External Digital Elevation Model Research Articles

Underlying Topography Estimation over Forest Using Maximum a Posteriori Inversion with Spaceborne Polarimetric SAR Interferometry

This paper presents a method for extracting the digital elevation model (DEM) of forested areas from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The method models the ground phase as a Von Mises distribution, with a mean of the topographic phase computed from an external DEM. By combining the prior distribution of the ground phase with the complex Wishart distribution of the observation covariance matrix, we derive the maximum a posterior (MAP) inversion method based on the RVoG model and analyze its Cramer–Rao Lower Bound (CRLB). Furthermore, considering the characteristics of the objective function, this paper introduces a Four-Step Optimization (FSO) method based on gradient optimization, which solves the inefficiency problem caused by exhaustive search in solving ground phase using the MAP method. The method is validated using spaceborne L-band repeat-pass SAOCOM data from a test forest area. The test results for FSO indicate that it is approximately 5.6 times faster than traditional methods without compromising accuracy. Simultaneously, the experimental results demonstrate that the method effectively solves the problem of elevation jumps in DEM inversion when modeling the ground phase with the Gaussian distribution. ICESAT-2 data are used to evaluate the accuracy of the inverted DEM, revealing that our method improves the root mean square error (RMSE) by about 23.6% compared to the traditional methods.

Monitoring and Comparative Analysis of Hohhot Subway Subsidence Using StaMPS-PS Based on Two DEMS

The subway alleviates the traffic pressure in the city but also brings the potential risk of land subsidence. The land subsidence caused by the subway is a global problem that seriously affects the safety of subway operations and surrounding buildings. Therefore, it is very important to carry out long-term deformation monitoring on the subway system. StaMPS-PS is a time-series Interferometric Synthetic Aperture Radar (InSAR) technique that serves as an effective means for monitoring urban ground subsidence. However, the accuracy of external (Digital Elevation Models) DEM will affect the accuracy of StaMPS-PS monitoring, and previous studies have mostly used SRTM-1 arc DEM (30 m) as the external DEM. In this study, to obtain a more precise measurement of surface deformation caused by the excavation of the Hohhot subway, a total of 85 scenes of Sentinel-1A data from July 2015 to October 2021, as well as two different resolution digital elevation models (DEMs) (ALOS PALSAR DEM and SRTM-1 arc DEM), were used to calculate and analyze the subsidence along the subway line in Hohhot city. The StaMPS-PS monitoring results showed the ALOS PALSAR DEM, as an external DEM, had higher accuracy, and there was regional subsidence in both the construction processes of Line 1 and Line 2 of the Hohhot subway, with a maximum subsidence rate of −21.1 mm/year. The dynamic changes in subway subsidence were fitted using the Peck formula and the long short-term memory (LSTM) model. The Peck formula results showed the width and maximum subsidence of the settlement troughs gradually expanded during the construction of the subway. The predicted values of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the LSTM model were less than 4 mm and 10%, respectively, consistent with the measured results. Furthermore, we discussed the factors that affect settlement along the subway line and the impact of two external DEMs on StaMPS-PS. The study results provide a scientific method for DEM selection and subsidence analysis calculations in the StaMPS-PS monitoring of urban subway subsidence.

Image-Based Baseline Correction Method for Spaceborne InSAR With External DEM

An accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for the geodetic application of the InSAR data. Although some advanced SAR satellites have precise orbit determination, there are still many SAR satellites suffering from baseline inaccuracies, such as GF-3. In this article, an image-based estimator for baseline correction is proposed, which requires only the external digital elevation model (DEM) data. The idea of the method is to project the orbit error phase onto the phase components carrying the baseline error information, which is called orbit error phase bases in this article, and to correct the baseline according to the projection coefficients. Since the pure orbit error phase is unavailable, the residual phase of the interferogram is used to approximate the orbit error phase, and a series of processes are introduced to weaken the effect of this approximation. Both the simulated and real data from GF-3 SAR are used to validate the proposed method, and a comparison with the conventional nonlinear least-square and the latest proposed flat-Earth phase-based baseline refinement methods are made. The results indicated the superior accuracy and robustness of our method, especially in areas with higher relief and wider coverage.

