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.

Drained and cultivated grasslands on peat soils behave as a significant source of greenhouse gasses by oxidation. However, the lack of empirical estimates of carbon losses from peatlands with adequate spatial and temporal resolution has forced researchers to rely on process-based model approximations to make quantitative, regional- or national-scale estimations. Here we use satellite-based synthetic aperture radar interferometry to estimate the land motion per parcel with a daily resolution, discriminate a reversible and an irreversible component, and convert this to an upper bound of (Formula presented.) -equivalent emissions over the western part of the Netherlands. We find an upper bound of 21.5 (Formula presented.) -eq/ha/yr, corresponding to a total regional output of 2.3 (Formula presented.) -eq/yr, or approximately 1.3% of the entire greenhouse gas emissions of the Netherlands in 2019. The method also allows us to provide estimates for future emissions as well as evaluate the efficacy of installed subsidence mitigation measures.

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.

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 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.

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 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.

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.

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 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.

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.