The study tested interferometric point target analysis (IPTA) on Sentinel-1A terrain observation with progressive scans SAR (TOPSAR) images between January 2016 and December 2018 to analyze the spatio-temporal distribution of earthquake-induced active unstable slopes in the surrounding region of the epicenter of the 2005 Kashmir earthquake in northern Pakistan. The results reveal dozens of unstable slopes with an average deformation rate varying between -35 mm/year and 10 mm/year along the line of sight (LOS) direction. A large number of unstable slopes showed relatively faster movements, with a rate between -35 and -25 mm/year. The study also found that the temporal behaviors of downslope movement are correlated with local precipitation. Rainwater finds its pathways through joints and cracks of rocks, speeding up slope destabilization. The study emphasizes the need for continuous monitoring and investigation of earthquake-induced slope stability to accurately delineate and investigate the sliding surfaces of unstable slopes with multi-sensor and multi-orbit data for effective mitigation measures.

The three-runway system expansion project of the Hong Kong International Airport (HKIA) began with the land reclamation to the north of the original runway. Understanding its ground deformation is essential for subsequent civil construction and planning at the new land. Synthetic Aperture Radar Interferometry (InSAR) technique is firstly used to investigate the spatiotemporal characteristics of land subsidence after the completion of third runway pavement. Due to the consolidation of underlay materials, the third runway is subject to varying degrees of land subsidence, with the monitored maximum sinking rate to be ~100 mm/year during September 2021 to October 2023. We adopted the Independent Component Analysis (ICA) to separate the underlying sources in order to explore the spatiotemporal characteristics of deformation in the reclaimed land. The results show that there are three distinct deformation sources in the study area, including an exponential decay signal (an exponential decay consolidation process), a periodic signal (thermal effects correlated with buildings and bridges) and a linear signal (a continuous subsiding). Considering the different reclamation methods, the linear deformation component is mainly located in areas with prefabricated vertical drains (PVD), which is strongly associated with the overall subsidence pattern. On the other hand, the land reclaimed by Deep Cement Mixing (DCM) method tends to reach a stable state earlier than those reclaimed by the PVD method, demonstrating the effectiveness of the DCM in reinforcing the reclamations. These results benefit our understanding of the settlement process over the third runway of HKIA and provide reliable suggestions for follow-up reinforcement plans on specific locations if needed.

Multitemporal InSAR is a widely used geodetic technique for measuring ground deformation. However, assessing the accuracy of InSAR deformation results is challenging, especially when field measurements such as leveling are limited in coverage or unavailable. While many studies have attempted to calculate the uncertainty of deformation using a priori InSAR stochastic models to assess the deformation reliability, these models are often biased by various factors. In this letter, we propose a new method called the Sparse Parameter Model (SPM) for InSAR deformation retrieval and uncertainty assessment when instantaneous deformation is not the focus. The method estimates the sparser deformation time series and leverages redundant SAR observations for the deformation uncertainty assessment and decorrelation noise suppression. The proposed model is tested by both simulated and real Sentinel-1 datasets and the derived deformation was validated with GPS measurements in the real application. The results demonstrated that the overall uncertainty of InSAR deformation, as estimated by the SPM, is 5.4 mm, falling well within the expected range of uncertainty, which highlights the effectiveness of the SPM in retrieving InSAR deformation and assessing uncertainty.

Differential interferometric synthetic aperture radar (DInSAR) has been widely used as one of the most important technologies for determining coseismic deformation. However, DInSAR processing is often perturbed by errors including atmospheric effects and those in topographic models, SAR satellite orbits, and phase unwrapping operation. These nuisance components can degrade the accuracy of the measurements and, therefore, distort the inversion of fault slips especially for moderate earthquakes. We propose in this letter a multitemporal InSAR (MTInSAR) method aiming to accurately determine coseismic deformation. By jointly analyzing a set of preseismic SAR images and one postseismic image, the method allows effective separation of coseismic deformation from topographic and satellite orbital errors deformation based on the distinct spatio-temporal characteristics of the signals. Since the solution is achieved directly from wrapped differential phases, the retrieved deformation is also immune to phase unwrapping errors. The October 6, 2008 Mw 6.3 Dangxiong, China earthquake is studied with the proposed method as an example. The slip inverted, respectively, from the MTInSAR and DInSAR coseismic deformation measurements shows up to 34-cm differences, indicating that the topographic error and inaccurate removal of orbital errors can bias the fault slip inversion.

