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.

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 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.

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.

We present a methodology which enables InSAR observations of ground motion in regions of low coherence and periodic decorrelation events which makes use of spatial and temporal contextual data to increase the information available to the processing algorithms. While this study is focused on observations of cultivated peatlands, the approach is generic and can be applied to other types of regions where coherence loss may be a concern. Neighbouring regions are grouped together by their contextual attributes such that when one region decorrelates, other observations from similarly behaving regions can still be used to derive a kinematic displacement model, thereby spanning the incoherent gap.

Parametric models are widely utilized for interferometric synthetic aperture radar (InSAR) time series analysis under the assumption that the parameterization is invariant over time. Yet, the complexity of InSAR scatterers makes the hypothesis of this consistent and uniform behavior less likely. Here, we propose a method for non-parametric time series analysis for arcs between point scatterers based on basic splines (B-splines), which has the potential of fitting the time series adequately due to its high flexibility. We implement B-spline modelling for the kinematic behavior of arcs, and we propose to apply it in the complex domain and optimize the B-spline settings by means of amplitude data. We find that B-splines in the complex domain show great potential for estimating InSAR time series behavior, due to its insensitivity to errors in the integer ambiguity estimation, and that multiple model solutions can be derived from different B-spline parameter combinations. Our approach does not aim to provide a solution of the InSAR non-uniqueness problem, but it emphasizes the contribution of smoothness constraints to limit the solution space.

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 3D reconstruction, leading to non-unique 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.

We introduce the termloss-of-lockto describe a specific form of coherence loss which results in the breakage of an 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 which 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 which 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 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. Especially, the isotropic parameters for the short-distance 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 and about 50 for long-distance scales. Our parametrization 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.

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.

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.

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.

Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CR) for SAR interferometric (InSAR) deformation monitoring, datum connection, and geodetic data integration. Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven. Here we address these challenges using a multitude of short baseline experiments with four transponders and six corner reflectors deployed at test sites in the Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months. We find an average Radar Cross Section (RCS) of over 42 dBm2 within a range of up to 15 degrees of elevation misalignment, which is comparable to a triangular trihedral corner reflector with a leg length of 2.0 m. Its RCS shows temporal variations of 0.3–0.7 dBm2 (standard deviation) which is partially correlated with surface temperature changes. The precision of the InSAR phase double-differences over short baselines between a transponder and a stable reference corner reflectors is found to be 0.5–1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to ±3 mm, which should be modeled and corrected for in high precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2–2.1 m in slant-range, i.e., within the range resolution of the Sentinel-1 IW product, with a temporal variability of less than 20 cm. Comparing similar transponders from the same series, we observe distinct differences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.

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.

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.

To retrieve the full displacement vector from InSAR, three line-of-sight (LoS) observations from different viewing geometries are required. However, often, at most two LoS observations are available. Within the literature, we encounter different approaches to address for this problem, unfortunately often with either mathematical or semantic flaws. Their impact reaches from quantitative errors in the reported studies, mismatches in comparative studies with other geodetic techniques, a lack of trust in the technology by end-users, to plain confusion. We propose both a uniform nomenclature and an alternative approach to the standard 3D decomposition problem using the concept of a strap-down reference system.

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 Dutch peatlands are a notoriously difficult region to monitor using InSAR. Low temporal coherence and signal-to-clutter levels necessitate the extraction of collective behaviour by the suppression of noise and clutter. Conventional techniques used to accomplish this include multilooking and phase-linking. The t-distributed Stochastic Neighbour Embedding (t-SNE) algorithm is a dimensionality reduction technique that aids in the analysis of large datasets. In this paper, we present an initial investigation into the suitability of the t-SNE algorithm to take the idea of extracting collective behaviour further. Similarly-behaved patches of land are automatically grouped together by the algorithm which aids in the specification of a functional model for that group. Our initial results show that the algorithm is able to successfully identify and group together areas in a scene which display similar behaviour over time. We also find that groups which display the same behaviour may also contain the same kinds of processing errors (for example unwrapping errors or cycle slips) and that these can also be automatically detected by the algorithm. We present this result as the first building block in an approach to smart InSAR data analysis which can learn from the data it is processing.

Atmospheric delay induces spatial phase errors and decorrelation in synthetic aperture radar (SAR) interferometry, especially in extreme weather conditions. For SAR missions, the atmosphere is considered to be spatio-temporally frozen during the aperture integration time, which is correct for Low Earth Orbit (LEO) SAR systems. However, this assumption may be inappropriate for Geosynchronous Earth Orbit (GEO) SAR since it can deploy a much longer integration time. Here we simulate a sequence of refractivity distributions with a high spatio-temporal resolution to analyze the spatio-temporal variable troposphere. The impacts of both frozen flow shift and turbulent delay in the time series in-terferograms are obtained, showing that tropospheric delay varies rapidly and may lead to phase decorrelaton within a few minutes.

InSAR and GNSS not only provide an additional source of ground motion measurements with respect to the conventional techniques, with different characteristics, also the data volumes increased significantly. This poses challenges on the integration of the various data sets available. The Integrated Geodetic Processing (IGP) software provides a systematic framework for the integration of geodetic data, including testing and quality assessment. The concept of the software is described, as well as an illustrative example of the output.

The main objective of this paper is to develop and evaluate a pragmatic approach to obtain an InSAR stochastic model for reduced InSAR datasets. This goal is achieved by calculation of the stochastic parameters per InSAR stack, propagating the noise structure to reduced datasets. The propagation of full covariance matrices when using a reduced dataset in space and time is avoided, using the derived analytical functions. This way, a computationally efficient approximation of the exact covariance matrix is obtained for reduced datasets.

Satellite InSAR time series are used to estimate the displacements of radar scatterers. This estimation problem includes the estimation of integer phase ambiguities, which is an ill-posed problem. Consequently, InSAR displacement estimation cannot yield unique solutions and may therefore be significantly biased. Here we show that phase reduction, using a priori information and the remove-compute-restore (RCR) methodology, is a viable way to solve this problem, as it reduces the likelihood of ambiguity errors. We found that application of this methodology to pastures on peat soils leads to a significant improvement in the estimated displacements. We assert that InSAR displacement estimation should always include an explicit statement on the first-order approximations and included assumptions on expected signal smoothness. We anticipate that a more systematic inclusion of the RCR method in standard processing algorithms will lead to more reliable and repeatable results of InSAR analyses.

