Small Baseline Subset Algorithm Research Articles

Surface Deformation Assessment due to the Grindulu Fault Activities using Sentinel 1 Data by Applying the Small Baseline Subset (SBAS)-InSAR Technique

The location of Grindulu Fault is in the Pacitan Regency on the Southern Coast of Java, which directly faces the South Java Subduction zone. The activities of the fault could contribute to the surface deformation in the surrounding area. The Meteorology Climatology and Geophysics Council in Indonesia (BMKG) reported a 3.1 Mw earthquake on 7 November 2019, possibly related to the Grindulu fault activities. In this research, we assess the surface deformation around the Grindulu Fault using the Sentinel I data by applying the Small Baseline Subset (SBAS) - Interferometry Synthetic Aperture Radar (InSAR) technique. The SBAS-InSAR is one of the techniques that can provide spatial-temporal continuous analysis of the surface deformation over the extent of InSAR data. The SBAS algorithm was applied to twenty-three (23) ascending Sentinel-1B images to assess the surface deformation in Pacitan between 2019 and 2021 using the GMTSAR software. The results showed surface deformation dominated by subsidence over the whole area, except at the northern part of Grindulu Fault, with the mean velocity values ranging from -2.07 to -43.05 mm/yr.

Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence to predict groundwater levels using these indicators, particularly in regions experiencing drought and subsidence. Precipitation, temperature, evapotranspiration and precipitable water vapor (PWV) are important meteorological parameters to predict groundwater levels. Two subsidence areas with different weather conditions were selected to conduct a comprehensive study on the effect of temperature and precipitation on groundwater level changes. The correct locations of the two subsidence areas were determined by analyzing Interferometric Synthetic Aperture Radar (InSAR) images of Sentinel-1A using the small baseline subset algorithm. The interferograms were processed to correct tropospheric effects using the advanced integration method. Specifying the exact locations of the two areas, the meteorological parameters were downscaled using the Statistical DownScaling Model (SDSM), synoptic observations, meteorological data, and the General Circulation Model (GCM). An Artificial Neural Network (ANN) was then employed to predict the groundwater level changes as a function of meteorological data, including Global Positioning System (GPS)-based PWV and the evapotranspiration index. The trained ANN, along with the downscaled meteorological indicators, was used to predict groundwater level changes over two time periods. In the first period, the prediction was performed over the current years to investigate the performance of the method using the available data, whereas in the second period, the prediction was performed for the coming years, up until 2030. The results confirmed the high performance of the prediction algorithm, and the importance of including PWV and evapotranspiration in groundwater level predictions. The Pearson correlation coefficient was used to check the relationship between groundwater level changes and meteorological variables. The statistical significance of these coefficients was tested at the significance level α=0.05. In more than 80% of the cases, the correlation coefficients were statistically significant, reaching more than 0.70 in some of the months. It is also observed that an increase in the depth of groundwater level has an obvious relationship with an increase in temperature and a decrease in rainfall.

Investigation of Ground Deformation around The Segments of Kendeng Fault in East Java 2017-2020 using Multi-Temporal InSAR Analysis with GACOS Correction Product

Multi-Temporal Interferometric SAR (MT-InSAR) analysis technique can provide spatially continuous measurements of the ground deformation and monitoring caused by the movement of four segments in the Kendeng Fault, East Java. Most ground deformation in these segments is indicated by the dominance of local thrust movements, folds, and the seismic density of earthquakes. In this study, ground deformation detection was carried out using the Small Baseline Subset Algorithm in the MT-InSAR process with the tropospheric correction from GACOS to determine ground deformation during 2017-2020. The percentage of change in the deformation velocity value resulting from applying the GACOS correction is up to 33.8% compared to without the tropospheric correction. The most significant troposphere effect correction is the area around the Cepu segments, which are dominated by high elevation thrust and folds. The results of the mean LOS velocity around the southern segments of Cepu, Waru, and Surabaya based on the Sentinel 1A SAR represent the occurrence of land subsidence in the south part of the area around segments with values ranging from 3.0 mm/yr to 6.0 mm/yr. Thus, the deformation effect causes land subsidence in the area around the delineation of the northern fault segment up to -3.0 mm/yr.

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

In the past 30 years, the small baseline subset (SBAS) InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters. Because of its ability to monitor large-scale deformation with millimeter accuracy, the SBAS method has been widely used in various geodetic fields, such as ground subsidence, landslides, and seismic activity. The obtained long-term time-series cumulative deformation is vital for studying the deformation mechanism. This article reviews the algorithms, applications, and challenges of the SBAS method. First, we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm, which provides a basic framework for the following improved time series methods. Second, we classify the current improved SBAS techniques from different perspectives: solving the ill-posed equation, increasing the density of high-coherence points, improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation. Third, we summarize the application of the SBAS method in monitoring ground subsidence, permafrost degradation, glacier movement, volcanic activity, landslides, and seismic activity. Finally, we discuss the difficulties faced by the SBAS method and explore its future development direction.