This research focuses on proposing an approach for delineation of slope units from topographic parameters of terrain slope, slope aspect and topographic position index (TPI) extracted from ALOS World 3D digital elevation model (DEM). The research was carried out through segmentation processes at four different levels with varying parameters values of scale, size and compactness on RGB composite (TPI, slope, aspect). D-matrix was used to find the optimal parametrization for accuracy measures and showed that the lowest value of$D$lies with the scale parameter as 50, size as 0.3 and compactness as 0.7. The segmentation results obtained with these parameter values were classified into two landform classes of flat terrain and slope units and compared with the reference watershed boundary delineated using soil and water assessment tool (SWAT). The result showed higher agreement in terms of accuracy with changing slope positions of ridge line, drainage line and valley that can be further used for enhanced landslide susceptibility maps.

Phase decorrelation, as one of the main error sources, limits the capability of interferometric synthetic aperture radar (InSAR) for deformation mapping over areas with low coherence. Although several methods have been realized to reduce decorrelation noise, for example, by phase linking and spatial and temporal filters, their performances deteriorate when coherence estimation bias exists. We present an arc-based approach that allows reconstructing unwrapped interval phase time-series based on iterative weighted least squares (WLS) in temporal and spatial domains. The main features of the method are that phase optimization and unwrapping can be jointly conducted by spatial and temporal iterative WLS and coherence matrix bias has negligible effects on the estimation. In addition, the linear formation makes the implementation suitable with small subset of interferograms, providing an efficient solution for future big SAR data. We demonstrate the effectiveness of the proposed method using simulated and real data with different decorrelation mechanisms and compare our approach with the state-of-art phase reconstruction methods. Substantial improvement can be achieved in terms of reduced root-mean-square error (RMSE) in the simulation data and increased density of coherent measurements in the real data.

In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China's newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products.

Tatun volcano group, the last active volcano in Taiwan, is located in northern Taipei Basin, only 15 km north of the Taipei City. It was previously thought to be a dead volcano, but recent studies show that the last magmatic eruption happened about 5000 to 6000 years ago. The geothermal and seismic activities over the Tatun volcanic area have been highly active in recent years. The geochemical analysis implies the potential existing of the magma chamber under the ground surface of northern Taiwan which has the possibility of re-eruption in the future. In this study, we use ALOS-1/PALSAR images to monitor the surface deformation at the Tatun volcanic area. Stratified atmospheric delay and orbit errors are well considered and corrected by an adaptive patch-based method. The derived displacement history provides a detailed map of surface change with large spatial extent, which is validated by GPS measurements. The results demonstrate the capability of InSAR technique to monitor surface deformation over the volcanic zones.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR), as one of the geodetic techniques, is widely used in geological disaster monitoring and engineering applications. However, modern satellites provide an endless array of SAR imagery for InSAR applications, which has prompted us in exploring more efficient and accurate MT-InSAR methods to monitor the ground stability continuously and early detect the ground hazards. We proposed in this paper a step-by-step estimator for continuous deformation monitoring, aiming to study the ground surface deformation that has occurred in the past few years and monitor the latest developments in the same location continuously. Associated with the existing results of MT-InSAR, when a new SAR image is available, the unwrapped phase vector is sequentially estimated with the ambiguity detection, and then the deformation parameters are updated timely. Through experimental verification over Hong Kong, the proposed estimator shown its capability to detect the abnormal changes and update the deformation sequentially without being affected by the complex deformation histories.