Satellite-based persistent scatterer satellite radar interferometry facilitates the monitoring of deformations of the earth's surface and objects on it. A challenge in data acquisition is the handling of large numbers of coherent radar scatterers. The behavior of each scatterer is time dependent and is influenced by changes in deformation and other phenomena. Built environments are especially challenging since scatterers may have different signal qualities and deformations may vary significantly among objects. Thus, the estimation of the actual deformation requires a functional model and a stochastic model, both of which are typically unknown per scatterer and observation. Here, we present an approach that models the deformation behavior for each individual scatterer. Our technique is applied in a postprocessing phase following the state-of-the-art interferometric processing of persistent scatterers. This addition significantly improves the interpretation of large data sets by separating the relevant phenomena classes more efficiently. It leverages more information than other methods from individual scatterers, which enhances the quality of the estimation and reduces residuals. Our evaluation shows that this technique can discriminate objects in terms of similar deformation characteristics that are independent of the specific spatial position and temporal complexity. Future applications analyzing large data sets collected by satellite radars will, therefore, drastically benefit from this new capability of extracting categorized types of time series behavior. This contribution will augment traditional spatial and temporal analysis and improve the quality of time-dependent deformation assessments.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is used for many applications in earth observation. Most MT-InSAR methods select scatterers with high coherence throughout the entire time series. However, as time series lengthen, inevitable changes in surface scattering lead to decorrelation, which systematically decreases the number of coherent scatterers. Here, we propose a novel method to detect and process temporary coherent scatterers (TCS) by subsequently analyzing the amplitude and the interferometric phase. Two hypothesis tests are developed for amplitude analysis in order to identify the moments of appearing and/or disappearing coherent scatterers. Based on the amplitude analysis, the parameters of interest are then estimated using the interferometric phase. An optimized adaptive temporal subset approach is proposed to improve the precision of the estimated parameters. If the scatterers are not evenly distributed over the area, a secondary (support) network is designed to improve the spatial point distribution. The main advantage of this method is the reliable extraction of a subset of time series without using any contextual information. Experimental results show that the TCSs significantly increase the number of observations for displacement monitoring and improve the change detection capability in urban construction areas.

Persistent scatterers (PSs) are coherent measurement points obtained from time series of satellite radar images, which are used to detect and estimate millimeter-scale displacements of the terrain or man-made structures. However, associating these measurement points with specific physical objects is not straightforward, which hampers the exploitation of the full potential of the data. We have investigated the potential for predicting the occurrence and location of PSs using generic 3-D city models and ray-tracing methods, and proposed a methodology to match PSs to the pointlike scatterers predicted using RaySAR, a ray-tracing synthetic aperture radar simulator. We also investigate the impact of the level of detail (LOD) of the city models. For our test area in Rotterdam, we find that 10% and 37% of the PSs detected in a stack of TerraSAR-X data can be matched with point scatterers identified by ray tracing using LOD1 and LOD2 models, respectively. In the LOD1 case, most matched scatterers are at street level while LOD2 allows the identification of many scatterers on the buildings. Over half of the identified scatterers easily correspond to identify double or triple-bounce scatterers. However, a significant fraction corresponds to higher bounce levels, with approximately 25% being fivefold-bounce scatterers.

Satellite radar interferometry (InSAR) has been used to monitor the structural health of line-infrastructure (e.g. railways, bridges, dams and dikes) in recent years. This enables the retrieval of millimeter-level changes in the line-infrastructure geometry on a bi-weekly basis. However, InSAR is an opportunistic method for which the location of the measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products vary from one case to another. Particularly, this is due to the orientation of the line-infrastructure relative to the satellite position, and its expected deformation magnitude and direction. Hence, the InSAR applicability and performance quality is not uniform. In operational situations, this tends to make asset managers skeptical about the potential of InSAR application on these assets. In this work, following [1] we develop new standard InSAR products for line-infrastructure monitoring, provide tools for predicting optimal multi-sensor SAR data combinations, and propose generic a priori performance assessment metrics for line-infrastructure. These products and metrics are tested on the Dutch railway line-infrastructure asset.

PS-InSAR time series yield large volumes of data points, observed during many epochs. While traditional processing algorithms use a single parameterization for the behavior of all points, in reality this behavior will differ significantly between points and over time. It is a challenge to find the optimal parameterization for this behavior, and to assess the quality of the measurements per point and per epoch. Here we propose a post-processing method to improve the model estimation of PS-InSAR phase time series. The method combines machine learning (ML) algorithms and hypothesis testing (HT) into the ML/HT method efficiently leading to significant improvements in data interpretation, parameterization, as well as the quality of the estimated parameters. Moreover we show that we can find structure in the data regardless of spatial location and temporal complexity. In contrast to conventional assumptions that nearby points behave in the same way, with unchanged characteristics over time, a method is developed that takes individual behavior into account. Demonstrating that we can move from spatial and temporal analysis tools to semantic-based analysis.

Well-identifiable reference benchmarks are important in SAR interferometry to enable linking between different measurement techniques, or to enable datum connection between the local InSAR datum and Terrestrial Reference Systems. As corner reflectors for C-band are rather large, weather sensitive, and difficult to maintain over time frames of several years, active electronic transponders are an alternative. However, low cost transponders have not been on the market until recently. Here we report results from field tests of a new type of transponder. We show that the phase precision is in the order of an equivalent displacement of 0.5 mm, and that the RCS of the transponder is equivalent to a trihedral corner reflector with a leg length of 1.03 m.

SAR as an active remote sensing technique is capable of providing insights into the physical features of agricultural vegetation. However, the noisy nature of SAR signals makes the direct conversion to effective productivity metrics challenging. This study sheds light on the effect of gaps present in a sugarcane field on L-band and C-band SAR signals and demonstrates the variability of this effect with changing spatial averaging windows, changing precipitation conditions and changing vegetation height.

The Aswan High Dam, Egypt, was built in the 1960s and is one of the biggest dams in the world. It stopped the seasonal flood of Nile river allowing the urban expansion of cities/villages and the full year cultivation, producing 10×109kWh of power annually. The dam is located in an area where several earthquakes (ML<;6) occurred from 1981 to 2007. In this paper, we want to identify any potential damage that could be caused to the dam, and assess its overall structural stability using Multi-Temporal InSAR (MT-InSAR). To reach this goal, we process Envisat data from descending orbits acquired between 2003 and 2010. Our initial estimates show relatively small rates (maximum around -3 mm/yr in the satellite Line-Of-Sight) of subsidence, whose implications must be further investigated. In addition, we perform a preliminary stress-strain analysis of the dam using FEL and FEM methods to assess if the detected movements correspond to the expected vertical behavior for such mega-structure.

Temporal decorrelation is one of the main error sources in satellite radar interferometry. As the range of physical mechanisms causing temporal decorrelation is wide, there is no single analytical method to model this effect. Recent studies report seasonally varying coherence behavior over pasture areas, which cannot be described by the current analytical models of temporal decorrelation. To acknowledge this periodicity, we introduce a new analytical model. Here, the hypothetical movements of elementary scatterers within resolution cells are modeled as a periodic stochastic process with non-stationary increments. The proposed model is a function of the temporal baseline and the date of the master image of each interferogram. The parameters of the proposed decorrelation model have been estimated and validated for a case study in the Netherlands.

