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.

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 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.

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.

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.

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.

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.

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 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.

Landslides are a major geohazard in hilly and mountainous environments. In-situ inspection of downslope motion is costly, sometimes dangerous and, requires prior knowledge of the existence of a landslide. Remote sensing from space is a way to detect and characterize landslides systematically at large scale. Interferometric Synthetic Aperture Radar (InSAR) has shown to be a valuable resource of deformation information, but it requires expert knowledge and considerable computational efforts. Moreover, the successful application of InSAR for landslides requires a favorable acquisition geometry relative to the landslide deformation pattern. Consequently, there is a need for a widely applicable tool to assess the potential of InSAR at a particular location a priori. Here we present a novel, generic approach to assess the potential of InSAR-based deformation tracking, providing a standardised and automated method applicable on any slope. We define the detection potential as the sensitivity of InSAR to detect downslope displacement combined with the presence of coherently scattering surfaces. We show that deformation can be detected on at least 91% of the global landslide-prone slopes, and provide an open source Google Earth Engine tool for the quick assessment of the availability of potential coherent scatterers. This tool enables any person interested in applying InSAR to routinely assess the potential for monitoring landslide deformation in their region of interest.

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.

We present a novel InSAR processing scheme which combines point scatterer (PS) and distributed scatter (DS) approaches in a hybrid framework along with contextual information about the environment under study. Data such as land parcel divisions, precipitation and temperature are integrated into the processing pipeline in order to produce accurate deformation time series estimates of the Dutch peatlands. In addition to these steps, a segmented processing scheme is introduced to manage irreversible losses of coherence in the interferogram stack. Initial results show a promising agreement with in-situ ground truth measurements gathered by extensometer readings of shallow surface deformation.

This book provides an introduction, at academic level, into the field of surveying and mapping. The book has been compiled based on hand-outs and readers written for the third-year course Surveying and Mapping, in the bachelor program Civil Engineering at Delft University of Technology. This book covers a wide range of measurement techniques, from land surveying, GPS/GNSS and remote sensing to the associated data processing, the underlying coordinate reference systems, as well as the analysis and visualization of the acquired geospatial information.

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.

Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for the conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by the machine learning algorithms which can create categorical predictions about the relative ambiguity level based on the readily available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum gradient phase unwrapping techniques.

Peat areas in the Netherlands are expected to exhibit extremely dynamic vertical motion, including both reversible and irreversible components. Yet the exact behaviour as a function of time is unknown, and is spatially very variable. This results in a poorly known estimation of greenhouse gas emissions and impact to existing infrastructure, and consequently limited ability to design and deploy mitigating or adaptive measures. In situ measurements are inefficient to capture this dynamic spatio-temporal variability. To monitor wide areas, InSAR has the necessary coverage, resolution, and high temporal sampling, but is very sensitive to noise due to temporal decorrelation of vegetated areas, which in combination with the high temporal dynamics exacerbates the success rate of the phase ambiguity estimation. Moreover, motion measured with InSAR is inherently relative to a reference point which is typically not stable in time. Thus even if InSAR would yield sufficiently coherent results, interpretation is notoriously difficult. To address these problems, an Integrated Geodetic Reference Station (IGRS) was installed in a peat area. It includes SAR reflectors mechanically coupled to a GNSS antenna. Arcs from the IGRS to natural scatterers can therefore be connected to ETRS89. The resulting arcs are thus single-differences of elevation with respect to a reference epoch. In order to reduce noise, and subsequently increase the success rate of ambiguity resolution, we define Elementary Usage Polygons (EUPs), which are expected to move homogeneously. This is achieved by constraining each EUP to have only one subsurface type and one land usage, which allows for the definition of an expected functional model of the vertical movement. Using the expected functional model in combination with the GNSS measurements on the IGRS we can constrain the unwrapping on the arcs between the IGRS and nearby EUPs in order to improve the success rate of the unwrapping. By making use of elevation model data from 2020, the elevation of the reference epoch can be constrained. The single-difference arcs showing elevation change with respect to the reference epoch are transformed into elevations with respect to NAP, allowing for an absolute elevation time series from InSAR observations in the vegetated rapid-moving peat areas of the Netherlands.

In radar interferometry, the method of storage for coherent scatterers and their attributes directly influences the ability for interpretation. Especially with the complexity and ambiguity of InSAR observations, the need for a consistent and queryable spatial data-platform becomes relevant. Our proposed method uses the concept of a spacetime matrix to store the coherent scatterers, implemented by means of a spatial database. The stored InSAR data are partitioned in modules of the displacement time series, inherent scatterer and processing-related attributes, and their corresponding contextual attributes. Data and context-driven queries are facilitated by the use of spatial indices. The method is illustrated by a case study for building stability in the northwestern part of Rotterdam, the Netherlands. The contextual attributes used characterize building foundations in part of the city.

The estimation of Signal-to-Clutter Ratio (SCR) of a radar point target, such as a corner reflector, is an essential step for synthetic aperture radar (SAR) interferometry and positioning, as it influences the phase measurement variance as well as the absolute positioning precision. The standard method to estimate the SCR of a point target relies on the debatable assumption of spatial ergodicity, using the clutter of the surrounding as representative of the clutter at the point target. Here, we estimate the SCR of a corner reflector using a time series of SAR measurements, i.e.,\ assuming temporal ergodicity. This assumption is often more realistic, particularly in a complex environment, in the presence of other point scatterers, and for small-sized reflectors. Empirical results on a corner reflector network, using Sentinel-1 SAR measurements, show that the temporal method yields a less biased and more precise estimate of the average SCR. A second experiment shows that the InSAR phase variance as well as positioning precision, predicted using SCR estimated by the temporal estimation method, is closer to the truth.