This letter presents two main contributions to the data quality assessment of China's new GaoFen-3 radar satellite. First, we observed a half-pixel misregistration between the horizontal (H) and vertical (V) transmitting channels in the azimuth direction. This was determined by investigating the corner reflector (CR) response of some stripmap products of GaoFen-3 quad-pol stripmap (QPSI) mode. Second, to check whether the azimuth misregistration exists in different beams of QPSI mode, we improved the RADARSAT-2 channel checking method as a tool and evaluated more than 300 stripmap scenes. This letter confirms that the half-pixel misregistration problem, which can cause about 10% decoherence in the co-pol and cross-pol channel correlation coefficients, is common in the GaoFen-3 stripmap products of QPSI mode. Furthermore, the improved method can be considered as an effective way to fix the misregistration problem.

Tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) measurements are mainly caused by spatial and temporal variation of pressure, temperature, and humidity between SAR acquisitions. These delays are described as one of the primary error sources in InSAR observations. Although independent atmospheric measurements have been used to correct TDs, their sparse spatial or temporal resolution requires interpolation, leading to uncertainties in the corrected interferograms. The performance of the conventional phase-based correction method is weakened by the presence of confounding signals (e.g., TDs, deformation, and topographic errors) and spatial variability of the troposphere. Here, we propose a method that can simultaneously estimate stratified TDs together with parameters of deformation and topographic error based on their distinct spatial-temporal correlation. Spatial variability of the relationship between TDs and topographic height is addressed through localized estimation in windows divided by quadtree according to height gradient. We demonstrate the performance of the proposed method with both simulated and real data sets. In addition, both advantages and disadvantages of this method are addressed.

Coherent point (including persistent and distributed scatterers) selection and phase ambiguity treatment (or parameter estimation) are the key tasks involved in multitemporal InSAR (MTInSAR) algorithms, which are usually conducted separately with empirical thresholds. It is not rare to see that due to the discrepancies on threshold setting, even for the same MTInSAR technique with the same data sets, it will raise different (sometimes quite notable) results and affect the applicability of InSAR techniques. We propose here an integrated MTInSAR estimator that combines the coherent point selection and phase vector unwrapping into a single step. Essentially, the estimator aims to recover the unwrapped phase vector at coherent points. Therefore, it could serve as an alternative solution of spatial-temporal phase unwrapping problem. In the estimator, wrapped phase at all pixels in short baseline interferograms are taken as observations. Starting from the phase differences at arcs of a fully connected network of pixels, based on the residual analysis and spatial closure of phase triangularity, the estimator can detect and delete the arcs having unacceptable phase noise and phase ambiguities. By integrating the phase differences at the remained arcs, the unwrapped phase at coherent points in consecutive acquisition intervals can be obtained. Impressively, the estimator is immune to the bias raised by improper deformation model. The performance of the proposed estimator is evaluated via semisynthetic and real data tests. Considering that the phase enhancement algorithms (e.g., phase-linking and Extended Minimum Cost Flow-Small BAseline Subset) that can reconstruct high-quality wrapped phases are gaining popularity, the proposed estimator can also be implemented as a postprocessing module of these algorithms for retrieval of unwrapped phase vectors at coherent points.

Removing the topographic component in the interferometric synthetic aperture radar (InSAR) phase is conventionally conducted using an external digital elevation model (DEM). However, with an increasing spatial resolution of SAR data, the external DEM is becoming less qualified for this purpose, resulting in notable phase residues and even decorrelation in differential interferograms. Although topographic residuals can be parameterized and estimated by multi-temporal InSAR (MTInSAR) techniques, its accuracy is limited by several factors. Instead of providing accurate height information, shortening the length of baselines is an alternative for DEM phase mitigation. We propose here an MTInSAR processing framework that can retrieve the deformation time series without the estimation of topographic residuals. Within the framework, we generate a set of pseudo interferograms with near-zero baselines by integer combination and take these pseudo interferograms as observations of MTInSAR model, where deformation becomes the only signal that needs to be parameterized. The deformation time series is then retrieved directly from wrapped phases by ridge estimation with an integer ambiguity detector. It is noted that although atmospheric artifacts might be magnified during the combination, their differential components at arcs constructed with neighboring points that are not significantly enlarged. The proposed method is particularly suitable for infrastructure deformation monitoring in urban areas where no accurate external DEM is available. It also has promising potential for retrieving deformation from SAR data stacks with short acquisition intervals since the combination can enlarge the signal of interests in pseudo-observations. Semisynthetic and real data tests indicate that the proposed method has satisfied performance on DEM error mitigation and deformation time series estimation.