Associating a radar scatterer to a physical object is crucial for the correct interpretation of interferometric synthetic aperture radar measurements. Yet, especially for medium-resolution imagery, this is notoriously difficult and dependent on the accurate 3-D positioning of the scatterers. Here, we investigate the 3-D positioning capabilities of ENVISAT medium-resolution data. We find that the data are perturbed by range-and-epoch-dependent timing errors and calibration offsets. Calibration offsets are estimated to be about 1.58 m in azimuth and 2.84 m in range and should be added to ASAR products to improve geometric calibration. The timing errors involve a bistatic offset, atmospheric path delay, solid earth tides, and local oscillator drift. This way, we achieve an unbiased positioning capability in 2-D, while in 3-D, a scatterer was located at a distance of 28 cm from the true location. 3-D precision is now expressed as an error ellipsoid in local coordinates. Using the Bhattacharyya metric, we associate radar scatterers to real-world objects. Interpreting deformation of individual infrastructure is shown to be feasible for this type of medium-resolution data.

This contribution proposes a new approach for the analysis and preparation of geodetic data for the use in geophysical modeling. The approach resolves the problem of non-uniformity in the datasets obtained by different measurement techniques. The approach is based on two main steps: uniformization of the data using a standardized data format, and the application of the CUPiDO conversion tool to construct double-difference observations. Both steps are described in detail. By using double-difference observations, the effect of different reference points and geodetic datums is eliminated, thereby making the outcomes of the CUPiDO tool well suited for an integrated inversion to estimate a model. The CUPiDO tool will be made publicly available.

Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.

In recent years, synthetic aperture radar interferometry has become a recognized geodetic tool for observing ground motion. For monitoring areas with low density of coherent targets, artificial corner reflectors (CRs) are usually introduced. The required size of a reflector depends on radar wavelength and resolution and on the required deformation accuracy. CRs have been traditionally used to provide a high signal-to-clutter ratio (SCR). However, large dimensions can make the reflector bulky, difficult to install and maintain. Furthermore, if a large number of reflectors are needed for long infrastructure, such as vegetation-covered dikes, the total price of the reflectors can become unaffordable. On the other hand, small reflectors have the advantage of easy installation and low cost. In this paper, we design and study the use of small reflectors with low SCR for ground motion monitoring. In addition, we propose a new closed-form expression to estimate the interferometric phase precision of resolution cells containing a (strong or weak) point target and a clutter. Through experiments, we demonstrate that the small reflectors can also deliver displacement estimates with an accuracy of a few millimeters. To achieve this, we apply a filtering method for reducing clutter noise.

InSAR time series analysis involves the processing of extremely large datasets to estimate the relative movements of points on Earth. The estimated movements may reveal geophysical processes, or strain in anthropogenic structures. In parametric estimation methods, it is important to chose the optimal mathematical functional model relating the satellite observations to the kinematic parameters of interest. A standard approach is to parameterize the kinematic behavior, in first order, as a linear function of time, but it is unlikely that all objects behave in this purely linear way. Ideally, the kinematic parameterization should be optimized for each individual measurement point in the area of interest. In this work, following [1] we introduce a method to select the optimal functional model, with a minimum but sufficient number of free parameters using a probabilistic method based on multiple hypotheses testing.

The paper debates a novel approach for land cover (LC) mapping based on the Hidden Markov Model. The proposed methodology is aimed to address both the urgent demand of off-line (or historic) LC information retrieval and of near-real time LC monitoring. The discrete-time model employs short steps of 16 days, that conveniently fits the Landsat revisit time while providing a continuous and temporally dense representation of the land cover dynamics. Two temporal pattern typologies were identified and modeled within the proposed Markov chain architecture: a seasonal and synchrounous behavior which can be associated to the observables of LC classes such as forest and grasses, and a highly asynchronous behaviour, which characterizes the crop observables. The first typology is addressed by introducing time-dependency in state output probabilities, whereas the latter is rendered through a sequence of (sub-class) states interlinked by means of a `left-right' based model. Such model inherently incorporates crop growth tracking functionalities as an added value. In this paper the methodology has been tailored to Sao Paulo state (Brazil) scenario, showing overall accuracy above 80% on the test sample. A particular emphasis is attributed to the identification of sugarcane plantations, that are indeed responsible for major land use changes.

InSAR deformation estimates form a `free network' referred to an arbitrary datum, e.g. by assuming a reference point in the image to be stable. Consequently, the estimates of any measurement point in the image are dependent of these postulations on reference point stability, and the estimates cannot be compared with datasets of other types of measurement (e.g. historical levelling data or sea-level changes). Yet, some applications require `absolute' InSAR estimates, i.e. expressed in a well-defined terrestrial reference frame (TRF). We achieve this using collocated InSAR and GNSS measurements, achieved by rigidly attaching phase-stable millimetre-precision compact active transponders to permanent GNSS antennas. The InSAR deformation estimates at these transponders are then estimated in a TRF using the GNSS measurements. Consequently, deformation estimates at all other scatterers are now also defined in the same TRF.

During the 2014–2015 sugarcane growth season in São Paulo, Brazil, a considerable dataset was acquired consisting of space-based remote sensing images from radar and optical sensors, together with intensive ground measurements. In this work, images from the Sentinel-1, Radarsat-2 and Landsat-8 satellites are used to test the effectiveness of satellite-based indicators in sugarcane growth monitoring. A two-fold hypothesis testing is applied, in order to find statistically significant emerging hot spots and cold spots, both in space and time. Especially the comparison of results from the radar and optical sensors gives an insight into the difference in capability of these sensors to detect spatial and temporal patterns and trends.

Monitoring the kinematic behavior of enormous amounts of points and objects anywhere on Earth is now feasible on a weekly basis using radar interferometry from Earth-orbiting satellites. An increasing number of satellite missions are capable of delivering data that can be used to monitor geophysical processes, mining and construction activities, public infrastructure, or even individual buildings. The parameters estimated from these data are used to better understand various natural hazards, improve public safety, or enhance asset management activities. Yet, the mathematical estimation of kinematic parameters from interferometric data is an ill-posed problem as there is no unique solution, and small changes in the data may lead to significantly different parameter estimates. This problem results in multiple possible outcomes given the same data, hampering public acceptance, particularly in critical conditions. Here, we propose a method to address this problem in a probabilistic way, which is based on multiple hypotheses testing. We demonstrate that it is possible to systematically evaluate competing kinematic models in order to find an optimal model and to assign likelihoods to the results. Using the B-method of testing, a numerically efficient implementation is achieved, which is able to evaluate hundreds of competing models per point. Our approach will not solve the nonuniqueness problem of interferometric synthetic aperture radar (InSAR), but it will allow users to critically evaluate (conflicting) results, avoid overinterpretation, and thereby consolidate InSAR as a geodetic technique.

Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

In recent years, new algorithms have been proposed to retrieve maximum available information in synthetic aperture radar (SAR) interferometric stacks with focus on distributed scatterers. The key step in these algorithms is to optimally estimate single-master (SM) wrapped phases for each pixel from all possible interferometric combinations, preserving useful information and filtering noise. In this paper, we propose a new method for SM-phase estimation based on the integer least squares principle. We model the SM-phase estimation problem in a linear form by introducing additional integer ambiguities and use a bootstrap estimator for joint estimation of SM-phases and the integer unknowns. In addition, a full error propagation scheme is introduced in order to evaluate the precision of the final SM-phase estimates. The main advantages of the proposed method are the flexibility to be applied on any (connected) subset of interferograms and the quality description via the provision of a full covariance matrix of the estimates. Results from both synthetic experiments and a case study over the Torfajökull volcano in Iceland demonstrate that the proposed method can efficiently filter noise from wrapped multibaseline interferometric stacks, resulting in doubling the number of detected coherent pixels with respect to conventional persistent scatterer interferometry.