Satellite radar interferometry: Potential and limitations for structural assessment and monitoring

The present paper analyzes potential and limitations of multi-temporal Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques applied for the structural monitoring and assessment of constructions affected by different external actions. A comprehensive approach for the processing, interpretation and proper use of satellite radar interferometry data for structural analyses at urban and single-construction scales is proposed. Following a brief literature review, highlighting the most relevant applications in this field, some aspects concerning DInSAR techniques are shortly summarized, particularly focusing on the Small BAseline Subset (SBAS) approach applied to perform advanced analyses at structural level. Then, the main issues of the DInSAR measurements exploitation for structural applications are discussed, focusing on the use of both one or two datasets relevant to ascending and descending satellite passes. Analysis at the scale of single construction (i.e., building, bridge, etc.) of displacements time series and mean deformation velocities is discussed, in order to identify scenarios that may become potentially critical for the structures. Some application examples are provided, employing COSMO-SkyMed (CSK) datasets relative to the urban area of Rome (Italy), acquired during the 2011–2019 time interval and processed through the SBAS algorithm. Specific considerations related to the use of DInSAR measurements for structural monitoring and assessment are then proposed, also discussing possible approaches for preliminary and detailed evaluation of structural damages.

Studying postseismic deformation of the 2010–2011 Rigan earthquake sequence in SE Iran using geodetic data

In this paper, we study the spatial distribution and temporal evolution of the postseismic deformation of the 2010–2011 Rigan earthquake sequence which occurred at the southern termination of the East Lut fault system, southeast Iran. One-year GPS measurements after the Rigan earthquake sequence reveals right-lateral postseismic displacement along the East Chahqanbar fault and left-lateral postseismic displacement along the South Chahqanbar fault. To investigate the deformation variations in time and space, InSAR time-series of COSMO-SkyMed images is applied using the Small Baseline Subset algorithm. The results confirm a clear cumulative postseismic signal increasing to 8 mm during the first five months following the first mainshock in the direction of the coseismic displacement. The cumulative postseismic displacements are well correlated with the cumulative number of the aftershocks and their associated moment release. Considering this correlation and the observation of a sharp discontinuity along the coseismic fault in the displacement map, it is concluded that the after-slip mechanism is responsible for the observed postseismic deformation in the study region. This study is the first observation of a short-term postseismic motion in eastern Iran through geodetic data in contrast with long-lasting postseismic displacements following the earthquakes that occurred around Lut block. Modeling of the postseismic displacement results in a distributed slip pattern with a maximum slip of 0.8 m on the fault plane responsible for the 2010 Rigan coseismic deformation. This indicates that the postseismic deformation on barriers remained unbroken during the mainshock.

Evidence for Active Rhyolitic dike Intrusion in the Northern Main Ethiopian Rift from the 2015 Fentale Seismic Swarm

AbstractMagmatic intrusions play a vital role not only in accommodating extensional stresses in continental rifts but also in feeding volcanic systems. The location, orientation, and timescale of dike intrusions are dictated by the interaction of regional and local stresses, the effect of pre‐existing weaknesses, and the composition of magma. Observing active intrusions can provide important information regarding the interaction between magmatic processes and the tectonic stress field during continental rifting. We focus on a seismic swarm that occurred in 2015 to the northeast of Fentale volcano, in the Main Ethiopian Rift (MER), and use radar interferometry to study surface deformation associated with the seismic swarm. Interferograms show a pattern of dike‐induced deformation, with a model estimate of volume change of 33×106±0.6×106m3 at a depth range of 5.4 to 8 km. We use a small baseline subset algorithm to calculate line of sight time series and find that the displacements decay exponentially with a decay constant of ∼83 days. Coupled source‐sink models suggest that such slow dike intrusions require a high viscosity rhyolitic magma. The difference in behavior between Fentale and other caldera systems in the MER, which show multi‐year cycles of inflation and deflation, suggests fundamental differences in magma composition and architecture of the plumbing system. This is the first direct observation of a dike intrusion in the MER and provides new constraints on the temporal‐spatial patterns of stress and strain that occur during continental rifting. Whether this activity is transient or a long‐term feature associated with rift evolution is an open question.

