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.

In order to constrain different drivers of subsidence in the Groningen gas field region, the integration of geomechanical simulations into a data assimilation procedure is crucial. Existing geomechanical models vary in complexity depending on their implementation of the available input data of the subsurface geometry and properties and reservoir pressure. High-complexity models are associated with many parameters to be estimated and tend to be computationally expensive, hindering their practical use in data assimilation. We develop a mechanical model that is optimised in terms of model complexity for the context of simulating surface deformation above the Groningen gas field. The reservoir discretisation and vertical elastic layering are simplified such that model details that are unlikely to be generating surface signals resolvable in geodetic data are eliminated. We demonstrate that the optimised model is ~100 times more numerically efficient than complete models. We also determine the sensitivity of subsidence to the lateral compaction resolution and the elastic layering of our efficient model, to constrain the model resolution in future data assimilation applications for Groningen.

In gebieden met een slappe veen- en/of kleigrond, die op grote schaal voorkomen in West- en Noord-Nederland, leiden verschillende bodemprocessen tot seizoensgebonden verticale bodembeweging en lange-termijn bodemdaling. Het bepalen hiervan is belangrijk om (1) in kaart te brengen waar, wanneer en in welke mate bodemdaling optreedt en daarmee risicogebieden te kunnen identificeren, (2) het proces en onderliggende sturende factoren beter te begrijpen, (3) deze kennis te gebruiken om effectieve maatregelen te ontwikkelen en toe te passen om bodemdaling en de negatieve effecten ervan te verminderen, en (4) bodemdaling beter te kunnen voorspellen. In deze Deltafact worden meetmethoden om ondiep veroorzaakte bodembeweging en -daling te bepalen toegelicht.

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.

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 Persistent Scatterers Interferometry (PSI) method enables displacement estimation with millimeter accuracy. However, the uncertain positioning of Point Scatterers (PS) makes it difficult to associate them with real objects in space and hampers the interpretation of the results. This article proposes a methodology to enhance the accuracy of PS positions. The methodology successfully establishes links between PS and real objects by associating them with the most likely candidate points extracted from Airborne Laser Scanning (ALS) point clouds. The selection process for suitable candidates is based on ALS analysis of return number, classification, and geometric features determined by neighborhood analysis. The linking process involves determining global transformation parameters for PSs using the Iterative Closest Point (ICP) algorithm. Then, the nearest neighbor search within the error ellipsoid of the PS positions is performed. Tests conducted demonstrated that this method allows for linking more than 80 % and 65 % of the PS derived from Sentinel-1 and TerraSAR-X mission data, respectively, in both ascending and descending geometries. To validate the obtained results, in addition to the quantitative assessment, a qualitative analysis is performed based on a developed 3D visualization module showing all stages of the proposed methodology.

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.

Interferometry SAR (InSAR) enables the estimation of displacements of (objects on) the Earth’s surface. To provide reliable estimates, both an independent stochastic and functional model are required. However, the intrinsic problem of InSAR is that both are unknown. Here, we propose an independent definition of the stochastic model, via an approximation scheme for 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. Detecting temporal partitions in the amplitude time series, we assign quality values to all phase observations within each partition. To reduce the impact of outliers, we introduce the normalized median absolute deviation (NMAD) of the vector of amplitudes to robustly estimate the variance of the phase observations. The method results in a scatterer-specific and time-variable stochastic model, which is independent of the phase observations itself and prior to parameter estimation. This differs from many conventional methods, where the quality is often determined a posteriori from the residuals between the model and the observations. This yields more realistic and reliable displacement estimates, as well as improved statements on the precision and reliability of the estimated parameters.

La plupart des méthodes exploitant les séries temporelles InSAR (interférométrie radar) présentent des caractéristiques similaires qui permettent de les classer en deux catégories de techniques. La première catégorie de techniques est basée sur des réflecteurs distribués (DS) pour la surveillance des déformations. Une façon courante de réduire la décorrélation des signaux consiste à sélectionner des interférogrammes avec des lignes de base spatiales et temporelles courtes (technique des small baselines subset, SBAS). La seconde catégorie est celle de l’approche PS (permanent/persistant scatterers), qui utilise des réflecteurs individuels dominant le signal à l’intérieur d’une cellule de résolution pour suivre la déformation dans le temps. Une technique avancée récente nous permet de combiner à la fois les PS et les DS pour surmonter la rareté des points identifiés pour l’estimation (technique PSDS combinant réflecteurs persistants et distribués). Ce chapitre décrit les deux principales familles de techniques InSAR de séries temporelles (SBAS et PSDS).

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.