Route Plans for UAV Aerial Surveys according to Different DEMs in Complex Mountainous Surroundings: A Case Study in the Zheduoshan Mountains, China

Accurate and error-free digital elevation model (DEM) data are a basic guarantee for the safe flight of unmanned aerial vehicles (UAVs) during surveys in the wild, especially in moun-tainous areas with large topographic undulations. Existing free and open-source DEM data gen-erally cover large areas, with relatively high spatial resolutions (~90, 30, and even 12.5 m), but they do not have the advantage of timeliness and cannot accurately reflect current and up-to-date topographical information in the survey area. UAV pre-scanning missions can provide highly accurate and recent terrain data as a reference for UAV route planning and ensure security for subsequent aerial survey missions; however, they are time consuming. In addition, being limited to the electric charge of the UAV, pre-scanning increases the human, financial, and time consumption of field missions, and it is not applicable for field aerial survey missions in reality, unless otherwise specified, especially in harsh environments. In this paper, we used interferometric synthetic aper-ture radar (InSAR) technology to process Sentinel-1a data to obtain the DEMs of the survey area, which were used for route planning, and other free and open-source DEMs were also used for flightline plans. The digital surface models (DSMs) were obtained from the structure of the UAV pre-scan mission images, applying structure for motion (SfM) technology as the elevation reference. Comparing the errors between the InSAR-derived DEMs and the four open-source DEMs based on the reference DSM to analyze the practicability of flight route planning, the results showed that among the four DEMs, the SRTM DEM with a spatial resolution of 30 m performed best, which was considered as the first reference for UAV route plans when the survey area in complex mountainous regions is covered with a poor or inoperative network. The InSAR-derived DEMs from the Sentinel-1 images have great potential value for UAV flight planning, with a large perpendicular baseline and short temporal baseline. This work quantitatively analyzed the errors among the different DEMs and provided a discussion regarding UAV flightline plans based on external DEMs. This can not only effectively reduce the manpower, materials, and time consumption of field operations, improving the efficiency of UAV survey tasks, but it also broadens the use of InSAR technology. Furthermore, with the launch of high-resolution SAR satellites, InSAR-derived DEMs with high spatial and temporal resolutions provide an optimistic and credible strategy for UAV route planning with small errors.

A Novel 2-D Phase Unwrapping Method Based on PUMA for Aerospace Topographic Mapping Systems

Two-dimensional phase unwrapping (PU) is the key step of interferometric synthetic aperture radar technology. The accuracy of the PU results directly affects the quality of the final product. Due to the limitation of the phase continuity assumption, the results obtained by the single-baseline PU in large gradient regions are not ideal. To solve this problem, this article proposes an improved PU max-flow/min-cut algorithm (PUMA) which can efficiently deal with the phase of large gradient regions. First, we construct a quasiconvex function which is the combination of logarithmic and quadratic functions as the potential function in the energy function model of the PUMA method. Then, we use the gradient information of the external digital elevation model to assist in setting the weight of the potential function. Finally, the unwrapped phase is obtained by minimizing the new energy function model. The experimental results on the TanDEM-X dataset show that the improved approach in this article can achieve higher accuracy PU results in the region of large gradient variation than the existing PU methods.

TECHNOLOGY ON HIGH-ACCURACY DEM EXTRACTION FROM AIRBORNE INTERFEROMETRIC SAR

Abstract. Remote sensing image mapping (aviation, aerospace, UAV) is the main form of current mapping. Optical image mapping has been a certain research foundation and played an important role in the mapping task. However, cloud, ice, snow, rain, and haze conditions seriously affect the interpretation and processing of optical images, making it difficult to meet the needs of rapid emergency response. Synthetic Aperture Radar (SAR) is currently the only method that can achieve all-day, all-weather rapid imaging under various weather conditions such as day, night, cloud, snow, rain, etc. With unique advantages unmatched by traditional optical remote sensing technology, it is an important technical guarantee for improving emergency response capabilities and promoting remote sensing and geographic information industries, and an earth observation technology that has been invested and vigorously developed internationally. Digital Elevation Model (DEM) plays an increasingly important role in the application of terrain information acquisition such as rapid acquisition of sudden disasters and surface deformation information acquisition. Rapid acquisition of large area high-accuracy DEM is one of the important research contents of SAR mapping. Based on interferometric SAR data combined with sensor orbit data, external DEM and other auxiliary data, DEM is finally generated through complex image registration, interference image generation, removal of flat ground effects, interference phase filtering, phase unwrapping, interference calibration and geocoding. The registration accuracy of multiple images greatly affects the accuracy of DEM. This paper proposes an improved registration method based on the conventional rough registration, pixel-level registration and sub-pixel registration. When the motion compensation of the sensor platform is unknown during imaging, it can meet the requirements of high-accuracy and large-scale rapid registration of dual-antenna polarization interferometric SAR. The experimental data is X-band airborne SAR data in Weinan region of Shaanxi Province, and DEM products with the elevation accuracy better than 0.5m is generated, which verifies the effectiveness, accuracy and fast processing ability of the method.