Karachi is located on southernmost border of Pakistan along the Arabian Sea coast. Concerned institutions fear the occurrence of subsidence in the city, further contributing to the relative sea level rise. No direct measurement has been made so far about the subsidence rate and its contribution to city's submergence risk. Our study presents first and preliminary results of vertical ground deformation measurement over this area using an advanced Temporarily Coherent Point InSAR technique using Envisat/Asar and Sentinel-l A data. These datasets allowed us to study deformation in this area from 2004-2016, with some data gaps, thus providing a long -term analysis. Results show that various parts of the city are unstable and undergoing deformation of up to about 15 mm/yr. However, we could not find significant correlation between faults passing through the city and the deformation in its different parts. Future studies should focus on monitoring the deformation on a regular basis.

Stratified tropospheric delays (TDs) in differential interferometric synthetic aperture radar (InSAR) result from the temporal variation of vertical stratification in the lower part of the troposphere. Although an approximately model can be made by assuming a linear relationship between topography and delayed phase in the interferogram, the estimation is weakened by the spatial variability of troposphere and the interference from other confounding signals (e.g., deformation, topographic error and orbit error, etc.). In this contribution, a jointly tropospheric correction scheme is proposed to simultaneously estimate stratified tropospheric delays with deformation and topographic errors. Spatial variability of tropospheric properties is addressed through a localized estimation which is derived by quadtree segmentation according to height gradient. The performance of the proposed method is validated and compared with the conventional linear and weather-model-based methods using Sentinel-1 dataset.

The primary bottlenecks that hinder the widespread application of Differential Synthetic Aperture Radar Interferometry (DInSAR) technique for bridge monitoring are the difficulties in reverting three-dimensional (3D) deformation of these complex structures and achieving detailed structural risk assessment for end users. To address these challenges, we developed an improved time series InSAR analysis approach that can retrieve 3D deformation of bridges and enable a detailed structural risk assessment. The proposed strategy effectively integrates the observations from multiple SAR sensors to reveal the 3D deformation. Moreover, the selected point-like targets (PTs) are classified and linked to specific bridge features based on their temporal deformation models to make a detailed structural risk assessment. Our results demonstrate the effectiveness of integrating observations from multiple SAR sensors in studying bridge 3D deformation and investigating the structural risk.

The last two decades have witnessed a dramatic development of the satellite interferometric synthetic aperture radar (InSAR) technology in both the theoretical methods and operational platforms. The propagation errors of SAR signal in the ionosphere is one of the problematic issues for the low-frequency SAR systems, such as L-band and P-band, and can also seriously degrade the accuracy of InSAR. Recently, there has been renewed interest in the range split-spectrum method for the ionopheric correction after some critical issues are resolved. In this study, we used the ultra-fine observation mode (80 MHz) of the Advanced Land Observation Satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar-2 (PALSAR-2) data over the 2016 Kumamoto earthquake to evaluate the performance of an asymmetric split-spectrum method for the ionospheric correction. The presented works demonstrate the effectiveness of two types of the asymmetric split-spectrum method for mitigating the ionospheric artifact in InSAR, as compared with the conventional split-spectrum method. Additionally, this study provides practical insight into the ionospheric correction strategies of the scheduled NASA-ISRO synthetic aperture radar (NISAR) mission.

The periodic surface are often seen in agriculture, such as the planting fields of potato, sugarcane, and green onion. There are strong coherent scattering for the fields whose aligning direction is perpendicular to radar's light of sight (LOS). Enhanced backscattering coefficients induced by coherent scattering on synthetic aperture radar (SAR) images hinder the retrieval of soil moisture. The paper investigated the capability of different retrieval models in the inversion of soil moisture for bare periodic fields. The results show that the copolarized ratio and HV backscattering coefficient, which are less affected by the periodic structure, can be used to reduce the effect of coherent scattering on soil moisture inversion.