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 spatio-temporal displacements, relative to a reference point and a reference epoch, here defined as the mother image. When dealing with time series, there are several options to treat the mother image in computing and plotting the temporal phase differences, producing distinctly different results, in terms of the estimated displacement parameters and their precision. Here we review the three approaches mostly encountered in literature, discuss the implications of the different approaches, and recommend the 'embracing mother' approach for standard InSAR analyses and visualizations.

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.

Deformation phenomena on Earth are inherently three dimensional. With SAR interferometry (InSAR), in many practical situations the maximum number of observations is two (ascending and descending), resulting in an infinite number of possible displacement estimates. Here we propose a practical solution to this underdeterminancy problem in the form of the strapdown approach. With the strapdown approach, it is possible to obtain “3D-global/2D-local” solutions, by using minimal and largely undisputed contextual information, on the expected driving mechanisms and/or spatial geometry. It is a generic method that defines a local reference system with transversal, longitudinal, and normal (TLN) axes, with displacement occurring in the transversal-normal plane only. Since the orientation of the local frame is based on the physics of the problem at hand, the strapdown approach gives physically more relevant estimates compared to conventional approaches. Moreover, using an a-priori uncertainty approximation on the orientation of the local frame it is possible to assess the precision of the final estimates. As a result, appropriate cartographic visualization using a vector map with confidence ellipses enables an improved interpretation of the results.

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.

Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning.

The repeat period of SAR data and its side-looking characteristics make InSAR time series analysis useful for water level monitoring applications. The standard approach determines corresponding scatterers by focusing the study area on the multipath radar reflections that include the water level. This paper introduces an alternative approach to identifying such signals using two metrics: cosine similarity and temporal differential coherence. The results show that temporal differential coherence can detect phase variations similar to water level by constantly returning high values even when there is an offset, while cosine similarity yields low scores. Within an urban environment, this approach finds point scatterers corresponding to water level changes in or near water, such as permanent floating objects, bridges, and buildings adjacent to water, where the highest differential coherence value was acquired from a permanent floating restaurant in open water.

In the view of climate change, understanding and managing effects on coastal areas and adjacent cities is essential. Permanent Laser Scanning (PLS) is a successful technique to not only observe notably sandy coasts incidentally or once every year, but (nearly) continuously over extended periods of time. The collected point cloud observations form a 4D point cloud data set representing the evolution of the coast provide the opportunity to assess change processes at high level of detail. For an exemplary location in Noordwijk, The Netherlands, three years of hourly point clouds were acquired on a 1 km long section of a typical Dutch urban sandy beach. Often, the so-called level of detection is used to assess point cloud differences from two epochs. To explicitly incorporate the temporal dimension of the height estimates from the point cloud data set, we revisit statistical testing theory. We apply multiple hypothesis testing on elevation time series in order to identify different coastal processes, like aeolian sand transport or bulldozer works. We then estimate the minimal detectable bias for different alternative hypotheses, to quantify the minimal elevation change that can be estimated from the PLS observations over a certain period of time. Additionally, we analyse potential error sources and influences on the elevation estimations and provide orders of magnitudes and possible ways to deal with them. Finally we conclude that elevation time series from a long term PLS data set are a suitable input to identify aeolian sand transport with the help of multiple hypothesis testing. In our example case, slopes of 0.032 m/day and sudden changes of 0.031 m can be identified with statistical power of 80% and with 95% significance in 24-h time series on the upper beach. In the intertidal area the presented method allows to classify daily elevation time series over one month according to the dominating model (sudden change or linear trend) in either eroding or accreting behaviour.

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.

The estimation of displacement vectors for (objects on) the Earth's surface using satellite InSAR requires geometric transformations of the observables based on orbital viewing geometries. Usually, there are insufficient viewing geometries available for full 3-D reconstruction, leading to nonunique solutions. Currently, there is no standardized approach to deal with this problem, resulting in products that are based on haphazard and/or oversimplified assumptions with biased estimates and reduced interpretability. Here, we show that a clear definition of - and subsequent adherence to - enabling conditions guarantees the validity and quality of displacement vector estimates leading to standardized interferometric products with improved interpretability. We introduce the concept of the null line as a key metric for InSAR geometry and bias estimation, assess its impact and orientation for all positions on Earth, and propose a novel reference system that is inherently unbiased. We evaluate current operational practice, leading to a taxonomy of frequently encountered misconceptions and to recommendations for InSAR product generation and interpretation. We also propose new subscript notation to uniquely distinguish different projection and decomposition products. Our propositions contribute to further standardization of InSAR product definition, improved map annotation, and robust interpretability.