Retrieval of Svalbard ice flow velocities using Sentinel 1A/1B three-pass Differential SAR Interferometry

Glacier velocity is an important parameter to characterize glacier dynamics and to derive ice thickness and mass balance. The 2-pass/3-pass Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques are advantageous in estimating glacier movements. However, the 2-pass DInSAR requires an external Digital Elevation Model (DEM), whereas the 3-pass DInSAR does not. The 3-pass DInSAR technique is adopted with Sentinel-1A/1B to map the Svalbard glacier flow velocities. Furthermore, the effect of glacier surface elevation change on the 2-pass DInSAR results were revealed by comparing glacier velocity with the two time period topographic information. The coherence indicates that Sentinel-1A/1B has a high potential to infer operational glacier velocity using the 3-pass DInSAR method with an average atmospheric uncertainty of 0.24 cm/day over the studied region. The precision of the 3-pass DInSAR is analyzed by comparing 2-pass derived line-of-sight (LOS) velocity on the same dates. The 3-pass DInSAR achieves high-resolution and detailed information.

Refining the conversion between phase and height in airborne and UAV-borne SAR interferometry

ABSTRACT Airborne or unmanned aerial vehicle (UAV)-borne interferometric synthetic aperture radar (InSAR) can offer topography and forest measurement with high accuracy and high resolution. In these measurements, the conversion between height and interferometric phase is frequently involved. The existing conversion approximations may be more suitable for the space-borne InSAR. However, for the airborne or UAV-borne InSAR in a low flight altitude, these conversion formulas cannot work well for topography and forest height measurement. To achieve high-quality InSAR measurements, we derive a refined conversion formula based on the Taylor expansion, i.e. Firsec-order conversion formula. Differing from existing studies contributed by first-order derivative, the Firsec-order conversion formula expands a second-order term. During refined phase-to-height (PHAtoH) conversion, an external low-precision digital elevation model (DEM) is introduced to calculate and then remove the second-order phase from the original interferometric phase. Thus, the high-precision DEM is reconstructed based on the refined phase rather than the differential phase. In addition, this article investigates the sensitivity of the second-order term to different baseline lengths and flight altitudes with respect to two common baseline configurations in airborne or UAV-borne InSAR. The results show that for the horizontal baseline configuration, the second-order term must be considered. In addition, the longer the baseline and the lower the flight altitude, the larger the second-order phase, especially for the UAV-borne InSAR geometries with altitudes of under 2 km. However, for the vertical baseline configuration, the adoption of the refined formula depends on the actual situation. The performance test shows that the Firsec-order conversion formula has much higher conversion accuracies than traditional ones.

Automated digital elevation model (DEM) generation from very-high-resolution Planet SkySat triplet stereo and video imagery

The Planet SkySat-C SmallSat constellation can acquire very high resolution (0.7 m to 0.9 m) triplet stereo and video imagery with short revisit times, providing an exciting opportunity for global, on-demand 3D mapping of dynamic surface features. However, a lack of suitable processing software, limited geolocation accuracy, and scene-to-scene offsets currently limit the potential for accurate SkySat digital elevation model (DEM) production. We developed an open-source workflow to refine the SkySat-C camera models and improve absolute image geolocation using external reference DEMs, without manual ground control point (GCP) selection. The refined camera models are used to generate accurate and self-consistent DEMs with 2-m posting and orthoimages at native resolution. We present sample DEM products for a triplet stereo collection over Mt. Rainier, USA and two video collections over Mt. St. Helen’s, USA. The output DEMs display <1 to 2 m relative and <2 to 3 m absolute vertical accuracy when compared to DEMs generated with stereo image pairs acquired by the DigitalGlobe/Maxar WorldView satellites and airborne LiDAR. Differencing the two SkySat-C video DEMs over Mt. St. Helen’s shows elevation change of ~5 to 15 m due to melting of seasonal snow and glacier flow. Our workflow can be scaled for batch processing of SkySat stereo imagery, and extended to other frame camera systems with limited initial geolocation accuracy.