Application of Time Series InSAR (SBAS) Method using Sentinel-1A Data for Land Subsidence Detection in Surabaya City

We studied land subsidence occurrence in Surabaya, considering the rapid urban growth in the city. It is evidenced by the increasing number of buildings (e.g., houses, apartments, offices) that have been realized before 2015 and the increasing transport activities in the industrial area. Land subsidence is a vertical deformation on the land surface that can be caused by landslides, earthquakes, soil consolidation process, or even human-made activities, depending on the material characteristics of the area. In this study, Time-series InSAR (TS-InSAR), specifically the Small Baseline Subset (SBAS) method, is applied to obtain the Line-of-Sight (LOS) velocities and time-series LOS displacements in Surabaya between May 2015 and September 2017. We used 28 Sentinel-1A data (IW mode) acquired by using Terrain Observation with Progressive Scan (TOPS) operation with the spatial resolution of 5 ´ 20 m and covers the area of 250 km2. DEM SRTM with 30 m spatial resolution was used to reduce topography effect contribution in the interferograms. Based on the SBAS method, interferogram pair selection is applied with the maximum value of 150 m for the perpendicular baseline and 100 days for the temporal baseline. The DInSAR process successfully generated seventy-four (74) interferogram pairs, and then by using SBAS algorithm, the LOS velocity and time-series LOS displacement were estimated. The results showed the varying displacements respond with the significant subsidence occurred in the North, East, and Southeast part of Surabaya. The SBAS result confirmed that the LOS velocity over the Surabaya area between May 2015 and September 2017 ranges from -40 to +30 mm/year. Significant land subsidence occurred in the Asemrowo area (North Surabaya) with the value of LOS displacement velocity up to -45 mm/yr.

Theory and Statistical Description of the Enhanced Multi-Temporal InSAR (E-MTInSAR) Noise-Filtering Algorithm

In this work, the statistical fundaments of the recently proposed enhanced, multi-temporal interferometric synthetic aperture radar (InSAR) noise-filtering (E-MTInSAR) technique is addressed. The adopted noise-filtering algorithm is incorporated into the improved extended Minimum Cost Flow (EMCF) Small Baseline Subset (SBAS) differential interferometric SAR (InSAR) processing chain, which has extensively been used for the generation of Earth’s surface displacement time-series in several different contexts. Originally, the input of the InSAR EMCF-SBAS processing toolbox consisted of a sequence of multi-looked, small baseline interferograms, which were unwrapped using the space-time EMCF phase unwrapping algorithm. Subsequently, the unwrapped interferograms were inverted through the SBAS algorithm to retrieve the expected InSAR deformation products. The improved processing chain has complemented the original codes with two additional steps. In particular, a new multi-temporal noise-filtering algorithm for sequences of time-redundant multi-looked DInSAR interferograms, followed by a proper interferogram selection step, has been proposed. This research study is aimed at primarily assessing the performance of the E-MTInSAR noise-filtering algorithm from a theoretical perspective. To this aim, the principles of directional statistics and errors propagation are exploited. Experimental results, carried out by applying the E-MTInSAR algorithm to a sequence of SAR data collected over the Los Angeles bay area, have been used to corroborate the academic outcome of this research.

A New Nonlocal Method for Ground-Based Synthetic Aperture Radar Deformation Monitoring

Ground-based synthetic aperture radar (GBSAR) interferometry offers an effective solution for the monitoring of surface displacements with high precision. However, coherence estimation and phase filtering in GBSAR interferometry is often based on a rectangular window, resulting in estimation biases and resolution loss. To address these issues, conventional nonlocal methods developed for spaceborne synthetic aperture radar are investigated with GBSAR data for the first time. Based on investigation and analysis, an efficient similarity measure is proposed to identify pixels with similar amplitude behaviors and a comprehensive nonlocal method is presented on the basis of this concept with the aim of overcoming current limitations. Pixels with high similarity are identified from a large search window for each point based on a stack of GBSAR single look complex images. Coherence is calculated based on the selected sibling pixels and then enhanced by the second kind statistic estimator. Nonlocal means filtering is also performed based on the sibling pixels to reduce interferometric phase noise. Experiments were conducted using short- and long-term GBSAR interferograms. Qualitative and quantitative analyses of the proposed nonlocal method and other existing techniques demonstrate that the new approach has advantages in terms of coherence estimation and phase filtering capability. The proposed method was integrated into a complete GBSAR small baseline subset algorithm and a time series analysis was achieved for two stacks of data sets. Considered alongside experimental results, this successful application demonstrates the feasibility of the proposed nonlocal method to facilitate the adoption of GBSAR for deformation monitoring applications.

Ground deformation associated with exploitation of deep groundwater in Cangzhou City measured by multi-sensor synthetic aperture radar images

The magnitude and severity of ground deformation over time were investigated in Cangzhou City, Hebei Province, China. A total of 17 descending orbit C-band Envisat and 22 ascending orbit L-band Advanced Land Observing Satellite synthetic aperture radar (SAR) images covering the study area between the beginning of 2007 and the end of 2010 were integrated using an enhanced small baseline subset algorithm. The annual rates of ground deformation were retrieved from combined ASAR and PALSAR interferograms in our study area where the average subsidence reached 19 mm/a and subsidence rates up to 51 mm/a were observed. Subsidence in Cangzhou is mainly caused by the excessive abstraction of groundwater from a confined aquifer and consequently the ground subsides evenly over time. A multi-sensor SAR imaging technique was used to survey the extent and severity of ground deformation associated with the exploitation of deep groundwater in Cangzhou City. With abundant observations from different wavebands, the highly accurate ground subsidence result is not only sensitive to minor ground displacement but maintains fine coherence. Additionally, the temporal resolution is improved, and the sampling period is decreased to 5 days.

