synthetic aperture radar interferometric (InSAR) time series. Loss-of-lock creates a specific pattern in the coherence matrix of a multilooked distributed scatterer (DS) by which it may be detected. Along with identification, we introduce a new DS processing methodology that is designed to mitigate the effects of loss-of-lock by introducing contextual data to assist in the time-series processing. This methodology is of particular relevance to regions that suffer from severe temporal decorrelation, such as northern peatlands. We apply our new method to two subsiding cultivated peatland regions in The Netherlands which previously proved impossible to monitor using DS InSAR techniques. Our results show a very good agreement with in situ validation data as well as spatial correlation between regions and the natural terrain.

The observed phase in time series of interferometric synthetic aperture radar (InSAR) products is a superposition of various components. Differential topography, line-of-sight displacements, and differential atmospheric delays are the main contributions and need to be disentangled to derive accurate digital elevation model (DEM), deformation, or atmospherical products from InSAR. However, isolating the atmospheric component has been proven difficult as it is spatiotemporally highly dynamic and a superposition of two atmospheric states. Here, we propose an approach to parameterize the stochastic properties of the single-epoch atmospheric delay field as a way to define the atmospheric signal. We found that the atmospheric signal of a time series of interferograms can be characterized by structure functions, which can be used to isolate the single-epoch structure functions. Due to the scaling properties of the atmospheric signal, it is then possible to construct a parametric function per SAR acquisition, using two isotropic and three anisotropic parameters. In particular, the isotropic parameters for the short-distance variation and long-distance variation in atmospheric delay can be used to characterize the atmospheric signal. For a test set of 151 Sentinel-1 acquisitions, this results in an atmospheric energy range of about 10 for short-distance scales and about 50 for long-distance scales. Our parameterization demonstrates that we can describe the spatiotemporal variability of InSAR atmospheric delays, which provides a measure for atmospheric noise for individual epochs in deformation time series based on distance and azimuth.

Numerical weather prediction (NWP) models are used to predict the weather based on current observations in combination with physical and mathematical models. Yet, they are limited by the spatial density and the accuracy of the available observations. Satellite radar interferometry (InSAR) is known to be extremely sensitive to the 3D atmospheric refractivity distribution, and has a high spatial resolution, providing information that can be used for assimilation in NWP models. However, due to the inherent superposition of two or more atmospheric states, only biased and temporally differenced signals can be retrieved, that can also be contaminated by deformation signals and decorrelation. Here we present a method to estimate single-epoch absolute atmospheric delays by combining InSAR time series with prior NWP model prediction time series, using a constrained least-squares estimation. We show that this leads to a solution that reliably extracts the single-epoch relative delays from InSAR data and uses prior NWP model data to find the absolute reference for these delays, while mitigating long-term deformation and decorrelation signal. This approach leads to repetitive delay updates with a spatial resolution of 500 m, that can be directly assimilated into numerical weather models.

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.