A Comparative Study of DEM Reconstruction Using the Single-Baseline and Multibaseline InSAR Techniques

Interferometric SAR (InSAR) is a practical technique to derive 3-D structure of the earth's surface. This article conducts a comparative study of digital elevation model (DEM) reconstruction using the single-baseline and multibaseline InSAR techniques. To improve the accuracy of the DEM generated from the single-baseline InSAR, we improve the weight definition approach according to the coherence magnitudes for the fusion of single-baseline InSAR DEMs. For multibaseline InSAR, we adopt a method based on the maximum-likelihood estimation (MLE) algorithm, which uses external DEM as a constraint. A TerraSAR-X dataset comprising four SAR images acquired over Barcelona is used for experimental investigation. The accuracies of four types of DEM are comparatively studied, including DEM by single-baseline InSAR, DEM by the fusion of single-baseline InSAR DEMs, DEM by unconstrained multibaseline InSAR, and DEM by constrained multibaseline InSAR. The results show that the proposed weighted fusion of single-baseline InSAR performed slightly better than the constrained multibaseline InSAR method, and both of them performed better than the single-baseline results, demonstrating that multiple observations can effectively improve the accuracy of the reconstructed DEM.

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

The interferometric synthetic aperture radar (InSAR) elevation inversion method based on the backprojection (BP) imaging model simplifies the interferometric processing chain where image registration and phase unwrapping are not required. Meanwhile, BP algorithm is suitable for radar with different imaging modes and geometry. However, the existing calibration methods are all aimed at traditional InSAR which adopts frequency domain imaging algorithm, but the calibration method for backprojection InSAR elevation inversion model has not been published. In this paper, we propose a novel InSAR calibration method which combines Fast Fourier Transform (FFT) estimation method with sensitivity equation method based on backprojection imaging model. FFT is used to process the interferometric phase which has removed the terrain phase of external digital elevation model (DEM) under BP imaging, and the estimation of interferometric parameters is realized through the phase fringe frequency. In the calibration method based on sensitivity equations, the deviations of interferometric parameters are calculated by solving sensitivity equations according to the three-dimensional information of ground control points. FFT estimation method not only generates the initial baseline parameters for sensitivity equation method, which is conducive to obtaining the global optimal solution, but also separates the calibration of phase offset and baseline parameters, which makes it possible to minimize the condition number of sensitivity matrix in sensitivity equation method. Finally, we obtain the calibrated interferometric parameters through iteration of sensitivity equation method and realize high accuracy DEM inversion. Both simulation experiment and airborne dual-antenna InSAR data processing are performed to verify the effectiveness of this method.

Correction of Time-Varying Baseline Errors Based on Multibaseline Airborne Interferometric Data Without High-Precision DEMs

The time-varying baseline error (TVBE) caused by the residual motion errors is one of the most significant error sources in the airborne interferometric synthetic aperture radar (InSAR). Although global digital elevation model (DEM) products can be used to extract the TVBE for areas without high-precision DEMs, they cause remarkable height differences (HDs) relative to the actual InSAR-measured topography. In addition, due to the low flight height of airborne SAR platforms, the range-dependent phases resulted from the HDs have significant impacts on the TVBE estimation. Through the mutual restraint of multibaseline observations, this research establishes a multibaseline parameterized model jointly considering HDs and TVBEs in each range direction for the absolute phase calibration. A weighted truncated singular value decomposition (TSVD) algorithm is used to solve the ill-posed problem in joint parameter estimation. Note that, to improve the TVBE extraction, a two-step strategy is adopted. In step one, TVBEs and HDs are preliminarily estimated by the multibaseline parameterized model. After that, bare areas with flat topography, where the truths of HDs can be assumed to be 0, are selected to correct the bias of the estimated HDs. If the flat and bare areas are absent, global ecosystem dynamics investigation (GEDI) or Ice, Cloud, and land Elevation Satellite (ICESAT) points can be used instead. In step two, the multibaseline parameterized model is updated by the calibrated DEM and then used to reestimate the TVBEs. The proposed method was validated by using a simulated data set and two real data sets (i.e., the P- and L-band data sets acquired by the airborne E-SAR system during the BioSAR2008 campaign). The results show that, with respect to the reference values, the root-mean-square error (RMSE) of the calibrated DEMs is 0.48 m for the simulated data set, 2.6 m for the P-band data set, and 3.6 m for the L-band data set, suggesting that the high-precision DEMs are well reconstructed and the TVBE-induced phases can be precisely extracted. After correcting the TVBE-induced phases, the accuracy of the DEMs generated by every linked phase (LP) is improved by at least 38.2% for P-band and by at least 51.0% for L-band. To assess the effectiveness and practicability of the proposed model, we further investigated the influence of the limited multibaseline number, and the baseline combination, and the precision of external DEMs on the TVBE correction. In conclusion, without high-precision DEMs, the multibaseline parameterized method can work well in accurately extracting the TVBE, even for the limited airborne InSAR data set.