In this paper, we propose a framework referred to as “geodetic synthetic aperture radar (SAR) tomography” that fuses the SAR imaging geodesy and tomographic SAR inversion (TomoSAR) approaches to obtain absolute 3-D positions of a large amount of natural scatterers. The methodology is applied on four very high resolution TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since all the TomoSAR estimates are relative to the same reference point object whose absolute 3-D positions are retrieved by means of stereo SAR, the point clouds reconstructed using data acquired from different viewing angles can be geodetically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3-D TomoSAR point clouds are compared with a digital surface model obtained by airborne LiDAR. It is demonstrated that an absolute positioning accuracy of around 20 cm and a meter-order relative positioning accuracy can be achieved by the proposed framework using TerraSAR-X data.

SAOCOM is an Argentinian L band system formed by two satellites (SAOCOM-1A and SAOCOM-1B). ESA is investigating the possible applications of a companion satellite (SAOCOM-CS) carrying a passive receiver working in concert with one of the SAOCOM-1 satellites. During the mission there will be cycles with a long along-track bistatic baseline, suitable for deformation monitoring. Together with the combination of ascending and descending orbits, this geometry will produce four measurements from different viewing geometries, enabling us to estimate the 3D motion vector. Here we investigate the sensitivity of such a configuration, and the opportunities for increasing the density of persistent scatterers.

The paper debates a novel approach for sugarcane identification and characterization based on multi-spectral and multi-temporal profile matching. A parametric model aimed at identifying sugarcane among pasture/grasses/shrubs, annual crops and forest is proposed. Differently from other supervised and unsupervised classification techniques, the discussed profile-based parametric model accounts for variability in growth date, that becomes valuable information to be extracted, rather than simply a nuisance parameter, and delivers an effective extrapolation of the cane vigor. The approach is then applied to Landsat 5 TM and ERS/ENVISAT SAR time-series over the Orindiuva area attaining preliminary promising although perfectible results.

The telecommunications sector has proposed to use the 5.350–5.470 GHz frequency band for ground-based communication services. The Sentinel-1, RadarSAT-2, and future RadarSAT constellation SAR satellites are operating in the same band. Here we assess the impact of these ground-based uncorrelated radio frequency interference (RFI) sources to radar interferometry-based ground motion analysis. Apart from a theoretical assessment, a Persistent Scatterer Interferometry analysis is performed based on a combination of real ENVISAT data and simulated RFI noise at different levels. The analysis shows that the RFI sources significantly reduce the applicability of radar interferometry, with a reduction of detected Persistent Scatterers with more than 50%. If the telecommunication sector would proceed with this plan, operational ground motion services based on Sentinel-1 data would no longer be possible.

The potential of differential SAR interferometry (D-InSAR) techniques for the study of the 3-D deformation phenomena has been extensively demonstrated. In particular, two different performance approaches (one analytical and one numerical solutions) have been implemented and analyzed. The primary objective is to investigate the capabilities of the Sentinel-1 follow-on SAR mission (named here HRWS) and its preferred acquisition modes for this particular application domain, which might help to the mission optimization.

Finding solutions for the mitigation of atmospheric phase delay patterns from differential synthetic aperture radar interferometry (d-InSAR) observations is currently one of the most active research topics in radar remote sensing. Recently, many studies have analyzed the performance of regional numerical weather prediction (NWP) models for this task; however, despite the significant efforts made to optimize model parameterizations, most of these studies have concluded that current regional NWPs are not able to robustly reproduce the atmospheric phase delay structures that affect SAR interferograms. Despite these previous findings, we have revisited the application of NWPs for atmospheric correction using a different analysis strategy. In contrast to earlier studies, which assessed the quality of NWP-derived phase screen data, we have studied NWPs from a statistical angle by analyzing whether they are able to provide realistic information about the statistical properties of atmospheric phase signals in d-InSAR data. We have determined that NWP forecasts can provide relevant statistical information about the atmospheric phase screen captured in d-InSAR data. Based on this, this study presents a new atmospheric phase filtering approach that is using statistical atmospheric information as a prior in order to optimize the choice of unknown filter parameters. The mathematical concept of the prior-driven filtering approach is outlined, and its implementation is explained. We have determined the performance of this new filter concept and have shown that it comes very close to a filter optimum.

Multitemporal interferometric synthetic aperture radar (InSAR) is increasingly being used for Earth observations. Inaccurate estimation of the covariance matrix is considered to be the most important source of error in such applications. Previous studies, namely, DeSpecKS and its variants, have demonstrated their advantages in improving the estimation accuracy for distributed targets by means of statistically homogeneous pixels (SHPs). However, these methods may be unreliable for small sample sizes and sensitive to data stacks showing large time spacing due to the variability of the temporal sample. Moreover, these methods are computationally intensive. In this paper, a new algorithm named fast SHP selection (FaSHPS) is proposed to solve both problems. FaSHPS explores the confidence interval for each pixel by invoking the central limit theorem and then selects SHPs using this interval. Based on identified SHPs, two estimators with respect to the despeckling and the bias mitigation of the sample coherence are proposed to refine the elements of the InSAR covariance matrix. A series of qualitative and quantitative evaluations are presented to demonstrate the effectiveness of our method.

Ground deformation can be monitored with subcentimetric precision from space, using interferometric synthetic aperture radar (InSAR). This technique can sometimes be limited by a low density of naturally occurring phase-coherent radar targets. Measurement densification may be achieved through improvements in processing algorithms and new satellites with better revisit times, but there can still exist areas where very few coherent targets are detected, e.g., in vegetated nonurbanized areas. For third-party end users of InSAR survey results, there is currently no systematic method to determine a priori whether these coherent targets have adequate spatial distribution to estimate the parameters of their interest. We propose such a method, along with a practical solution for measurement densification, i.e., deployment of coherent target devices such as corner reflectors or transponders. We propose a generic network design methodology that does the following: 1) determines whether the naturally occurring InSAR measurements are adequate; 2) finds the minimum number of additional devices (if required); and 3) finds their optimal ground locations. The method digests, as inputs, the expected locations and quality of existing coherent targets, the quality of the devices being deployed, and, if available, any prior knowledge of the deformation signal. At the core of the method is a comparison of different covariance matrices of the final parameters of interest with a criterion matrix (i.e., the desired idealized covariance matrix), using a predefined metric. The resulting network is optimized with respect to precision, reliability, and cost criteria. Simulated data sets and a subsidence case study in the Netherlands are used to demonstrate this method.