Interferometric synthetic aperture radar (InSAR) is one of the most popular geodetic techniques for studying earthquake-related crustal displacements. Satellite SAR signals interact with the ionosphere when they travel through it during the synthetic aperture time. The condition of the ionosphere and its variation can significantly affect spaceborne InSAR measurements. In this letter, we use the Advanced Land Observation Satellite Phase Array-Type L-band SAR data from the 2009 southern Sumatra earthquake to evaluate the effects of the ionospheric artifacts on the slip distribution inversion of earthquake. The split-spectrum method is used to estimate and correct the ionospheric artifacts in the InSAR results. This letter shows that the long-wavelength ionospheric artifacts in the coseismic interferograms can be effectively mitigated. The slip distribution of the earthquake derived from the interferograms corrected for the ionospheric artifacts is presented. The slip distribution pattern and the magnitude of the slip are significantly refined after correcting the ionospheric artifacts.

A new approach is presented for mapping 3-D surface displacement caused by subsurface fluid volumetric change based on 1-D interferometric synthetic aperture radar (InSAR) line-of-sight measurements and surface deformation modeling. The relationship between surface deformation and source fluid volumetric change is modeled according to elastic half-space theory. A distinctive advantage of the proposed approach is that it effectively extends the capability of the sun-synchronous orbit side-looking synthetic aperture radar that has been essentially only able to measure 1-D displacements accurately or at most 2-D displacements when InSAR measurements from more than one orbit or platform are combined. Experimental studies are carried out with both simulated and real data sets to test the performance of the method. The results have demonstrated that the approach works very well.

Ground based interferometric radar (GBIR) is a revolutionary advanced measurement technique for geoscience and engineering geodesy. It is powerful for temporally and spatially dense measurements of highly dynamic target with sub-millimetric accuracy, especially in man-made structures, e.g. buildings, towers, dams and bridges. In this case study, we use a real aperture radar system, the Gamma Portable Radar Interferometer (GPRI-II), to perform near-real-time deformation monitoring of the deck (back side) of a cable-stayed bridge. As a test site, the Ting Kau Bridge at Tsuen Wan, Hong Kong, was continuously measured from two modes of observation, rotated azimuth scanning (RAS) and fixed azimuth scanning (FAS). The results reveal the wind-driven and vehicle-driven non-uniform oscillation of the bridge. The presented works demonstrate the ability of GPRI-II in bridge deformation or oscillation monitoring, which provide a new way for structural health monitoring of bridge.

Coherence loss is a critical issue in interferometric synthetic aperture radar geodesy, particularly when short-wavelength radar images are used to monitor earthquake deformation, and it may result in isolated fringes in an interferogram. The conventional unwrapping algorithms may incompletely unwrap or wrongly estimate the integer jumps between isolated fringes. In this paper, we propose a novel method to connect the isolated fringes in earthquake slip inversion. We use multiple starting points to unwrap the interferogram and then solve the integer ambiguities among the starting points by a dislocation-model-based integer least squares estimator. This estimator allows us to provide a quantitative evaluation of the reliability of the integer solutions in terms of two indicators (the success rate and residual ratio). The algorithm is robust to a certain degree of data noise and fault geometry error, as tested. Simulated experiments and case studies demonstrate that the proposed method can give better unwrapping results than the conventional approaches such as the minimum-cost flow (MCF), statistical-cost network-flow algorithm for phase unwrapping (SNAPHU), and iterative forms of MCF and SNAPHU with the assistance of a slip model. The earthquake slip inversion therefore benefits from the more accurate unwrapping results.

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.

Downsampling is a routine step before applying interferometric synthetic aperture radar (InSAR) data to earthquake inversion because of the high computational burden. In this letter, we make use of the matrix perturbation theory to describe the downsampling process, which is considered as matrix perturbation on inversion equation. First, we derive a formula to quantitatively assess the perturbation on the inversion solution caused by data downsampling. Next, we propose an equation-based InSAR data downsampling algorithm to better reduce the perturbation. The experiment with simulated data demonstrates that our new algorithm preserves the most details from full data inversion comparing with previous algorithms. Finally, we use our method to study the slip distribution of the 2008 Mw 6.3 Dangxiong earthquake.