A Phase Slicing 2-D Phase Unwrapping Method Using the L1-Norm

2-D phase unwrapping (PU) is one of the key steps in the processing of interferometric synthetic aperture radar (InSAR) data. PU in large-gradient areas has always been a fundamental problem. The phase continuity assumption makes it difficult to obtain ideal PU results in large-gradient areas. To address this issue, in this letter, we propose a phase slicing 2-D PU method using the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm that can effectively reduce the phase gradient interval. First, the study area is analyzed using an external digital elevation model (DEM) and interferometric phase information to obtain the number of phase slices and assisted phases. Then, we cut the interferometric phase to obtain the sliced interferometric phase and use the single-baseline (SB) L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm PU method to unwrap the sliced interferometric phase. Finally, the unwrapped phase is recovered by summing the sliced unwrapped phases and assisted phases. Experiments are conducted with simulated and TanDEM-X InSAR data sets. Compared with the representative SB-PU method, the proposed method can effectively avoid the problem of large gradients in PU.

Mars Surface Imaging by Exploiting Off-Nadir Radar Sounding Data

Radar sounder surface imaging is a rather unexplored approach to the analysis of planetary bodies. While a radar sounder is an instrument specifically designed for subsurface investigations, a particular set of power measurements (denoted as off-nadir surface echoes) can be exploited together with an external digital elevation model to produce images of the investigated surface at meters wavelength. The use of the off-nadir data may also reveal the presence of previously undetected subsurface features. In this article, we present a method for producing surface roughness images by high-frequency (HF) radar sounder data. The study of surface roughness in the HF band is particularly useful for both geologic studies and landing-zone reconnaissance as it is evaluated at meters to hundreds of meters horizontal scale. The proposed method combines off-nadir data of the Shallow Radar Sounder (SHARAD) with the Mars Orbiter Laser Altimeter (MOLA) digital elevation model. The produced roughness images at 20 MHz (15-m wavelength) of the Martian surface provide higher coverage and resolution of the surface roughness characterization at a 10-100-m horizontal scale than previous SHARAD work. By comparing the experimental roughness image with the one obtained by radar backscattering simulations, it is possible to identify subsurface features. In our experiments, we were able to produce a bidimensional image of a previously undetected large buried crater (10 km ×12 km) located in the Nili Fossae. This finding opens up new possibilities in exploiting radar sounding data for better detecting shallow subsurface features.

Impacts of Systematic Errors on Topographic Parameter Estimation in Multitemporal InSAR: A Quantitative Description

Estimation of surface deformation using synthetic aperture radar interferometry (InSAR) technique requires a precise removal of the topographic phase. Under multitemporal InSAR (MTInSAR) framework, topographic residual raised by differential operation with external Digital Elevation Model (DEM) is usually parameterized and jointly estimated together with deformation model, while the estimation can be distorted by systematic errors (e.g., model bias, baseline error). This letter aims to offer practical guidelines to users of MTInSAR framework concerning these errors and estimation precision. Starting from the generalized model, we derived the error propagation formula to quantitatively indicate how and to what extent the systematic errors degrade the topographic parameter estimation. The formulas validated by simulated tests are expected to be useful for optimal selection of MTInSAR modeling strategies and development of innovative algorithms (e.g., non-parametric estimator) for retrieval of DEM residuals from MTInSAR measurements.

How Different Analysis and Interpolation Methods Affect the Accuracy of Ice Surface Elevation Changes Inferred from Satellite Altimetry

Satellite altimetry has been widely used to determine surface elevation changes in polar ice sheets. The original height measurements are irregularly distributed in space and time. Gridded surface elevation changes are commonly derived by repeat altimetry analysis (RAA) and subsequent spatial interpolation of height change estimates. This article assesses how methodological choices related to those two steps affect the accuracy of surface elevation changes, and how well this accuracy is represented by formal uncertainties. In a simulation environment resembling CryoSat-2 measurements acquired over a region in northeast Greenland between December 2010 and January 2014, different local topography modeling approaches and different cell sizes for RAA, and four interpolation approaches are tested. Among the simulated cases, the choice of either favorable or unfavorable RAA affects the accuracy of results by about a factor of 6, and the different accuracy levels are propagated into the results of interpolation. For RAA, correcting local topography by an external digital elevation model (DEM) is best, if a very precise DEM is available, which is not always the case. Yet the best DEM-independent local topography correction (nine-parameter model within a 3,000 m diameter cell) is comparable to the use of a perfect DEM, which exactly represents the ice sheet topography, on the same cell size. Interpolation by heterogeneous measurement-error-filtered kriging is significantly more accurate (on the order of 50% error reduction) than interpolation methods, which do not account for heterogeneous errors.