Temporal decorrelation is one of the main limitations of synthetic aperture radar (SAR) interferometry. For nonurban areas, its mechanism is very complex, as it is very dependent of vegetation types and their temporal dynamics, actual land use, soil types, and climatological circumstances. Yet, an a priori assessment and comprehension of the expected coherence levels of interferograms are required for designing new satellite missions (in terms of frequency, resolution, and repeat orbits), for choosing the optimal data sets for a specific application, and for feasibility studies for new interferometric applications. Although generic models for temporal decorrelation have been proposed, their parameters depend heavily on the land use in the area of interest. Here, we report the behavior of temporal decorrelation for a specific class of land use: pasture on drained peat soils. We use L-, C-, and X-band SAR observations from the Advanced Land Observation Satellite (ALOS), European Remote Sensing Satellite, Envisat, RADARSAT-2, and TerraSAR-X missions. We present a dedicated temporal decorrelation model using three parameters and demonstrate how coherent information can be retrieved as a function of frequency, repeat intervals, and coherence estimation window sizes. New satellites such as Sentinel-1 and ALOS-2, with shorter repeat intervals than their predecessors, would enhance the possibility to obtain a coherent signal over pasture.

Persistent scatterer (PS) interferometry and small baseline subset algorithms can be used to estimate time series of surface deformation with high precision. In areas with low coherence, and in the absence of sufficient PS, the estimation of reliable phase information can be cumbersome. Here, we report a successful approach for estimating deformation at pasture on drained peat soils using the integrated use of data from several satellite missions, a parametric deformation model with a generalized least squares method, and spatial averaging over statistically homogeneous pixels. The developed methodology is analyzed and applied on a test site in The Netherlands, where we report local subsidence rates and periodic signal over the peat and pasture areas.

Applying time-series InSAR to measure crustal deformation in vegetated non-urbanized areas often yields a low density of measurement points (persistent scatterers or PS). Algorithmic improvements and new sensors with better revisit times can improve measurement densities, but there still exist areas with heavy decorrelation from where almost no coherent information can be extracted. We propose a new scheme that determines the optimal density and locations of introduced in situ devices (e.g. passive corner reflectors or active transponders) for measuring deformation within the constraint of a desired optimality criterion. The scheme digests, as input, prior knowledge of the expected deformation signal, (probable) PS locations, PS quality and device measurement precision. We demonstrate this scheme through a simulated dataset and a ground subsidence case study in the Netherlands.

In this paper, we propose a framework referred to as “geodetic TomoSAR“ that fuses the SAR image geodesy and TomoSAR approaches to obtain absolute 3D positions of a large amount of natural scatterers. The methodology is applied on four Very High Resolution (VHR) TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since the TomoSAR estimates are referred to the identical reference point whose absolute 3D positions are retrieved by means of Stereo-SAR, the point clouds from ascending and descending orbits are automatically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3D TomoSAR point clouds are compared to a DSM obtained by airborne LiDAR.

Synthetic aperture radar (SAR) image coregistration is a key procedure before interferometric SAR (InSAR) time-series analysis can be started. However, many geophysical data sets suffer from severe decorrelation problems due to a variety of reasons, making precise coregistration a nontrivial task. Here, we present a new strategy that uses a pixel-offset series of detected subimage patches dominated by point-like targets (PTs) to improve SAR image coregistrations. First, all potentially coherent image pairs are coregistered in a conventional way. In this step, we propose a coregistration quality index for each image to rank its relative “significance” within the data set and to select a reference image for the SAR data set. Then, a pixel-offset series of detected PTs is made from amplitude maps to improve the geometrical mapping functions. Finally, all images are resampled depending on the pixel offsets calculated from the updated geometrical mapping functions. We used images from a rural region near the North Anatolian Fault in eastern Turkey to test the proposed method, and clear coregistration improvements were found based on amplitude stability. This enhanced the fact that the coregistration strategy should therefore lead to improved InSAR time-series analysis results.

Monitoring ground deformation using SAR interferometry (InSAR) sometimes requires the introduction of coherent radar targets, especially in vegetated nonurbanized areas. Passive devices such as corner reflectors were used in such areas in the past. However, they suffer from drawbacks related to their large size and weight, conspicuousness, and loss of reliability because of geometric variations as well as material and maintenance-related degradation over several years of deployment. The viability of smaller, lighter, and less conspicuous radar transponders as an alternative is demonstrated via two field experiments: validation tests in a controlled environment, and operational performance for monitoring landslides in a heavily vegetated area. Comparison of 113 transponder-InSAR observations with independent validation measurements such as leveling and the global positioning system yields an empirical precision range of 1.8-4.6 mm, after outlier removal, for double-difference (spatial and temporal) transponder phase measurements in the radar line of sight, for Envisat and ERS-2.

The main challenge in analyzing the results of persistent scatterer techniques is to associate each coherent radar reflection to a real-world object, referred to as target type classification. In recent years different methods to perform target type classification were studied. In this paper we propose a height-based target type classification method to discriminate radar reflections emanating from the ground and above-ground objects. Data acquired from multiple spaceborne satellites such as ERS, Envisat, and TerraSAR-X covering Amsterdam, the Netherlands spanning over two decades from 1992 to 2012 are processed. The target classification results are validated with highly precise elevation data obtained from an independent airborne laser altimetry technique. In this paper we demonstrate that our target type classification method is accurate and thereby the generated DEM of the ground is of nearly sub-metric accuracy in case of ERS and Envisat, and TerraSAR-X.

The geodetic quality of a low-cost commercial off-the-shelf InSAR transponder has been empirically assessed, both under controlled conditions and operationally for landslide monitoring. Comparison of 113 transponder-InSAR observations with independent validation measurements (levelling or GPS) yields a transponder precision range of 1.8-4.6 mm after outlier removal for double-difference (spatial and temporal) phase measurements in the satellite line of sight for Envisat and ERS-2, making it a compact and lightweight alternative to a corner reflector for C-band InSAR.

Algorithms have been proposed in the recent years in order to retrieve all information available in interferometric stacks of SAR acquisitions with focus on distributed scatterers. One of the key steps in these algorithms - called phase triangulation, phase linking or phase multi-linking - is to optimally estimate filtered wrapped interferometric phases from all possible interferometric combinations preserving useful information and filtering noise. The advantages of these methods compared to conventional approaches are that the algorithm can be applied before phase unwrapping, and that it considers all possible interferograms. In this contribution we propose a new algorithm for phase triangulation based on the integer least squares (ILS) method. We model the phase triangulation problem as a system of linear observation equations. After computing the full covariance matrix of interferometric phases using a Monte-Carlo method, we use ILS to estimate the unknowns. The advantages of our method are that it is capable of considering the mutual correlation between all interferograms, and additionally provides as a output the precision of the estimates. Simulation results show that the proposed method works effectively and can optimally filter noise from interferometric stacks before unwrapping.