In standard differential synthetic aperture radar interferometry, there could still be a residual tilt (orbital error) in the interferometric phase due to inaccurate baseline estimation. We demonstrated theoretically that the orbital errors were partially elevation dependent. On the basis of this, we introduced an elevation-dependent item to the conventional polynomial model to simulate, and therefore, compensate the orbital errors, as well as the small scale topographic and/or topography-related phase errors. Robust regression approach was suggested to determine the parameters of the proposed model. The model was validated with both synthetic and real ALOS PALSAR data of the Zhouqu, China mudslide. The synthetic test indicated that upon applying the refined model, the accuracies of phase measurements were improved by nearly two times, compared to those using conventional linear and quadratic models. The real data experiment indicated that after utilizing the refined model, the correlation between the interferogram and the digital elevation model of Zhouqu reduced to about 1/5 of those using linear and quadratic models. This demonstrates that the elevation-dependent phase components have been largely removed by the new model. More importantly, the interferogram corrected by the new model visibly disclosed the deformation area affected by the Zhouqu mudslide.

Orbital errors, characterized typically as longwavelength artifacts, commonly exist in interferometric synthetic aperture radar (InSAR) imagery as a result of inaccurate determination of the sensor state vector. Orbital errors degrade the precision of multitemporal InSAR products (i.e., ground deformation). Although research on orbital error reduction has been ongoing for nearly two decades and several algorithms for reducing the effect of the errors are already in existence, the errors cannot always be corrected efficiently and reliably. We propose a novel model that is able to jointly estimate deformation rates and orbital errors based on the different spatial-temporal characteristics of the two types of signals. The proposed model is able to isolate a long-wavelength ground motion signal from the orbital error even when the two types of signals exhibit similar spatial patterns. The proposed algorithm is efficient and requires no ground control points. In addition, the method is built upon wrapped phases of interferograms, eliminating the need of phase unwrapping. The performance of the proposed model is validated using both simulated and real data sets. The demo codes of the proposed model are also provided for reference.

The coherence of radar echoes is a fundamental observable in interferometric synthetic aperture radar (InSAR) measurements. It provides a quantitative measure of the scattering properties of imaged surfaces and therefore is widely applied to study the physical processes of the Earth. However, unfortunately, the estimated coherence values are often biased due to various reasons such as radar signal nonstationarity and the bias in the estimators used. In this paper, we focus on multitemporal InSAR coherence estimation and present a hybrid approach that mitigates effectively the errors in the estimation. The proposed approach is almost completely self-adaptive and workable for both Gaussian and non-Gaussian SAR scenes. Moreover, the bias of the sample coherence can be mitigated with even only several samples included for a given pixel. Therefore, it is a more pragmatic method for accurate coherence estimation and can be applied actually. Different data sets are used to test the proposed method and demonstrate its advantages.

A novel coherence estimation method for small data sets is presented for interferometric synthetic aperture radar (SAR) (InSAR) data processing and geoscience applications. The method selects homogeneous pixels in both the spatial and temporal spaces by means of local and nonlocal adaptive techniques. Reliable coherence estimation is carried out by using such pixels and by correcting the bias in the estimated coherence caused by the non-Gaussianity in high-resolution SAR scenes. As an example, the proposed method together with coherence decomposition is applied to extract the temporal decorrelation component over an area in Macao. The results show that the proposed algorithms work well over various types of land cover. Moreover, the coherence change with time can be more accurately detected compared to other conventional methods.

We present an approach for reconstructing land surface temperature (LST) time series over mountainous areas based on Regression Kriging (RK) technique and a data processing scheme for filtering out LST noise and artifacts. A total of 1462 eight-day composite Moderate Resolution Imaging Spectroradiometer LST images over central Qinghai-Tibet Plateau over 2003-2010 are reconstructed. The regression model includes four auxiliary predictors-latitude, longitude, elevation, and NDVI-which are proven to be a good estimator for the 8-day LST. Comparison of ground surface temperature (GST) measurements at eight meteorological stations with the raw and reconstructed LST series shows that the reconstruction strategy can effectively recover complete high-quality over-land LST maps and significantly improve the consistency between LST and GST.