Nonparametric Estimation of DEM Error in Multitemporal InSAR

Isolating phase residuals due to inaccurate external digital elevation model (DEM) is important in retrieval and interpretation of deformation behavior from interferometric synthetic aperture radar (InSAR) observations. Multitemporal InSAR (MTInSAR), by taking DEM error as a parameter, can make the isolation possible. However, due to the presence of atmospheric artifacts in observations and improper deformation model employed in the observation system, accurate retrieval of DEM error cannot be guaranteed in current MTInSAR techniques. Considering that the DEM error has a fixed spatial pattern and its contribution to interferometric phase-only changes with spatial baselines, we propose here a nonparametric method that can estimate the DEM error in a more robust way. To retrieve signals having a fixed spatial pattern from unwrapped MTInSAR measurements, the independent component analysis (ICA) is used. Experiments with synthetic and real data sets indicate the proposed method is able to estimate DEM error with no $a$ priori information about deformation. Moreover, experiments also show that the method can provide a more robust estimation when the observed phase observations are affected by atmospheric delays and/or the number of interferograms used is limited.

Minimizing Height Effects in MTInSAR for Deformation Detection Over Built Areas

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.

Land subsidence rate analysis of Jakarta Metropolitan Region based on D-InSAR processing of Sentinel data C-Band frequency

Land subsidence in Jakarta has been reported by many studies using various geodetic techniques. High exploitation of groundwater is the main factor of increasing land subsidence rate in Jakarta. The impact of land subsidence can be seen in several forms such as the damage on buildings and infrastuctures and the change of surface water pattern flow. This paper presents the update status and analysis of land subsidence of Jakarta Metropolitan Region (Jabodetabek) based on Differential Interferometric Synthetic Aperture Radar (D-InSAR) technique. D-InSAR technique based on interferogram that developed from a coherence technology of active radar imaging. Two images acquired with the same nominal geometry is required to develop interferometry SAR by using phase as a fraction of the wave, and change to distance. The displacement map is the final result of interferometry SAR and external Digital Elevation Model (DEM) required to remove topographic phase. Displacement map using pair of Sentinel data that acquired on March 18, 2017 and March 13, 2018 was successfully developed. The highest rate of land subsidence with 6 cm/year occurred in eastern part and western part of north area of Jakarta, some part in West Jakarta, Central Jakarta, and South Jakarta. The similar rate also occurred in Bekasi City, Bekasi Regency, Depok City, and Tangerang Regency. The moderate rate of land subsidence occurred in Tangerang City and South Tangerang City with 2-3 cm/year. Jakarta Metropolitan Region is a susceptible area of flooding. In this study, land subsidence has a similar pattern with the emergence of flood inundation. Therefore, the increasing of areas with flood inundation is affected by land subsidence. Monitoring of land subsidence rate in Jakarta Metropolitan Region by using D-InSAR Sentinel data is highly required due to high temporal resolution and accurate data especially for flood management and other urban development management, and free access of the data.

Estimation of terrain’s linear deformation rates using synthetic aperture radar systems

Synthetic Aperture Radars (SAR) are currently one of the most popular systems in the remote sensing domain, being widely utilized in the earth observation field. Their range of applicability extends in both marine and terrestrial regions. In the maritime domain, SAR systems are intensively used for the study of oceanic waves, waves breaking, marine currents, underwater topography, oil stains, for monitoring the glacier’s ice flow, and also for ships detection and localization. In the land areas, a class of applications which exploits the coherence propriety of SAR signals is able to retrieve information related to terrain’s characteristics, like topography and displacements. In this work, a processing chain for linear deformation rates estimation is presented and implemented on a dataset of 30 SAR images of Buzău and Focşani cities regions (Romania). The algorithm is based on identification of targets with stable electromagnetic response, exploiting their temporal coherence to obtain reliable estimates. An iterative phase regression analysis is conducted exclusively in the set of detected stable targets. The main challenge is represented by the estimation of the residual component of the phase, due to its random nature. Main feature of the proposed processing chain consists in the fact that it includes a step for terrain’s topography estimation, instead of using an external digital elevation model.