Conventional PSI technology is aimed towards estimating displacement time series of persistently coherent scatterers (PS) from a given set of radar acquisitions. Whenever the data from a new acquisition become available, the estimators for the parameters of interest will be computed by re-adjustment of the system of equations. This strategy of batch processing after a new acquisition is not optimal to identify changes in the behavior of single scatterer. For monitoring the structural health of buildings and civil infrastructure, there is a need for fast identification of anomalous behavior of scatterers, including the likelihood estimations of such detection results. Here we propose a general framework for the detection of anomalous behavior of (parts of) buildings and civil infrastructure by generating a sequential update of conventional interferograms, in combination with the parallel processing of the data using time series (PSI) interferometry. By estimating and analyzing the phase change per arc from each wrapped interferogram, abnormal changes can be detected fast and reliably. Our approach is demonstrated on a near-collapse of a building in Heerlen, the Netherlands, using Radarsat-2 data.

Artificially introduced persistent scatterers (PS) are often desirable, and sometimes even crucial, when monitoring deformation using InSAR especially in non-urbanised areas. The use of active radar transponders as viable `artificial PS' is demonstrated via two field experiments: a validation test in a controlled calibration environment, and their operational use for monitoring landslides. In the latter case, the added value of having collocated InSAR-GNSS measurements is also presented.

High resolution numerical weather models have recently raised a great interest in the InSAR community for atmospheric phase screen (APS) mitigation. Following the re search carried out in [1], in this study we focus on investigating the sensitivity of WRF (Weather Research and Fore casting) predictions to the model parameter settings which may substantially affect the result of water vapor modeling and to different boundary conditions. We validate the model predictions using atmosphere-only interferograms as well as radiosonde records. Our result shows that the radiosonde records (on average) agree very well with the WRF predictions based on our new model settings. However, in terms of spatio-temporal delay variation, the new settings do not always lead to a better prediction and the correction of atmospheric delay is case dependent. Therefore, we conclude that WRF lacks the reliability to correct the realistic APS in interferograms.

A method to derive accurate spatially dense maps of 3-D terrain displacement velocity is presented. It is based on the merging of terrain displacement velocities estimated by time series of interferometric synthetic aperture radar (InSAR) data acquired along ascending and descending orbits and repeated GPS measurements. The method uses selected persistent scatterers (PSs) and GPS measurements of the horizontal velocity. An important step of the proposed method is the mitigation of the impact of atmospheric phase delay in InSAR data. It is shown that accurate vertical velocities at PS locations can be retrieved if smooth horizontal velocity variations can be assumed. Furthermore, the mitigation of atmospheric effects reduces the spatial dispersion of vertical velocity estimates resulting in a more spatially regular 3-D velocity map. The proposed methodology is applied to the case study of Azores islands characterized by important tectonic phenomena.

The analysis of radar time series with persistent scatterer techniques usually relies on temporal unwrapping, because phase behavior can be often described by simple models. However, one of the major limitations of temporal algorithms is that they do not take advantage of spatially correlated information. Here, we focus on two types of information that can be spatially estimated, namely, observation precision and the probability density function of the model parameters. We introduce them in phase unwrapping using Bayesian theory. We test the proposed method using simulated data. We also apply them to a small area in the southern Netherlands and compare with conventional temporal unwrapping methods.

Image alignment is a crucial step in synthetic aperture radar (SAR) interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than a few tenths of a resolution cell to avoid significant loss of phase coherence. In conventional interferometric precise coregistration methods for full-resolution SAR data, a 2-D polynomial of low degree is usually chosen as warp function, and the polynomial parameters are estimated through least squares fit from the shifts measured on image windows. In case of rough topography or long baselines, the polynomial approximation may become inaccurate, leading to local misregistrations. These effects increase with spatial resolution of the sensor. An improved elevation-assisted image-coregistration procedure can be adopted to provide better prediction of the offset vectors. This approach computes pixel by pixel the correspondence between master and slave acquisitions by using the orbital data and a reference digital elevation model (DEM). This paper aims to assess the performance of this procedure w.r.t. the “standard” one based on polynomial approximation. Analytical relationships and simulations are used to evaluate the improvement of the DEM-assisted procedure w.r.t. the polynomial approximation as well as the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different resolutions and baselines. The two approaches are then evaluated experimentally by processing high-resolution SAR data provided by the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO/SkyMed) and TerraSAR-X missions, acquired over mountainous areas in Italy and Tanzania, respectively. Residual-range pixel offsets and interferometric coherence are used as quality figure.

Here we present an efficient algorithm to analyze the deformation behavior of line infrastructure, such as water defense structures and railways, using radar interferometric time series. Due to the limited amount of pixels and the consistent reflection properties, a detailed analysis can be performed. By considering neighboring pixels, the influence of a large part of error sources is reduced. However, the strong correlation between pixels should be considered. The algorithm is applied to dikes in the Netherlands, showing global as well as local deformation effects.

Here we report recent applications that extend the range of feasibility of InSAR: imaging subsurface fluid flow, estimating flow properties such as permeability, and tracking the integrity of water defense structures.

Atmospheric delay is one of the major error sources in In-SAR, hindering the accurate monitoring of ground motion. Here we use the WRF (Weather Research and Forecasting) weather model to hindcast atmospheric delays at SAR acquisition times over both mountainous and flat regions. The performance of the model is evaluated by comparing it to interferograms formed using acquisitions with short temporal baselines (¿4 months). Our results show that for flat regions the model not only misestimates atmospheric delay in magnitude and location but also largely underestimates the (horizontal) spatial variation (turbulent mixing) of the delay. In mountainous areas it can model the height dependent (vertical stratification) part of total delay correctly in some cases but not always. By removing the height dependent part we find again that the model may underestimate the spatial variation of the delay. Therefore, we conclude that the WRF weather model is in general not reliable for the operational mitigation of atmospheric delay in interferograms.

Radar interferometry has proven to be a very suitable, low-cost and accurate tool to measure surface displacements. We investigate several data fusion or time-series analysis strategies which aim to mitigate C-band InSAR restrictions for volcano deformation monitoring applications. The focus is on active African volcanic areas. Firstly, data fusion of C-band ENVISAT/ASAR and L-band ALOS/PALSAR sensors helps the determination of a rifting event sequence that took place in summer 2007 in Lake Natron area. The second strategy investigated is a new Wavelet Based InSAR time series applied on ERS-2 data covering the Nyiragongo-Nyamulagira area. It allows new ground displacements identifications outside the local rift valley. Lastly, PALSAR Quad-Pol POLInSAR applicability is explored for La Palma Island.

InSAR data acquired from independent overlapping tracks can be exploited for a reliability assessment of the Persistent Scatterer InSAR (PS-InSAR) technique. This is obtained by means of the datum connection of multiple tracks, simultaneously evaluating the misclosures between multi-track PS-InSAR estimates. Due to a different viewing geometry, many of the detected PS will physically not be the same. However, their estimates may still refer to the same deformation signal. The existence of independent observations of the same deformation signal provides a powerful tool to increase the redundancy and evaluate the reliability. The datum connection can be subdivided in two steps. The first step consists of the conversion of PS locations to a common datum. Secondly, the PS-InSAR parameter estimates (velocities, displacements, heights) are connected. In stead of the conventional approach of separately geocoding each track, we propose the use of a common radar datum defined by the acquisition geometry of the 'master track'. Multi-track datum connection has been applied in the Groningen region, the Netherlands, which is affected by subsidence due to gas extraction with displacement rates up to 7 mm/year. The main reservoir is (partly) visible in 6 independent overlapping ERS tracks from 1992 (ascending and descending). Datum connection resulted in a consistent set of PS-InSAR deformation estimates. Additionally, the deformation signal was decomposed in horizontal and vertical movements, utilizing the different viewing geometries of the tracks.