A Kalman-filter-based approach is presented for resolving 3-D surface displacements using multisensor, multitrack, and multitemporal interferometric synthetic aperture radar (SAR) measurements. Measurements from each interferogram are projected into the three reference directions and combined in the Kalman filter model with displacements determined from previous interferograms to produce updated displacement measurements. Both simulated and real data sets are used to test the proposed approach. It is found that the method works well when the measurement noise is low. The displacements in the north direction, however, are much lower in accuracy than those in the other two directions and even become unstable when the measurement noise is high due to the polar-orbiting imaging geometries of the current satellite SAR sensors.

MODIS LST products provided by NASA may suffer from missing values and noises from various sources, which can degrade the LST quality and hamper its efficient applications. The paper presents an algorithm to reconstruct complete LST image based on regression analysis of LST with elevation in each sliding window, after filtering low-quality and unreliable pixels. Comparison of reconstructed LST with meteorological temperature measurements (T) indicates that LST is significantly correlated with T with an average correlation coefficient of 0.96 and a mean absolute difference (MAE) of 2.02 K. LSTs and Ts show no significant differences at monthly and yearly scale. The differences between LSTs and Ts have certain correlations with their different spatial and temporal definitions; however some residual noises existing in the reconstructed LSTs indicate more meticulous algorithm needed to work out more accurate RS-LST data.

We map the coseismic deformation of the 2011 Tohoku-Oki earthquake with data from three descending Envisat/ASAR tracks and six ascending ALOS/PALSAR tracks that cover most of northeastern Japan. Due to the inaccurate estimation of the satellite status, orbital ramps commonly exist in the coseismic interferograms, which resulted in inconsistency among the deformation maps released by several research groups. In this letter, calibration has been performed to accurately remove these ramps by a 2-D quadratic-phase model derived based on GPS measurements from the ARIA team at the Jet Propulsion Laboratory and Caltech. The average RMS of the interferometric synthetic aperture radar (InSAR) measurements, as compared with GPS measurements at the validation stations, has decreased from 17.8 to 7.7 cm after the orbital ramp correction is made, indicating that much more accurate InSAR measurements are achieved. The corrected coseismic deformation from the InSAR measurements is consistent with not only the GPS observations at the individual GPS stations but also with the coseismic deformation interferogram from interpolated GPS observation in the SAR viewing directions. The corrected coseismic deformation measurement results show a maximum line-of-sight displacement of up to 3.7 m from the ascending PALSAR tracks and 2.4 m from the descending ASAR tracks, respectively.

Previous approaches that integrate interferometric synthetic aperture radar (InSAR) and GPS measurements for 3-D surface displacement mapping require statistically estimating the variances of the measurements to yield optimal results. We present a variance component estimation approach to weigh the InSAR and GPS measurements in deriving 3-D surface displacements. The approach exploits the observations themselves for determining the weighting scheme, and therefore the a priori information on the stochastic model of the observations is not required. This is of great importance as accurate knowledge on the stochastic model is often unavailable. The performance of the proposed method is validated with both simulated and real datasets.

In this paper, we propose a least-squares-based method for multitemporal synthetic aperture radar interferometry that allows one to estimate deformations without the need of phase unwrapping. The method utilizes a series of multimaster wrapped differential interferograms with short baselines and focuses on arcs at which there are no phase ambiguities. An outlier detector is used to identify and remove the arcs with phase ambiguities, and a pseudoinverse of the variance-covariance matrix is used as the weight matrix of the correlated observations. The deformation rates at coherent points are estimated with a least squares model constrained by reference points. The proposed approach is verified with a set of simulated data.