Because the quality assessment of Persistent Scatterers (PS) is dependent on the deformation model chosen, PS may be falsely rejected due to model imperfections. To accept these PS, more advanced deformation models should be used. Two methods applying adaptive deformation models are proposed. The first is based on a sequential scheme of alternative hypothesis testing of extended deformation models within the integer least- squares framework. The second uses a iterative scheme of global deformation modeling based on previous PS results. Application of the techniques to a salt mining area in The Netherlands confirms the increase in the number of detected PS.

This paper presents the concept of Dynamic Persistent Scatterers Interferometry (PSI) processing, which enables the sequential estimation of parameters. The method is based on the Integer Least Squares (ILSQ) PSI concept and makes use of the estimation vector and corresponding variance-covariance matrix of the initial estimation epoch. In addition, the concept of multi-modal adaptive estimation and testing is applied. The algorithm systematically adds a new acquisition or set of acquisitions to an existing stack, updates the solution of the previous run, and analyzes whether the behavior of the (pre-) selected points fits the expected one.

SAR amplitude calibration is performed prior to the selection of potential Persistent Scatterers (PS) to avoid amplitude variations due to sensor characteristics and viewing geometry. As only the interferometric phases of a small percentage of the radar pixels in an image is used in the PS-InSAR analysis, it is investigated if this time and storage space consuming step can be omitted. We present an integrated method which does not perform amplitude calibration explicitly, but integrates it into the PS point selection procedure for validation purposes by evaluating the hypothesis that a point would have been selected if all images were calibrated beforehand. Its performance assessment is based on coherent phase behavior of the selected potential PS and indicates that empirical calibration validation is an alternative for calibrating full images based on physical sensor parameters.

PS-InSAR has proven to be an accurate and ef- ficient technique for the joint estimation of topographic and displacement signal from stacked interferometric combinations. In this contribution a new method for PS-Insert processing is introduced, which enables the recursive estimation of parameters of interest. The method is based on the ILSQ PS-InSAR concept and makes use of the estimation vector and corresponding variance-covariance matrix of the initial estimation epoch. The presented methodology systematically adds a new acquisition (or set of acquisitions) to the existing stack, updates the solution of the previous run, and analyzes whether the behaviour of the (pre-) selected points fits the expected one. This contribution focuses on a mathematical framework, rather then on specific applicational problems. Nevertheless, the performed numerical analysis on simulated data sets is analyzed and discussed, which shows that the preset aims of the recursive PS-InSAR estimation technique is achieved. I. INTRODUCTION Time series InSAR analysis using persistent scatterer (PS) techniques aims at the joint estimation of topographic and displacement signal from a number of interferometric com- binations, (1), (2). Since the estimates of both parameters are correlated and error signal due to, e.g., atmospheric signal can significantly affect the adjustment, an accurate estimation depends on the availability of a large data stack, i.e., more than 20-30 images. A smaller number of images usually results in problems like detecting the potential PS, reducing the atmospheric signal, separating topography and displacement, and phase ambiguity estimation. An additional problem for all current multi-image pro- cessing concepts is that the parameter estimation is usually performed in batches, i.e., by using all available acquisitions at once. Hence, in order to incorporate a newly available acquisition into the processing chain, and consequently update the estimates, the whole processing (at least the PS part) has to be performed again. Such an approach consequently leads to an increase of processing time, limits the application to the areas where only a sufficient number of images is available, and reduces the potential application of the method to a semi- real-time deformation monitoring. The two main processing concepts of PS-InSAR are the concept of the ambiguity function, (1), and Integer Least Squares (ILSQ) method, (2). The main drawback of the first one is that the propagation concept of observations to the unknown parameters is suboptimal. Moreover, the method strongly depends on the discretization of the solution space and it treats unknown ambiguities as deterministic parameters instead of stochastic ones. The ILSQ approach is based on the principles of Best Linear Unbiased Estimation (BLUE) - it is based on the minimization of the mean squared error and it is formulated as a constrained minimization problem on the integer nature of the unknowns, (6). By means of the ILSQ method, the quality description of estimated parameters is the one of the end products of the analysis, which can conse- quently be used to determine the significance and reliability of the estimated parameters. The ILSQ PS-InSAR processing framework sets the basis for a recursive data processing strategy, where new acquisi- tions can be easily added to an existing data stack, significantly reducing the computational requirements. This implies that the presented methodology systematically adds a new acquisition to the existing stack, updates the solution of the previous run, and analyzes whether the behaviour of the (pre-)selected points fits to the expected behaviour of parameters of interest. If not, an alternative hypothesis is tested against the prior solution, leading to the rejection of the point, adaptation of the model, or manual intervention. For the conditions on the practical application of recursive PS-InSAR processing, it can be referred to the block-diagonal structure of the variance-covariance matrix of the introduced recursive model (the estimates from the initialization run and phase observations of the additional acquisition are assumed to be uncorrelated). Secondly, the atmospheric and non-modelled displacement contributions to the interferometric phase have to be modelled and incorporated into the variance matrix by means of covariance functions, (4), (5) - in the presented study the covariance functions are not further elaborated on. Moreover, in numerical experiments, phase contributions are isolated by low-pass filtering in the spatial domain and high- pass filtering in the temporal domain. Furtheron, in order to correctly perform the initialization run (candidate selection and unwrapping), a sufficient number of images (15-20) is needed. In the following sections the concept of the recursive PS- InSAR is presented. Examples on simulated data are used

Quality description and evaluation of InSAR results is hampered by the fact that the model to derive parameters from the observations is usually underdetermined. Only using strong, often rather qualitative, assumptions it is possible to reach unique solutions. One of the most prominent assumptions is that phase ambiguity resolution can be treated as a deterministic problem. In this study, a model formulation is presented that captures the majority of the assumptions in a mathematical sense, allowing for adjustment, testing procedures and formal error propagation. The influence of stochastic ambiguity resolution to the probability distribution of the estimated parameters is shown.

In this study, we introduce a refined algorithm for the fine InSAR image coregistration which could be used in highly decorrelated scenes. The refinement is introduced at the point of selection of points necessary for the estimation of the offset vectors between master and slave image. A new approach for point selection based on the Harris corner detector algorithm is presented. The new point selection algorithm results with the clusters of point candidates for the offset vectors over a scene. Consequently, the number of points and their spatial distribution are improved, which results in a better global quality of the coregistration model

In the permanent scatterer technique of synthetic aperture radar interferometry, there is a need for an efficient and reliable nonlinear parameter inversion algorithm that includes estimation of the phase cycle ambiguities. Present techniques make use of a direct search of the solution space, treating the observations as deterministic and equally weighted, and which do not yield an exact solution. Moreover, they do not describe the quality of the estimated parameters. Here, we use the integer least squares estimator, which has the highest probability of correct integer estimation for problems with a multivariate normal distribution. With this estimator, the propagated variance-covariance matrix of the estimated parameters can be obtained. We have adapted the LAMBDA method, part of an integer least squares estimator developed for the ambiguity resolution of carrier phase observations in global positioning systems, to the problem of permanent scatterers. Key elements of the proposed method are the introduction of pseudo-observations to regularize the system of equations, decorrelation of the ambiguities for an efficient estimation, and the combination of a bootstrap estimator with an integer least squares search to obtain the final integer estimates. The performance of the proposed algorithm is demonstrated using simulated and real data.

For ten years, a long history of data was acquired by the SAR sensors on the satellite ERS-1 and ERS-2 offering a wide range of interferometric applications. In 2002, the more advanced satellite ENVISAT was launched. The SAR on board on ENVISAT (ASAR) can continue the success of the remote sensing mission of the ERS satellites and preserve or even increase the value of the archived ERS data. The subject of this study is to demonstrate the continuity of the interferometric measurements by the combination of the SAR scene of the different sensors to interferograms (cross interferometry).

Atmospheric signal in spaceborne radar interferograms can be used for both meteorological interpretation in atmospheric studies, as well as for subtracting it from interferograms intended for surface deformation or topography studies. We show that atmospheric signal can be conveniently described stochastically by a power-law behavior, where the absolute amount of energy in the signal, related to the weather situation, can be described using a /spl chi//sup 2/ probability density function, based on EUREF GPS data. We present a single master stacking as well as cascaded interferogram stacking as methods to derive the atmospheric phase screen from the data.

In Azerbaijan oil mud volcanoes form on the surface as expressions of the vertical migration of oil and gas as a result of gravitationa l loading of largely unconsolidated sediments in combination with structure control and stress regime. In general it is believed that mud volcano eruptions are triggered by earthquake activity since this can cause hydrate instability and explosive dissociation of the hydrocarbons can occur. Through typical geomorphologic mud volcano vents called gryphons and salses, mud volcanoes eject argillaceous material (breccia) and build up their topography. Optical satellite images (Advanced Spaceborne Thermal Emission and Reflection - ASTER) and ground truth data from 2000 to 2002, centred on onshore Azerbaijan, are analysed using Variable Multiple Endmember Spectral Mixture Analysis (VMESMA) in combination with Interferometric Synthetic Aperture Radar (InSAR) from six ERS-2 scenes from 1996 to 1999. ASTER and InSAR imagery are used to look for evidence of mineral alterations and precursory surface deformation related to mud volcanism. Preliminary field spectral data of 5 onshore mud volcano vents show typical mineral zonations present in the mud breccia fields. ASTER image analyses on Aktharma -Pashaly mud volcano shows Al-OH mineral zonation patterns over various mud volcano vents. Initial InSAR processing for Aktharma-Pashaly shows little correlation over this particular mud volcano, which makes it hard to assess data combinations of ASTER and InSAR. Fair correlation was found for Touragai, Great - and Lesser Kjanizadag mud volcanoes showing high to moderate correlation over a time period of 3 years.

Monitoring of landscapes or sea bottoms by means of laser altimetry or multibeam results in huge amount of data covering the same area in different epochs. Often stable benchmarks are not available in the area covered. We propose a geodetic/geostatistical method to analyze possible deformations in such area out of time series of data. The method is used for a deformation analysis of six consecutive years of laser data covering a dune section on the south-west coast of the island of Texel, the Netherlands.

Repeat-pass synthetic aperture radar interferometry is an important tool for measuring Earth surface topography and/or surface deformations. These observations, however, are highly affected by the atmosphere. Therefore, an accurate description of atmospheric distortions is very important to improve an accuracy of interferometric measurements. In this paper we discuss influence of hydrometeors on the microwave propagation. On examples of two interferograms we show that there is a strong increase in a propagation delay associated with rain. To validate this observation we have used weather radar measurements to estimate contribution of rain droplets on the propagation path. It is shown that in some cases, a rain induced propagation delay can be of several centimetres.

During ERS-1 and ERS-2 missions, the application of synthetic aperture radar interferometry (InSAR) become known as a very important method for topographic mapping and high accuracy surface displacement measurments. Further investigations, however, showed that expected accuracy couldn't be achieved. It appeared that radiowave propagation through the atmosphere causes significant distortion to the observed signal and obscures effects of topography and/or deformations. Therefore, it became clear that in order to achieve very accurate measurements of surface displacements additional knowledge of state of atmosphere during InSAR measurements is necessary. In this paper the possibility of using Medium Resolution Imaging Spectrometer (MERIS) in combination with Advanced SAR (ASAR), both are on board of ENVISAT, for obtaining atmosphere free interferograms is discussed.

The main limiting factors for deformation mea- surements using repeat-pass satellite radar interferometry are temporal decorrelation of the scattering characteristics of the earth and atmospheric delay phase contributions to the interfer- ometric phase. Ferretti et al.(1) showed that using a multitude of radar acquisitions over the same site—a time series approach— reflections can be identified with a stable phase behavior in time, thus allowing deformation behavior to be estimated. The model of observation equations consists of m phase observations for a specific pixel and n unknown parameters describing surface deformation, elevation, and trend. Atmospheric delay is an important error source in these observations, but since it is temporally uncorrelated (while spatially correlated) it can be approximated per pixel per interferometric combination. This approximation requires a heuristic decision on which part of the temporal behavior of the interferometric phase is due to unmodelled deformation (e.g., non-linear deformation if the model estimates only linear deformation), and which part is due to uncorrelated atmospheric signal. Second, the residues attributed to atmosphere for all selected points within a single interferogram are expected to show spatial correlation, following a specific power law behavior (Hanssen, 2001). This results in a second decision on dividing atmospheric contribution and phase noise. Although the assumptions on which these two decisions are based are reasonable and results of previous studies show estimations of deformation and topography which are very likely, there is no independent means of control for the approach followed. In this paper, we will investigate the atmospheric signal estimated from a stack of 70 radar images acquired over Berlin, Germany. The estimated signal will be statistically parameterized and physically compared with meteorological data such as visual, infrared, and water vapor images from meteorological satellites and synoptic data. We will draw conclusions on the likelihood of the assumptions underlying the isolation of atmospheric signal, resulting in an increased reliability of the estimated parameters.

Interpolation is required in interferometric synthetic aperture radar (SAR) processing for coregistration of complex signals. Straightforward system theoretical considerations provide objective figures of merit for interpolators, such as interferometric decorrelation and phase noise. Theoretical and simulation results are given for nearest neighbor, piecewise linear, four- and six-point cubic convolution, and truncated sinc kernels.