Synthetic aperture radar interferometry has been applied widely in recent years to ground deformation monitoring although difficulties are often encountered when applying the technology, among which the spatial and temporal decorrelation and atmospheric artifacts are the most prominent. The permanent-scatterer interferometric synthetic aperture radar (PS-InSAR) technique has overcome some of the difficulties by focusing only on the temporally coherent radar targets in a time series of synthetic aperture radar (SAR) images. This paper presents an improved PS-InSAR technique by introducing PS-neighborhood networking and empirical mode decomposition (EMD) approaches in the PS-InSAR solution. Linear deformation rates and topographic errors are estimated based on a least squares method, while the nonlinear deformation and atmospheric signals are computed by singular value decomposition and the EMD method. An area in Phoenix, AZ, is used as a test site to determine its historical subsidence with 39 C-band SAR images acquired by European Remote Sensing 1 and 2 satellites from 1992 to 2000.

Synthetic aperture radar interferometry has been applied widely in recent years to ground deformation monitoring although difficulties are often encountered when applying the technology, among which the spatial and temporal decorrelation and atmospheric artifacts are the most prominent. The persistent-scatterer interferometric synthetic aperture radar (PS-InSAR) technique has overcome some of the difficulties by focusing only on the temporally coherent radar targets in a time series of synthetic aperture radar (SAR) images. This paper presents an improved PS-InSAR technique by introducing PS-neighborhood networking and empirical mode decomposition (EMD) approaches in the PS-InSAR solution. Linear deformation rates and topographic errors are estimated based on a least squares method, while the nonlinear deformation and atmospheric signals are computed by singular value decomposition and the EMD method. An area in Phoenix, AZ, is used as a test site to determine its historical subsidence with 39 C-band SAR images acquired by European Remote Sensing 1 and 2 satellites from 1992 to 2000.

The Vondrak filter is a unique technique for smoothing data. The filter aims to achieve a balance between the fidelity and the smoothness of the filtered results. It can therefore preserve the original attributes of the observational data while, at the same time, smooth out the noise. We reformulate the 1-D Vondrak filter that has been widely used in data processing in fields such as astronomy and geophysics and then extend it into two dimensions. The method of conjugate gradients is used to solve the least squares optimization problem. The proposed 2-D filter is a powerful tool for enhancing the quality of various geoscience and remote sensing data such as satellite images. Various tests with simulated and real synthetic aperture radar interferograms show that the new filter is very effective in removing the noise.

This paper analyzes the relationship between the noise level in interferometric phases at permanent scatters (PS) and the accuracy in deformation measurements with the PS-based differential SAR interferometry (PS-DInSAR). The study is carried out based on interferograms that are simulated with parameters of 26 ERS-1/2 SAR scenes over Shanghai. The results show that in the cases of high phase signal to noise ratio (noise level lower than plusmn0.5 rad), the accuracy of the deformation rates estimated with PS-DInSAR can be up to about plusmn2 mm/a, and the accuracy of the estimated elevations is about plusmn1 m. The accuracies decrease with the increase of the noise level of the phase data. When the noise level is plusmn0.8 rad, the accuracy of deformation measurements decreases to plusmn1 cm/a and that of elevation measurements decreases to plusmn3 m.

Differential interferometric synthetic aperture radar (SAR) (DInSAR) has proven to be very useful in mapping and monitoring land subsidence in many regions of the world. Shanghai, China's largest city, is one of such areas suffering from land subsidence as a result of severe withdrawal of groundwater for different usages. DInSAR application in Shanghai with the C-band European Remote Sensing 1 & 2 (ERS-1/2) SAR data has been difficult mainly due to the problem of decorrelation of InSAR pairs with temporal baselines larger than 10 months. To overcome the coherence loss of C-band InSAR data, we used eight L-band Japanese Earth Resource Satellite (JERS-1) SAR data acquired during 2 October 1992 to 15 July 1998 to study land subsidence phenomenon in Shanghai. Three of the images were used to produce two separate digital elevation models (DEMs) of the study area to remove topographic fringes from the interferograms used for subsidence mapping. Six interferograms were used to generate 2 different time series of deformation maps over Shanghai. The cumulative subsidence map generated from each of the time series is in agreement with the land subsidence measurements of Shanghai city from 1990 - 1998, produced from other survey methods.

2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005, Seoul, 25-29 July 2005