Deformation Parameter Estimation in Low Coherence Areas Using a Multisatellite InSAR Approach

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.

A Distributed Scatterer Interferometry Approach for Precision Monitoring of Known Surface Deformation Phenomena

This paper presents a new technique for mapping mean deformation velocity in highly decorrelated areas with known deformation patterns, exploiting high-resolution synthetic aperture radar (SAR) data. The implemented method is based on distributed scatterers and first makes use of the Anderson-Darling (AD) statistical test to identify homogenous patches of pixels based on SAR amplitude images. Then, a robust object adaptive parameter estimation is performed to estimate the local gradients of deformation velocity and the local gradients of residual DEM in range and azimuth directions for these patches, utilizing small baseline differential interferograms. Finally, the information obtained from different patches is connected to get the deformation velocity, via a 2-D model-based deformation integration using Bayesian inference. Compared with published multitemporal interferometric work, the main advantage of the newly developed algorithm is that it does not require any phase unwrapping, and because of this, the method is largely insensitive to decorrelation phenomenon occurring in natural terrains and the availability of persistent scatterers (PSs), in contrast to the coherent stacking techniques such as PS interferometry, small baseline subset algorithm, and SqueeSAR. The method is computationally inexpensive with respect to SqueeSAR as only the small baseline interferograms are used for the processing. The method provides spatially dense deformation velocity maps at a suitable object resolution, as compared with a few measured points provided by the stacking techniques in difficult decorrelated regions. High Resolution Spotlight TerraSAR-X data set of Lueneburg in Germany is used as a processing example of this technique.

Fusion of Monostatic/Bistatic InSAR Stacks for Urban Area Analysis via Distributed Scatterers

Interferometric synthetic aperture radar (SAR) is a powerful technique providing meter-precision elevation maps and millimeter-precision surface displacement maps. Since 2007, the high-resolution SAR satellite TerraSAR-X allows monitoring of even single buildings from space using advanced monostatic repeat-pass stacking techniques. Furthermore, the launch of its twin satellite TanDEM-X in 2010 facilitates bistatic single-pass SAR interferometry. The main objective of this mission is the generation of a global digital elevation model. It also provides a configurable SAR platform for demonstrating new interferometric techniques and applications. However, in dense urban areas, standard TanDEM-X elevation models are inaccurate because ambiguities in radar layover areas cannot be solved. This letter describes the potential of joint monostatic and bistatic (motion-free and atmosphere-free) SAR interferometric stacking for an improved scene elevation and surface deformation estimation in complex urban areas. It involves exploiting distributed scatterers (DSs) using an advanced high-resolution small-baseline subset algorithm. Since most of the scatterers within a radar image can be classified as DSs, there is an increasing focus on an optimal processing of DSs for urban area monitoring. The fusion technique and an application test case are presented using a high-resolution spotlight mixed TerraSAR-X/TanDEM-X data stack of Las Vegas, USA.

ADAPTIVE SPATIAL FILTERING OF INTERFEROMETRIC DATA STACK ORIENTED TO DISTRIBUTED SCATTERERS

Abstract. Standard interferometry poses a challenge in non-urban areas due to temporal and spatial decorrelation of the radar signal, where there is high signal noise. Techniques such as Small Baseline Subset Algorithm (SBAS) have been proposed to make use of multiple interferometric combinations to alleviate the problem. However, the interferograms used in SBAS are multilooked with a boxcar (rectangle) filter to reduce phase noise, resulting in a loss of resolution and signal superstition from different objects. In this paper, we proposed a modified adaptive spatial filtering algorithm for accurate estimation of interferogram and coherence without resolution loss even in rural areas, to better support the deformation monitoring with time series interferometric synthetic aperture radar (InSAR) technique. The implemented method identifies the statistically homogenous pixels in a neighbourhood based on the goodness-of-fit test, and then applies an adaptive spatial filtering of interferograms. Three statistical tests for the identification of distributed targets will be presented, applied to real data. PALSAR data of the yellow river delta in China is used for demonstrating the effectiveness of this algorithm in rural areas.

HIGH RESOLUTION DEFORMATION TIME SERIES ESTIMATION FOR DISTRIBUTED SCATTERERS USING TERRASAR-X DATA

Abstract. In recent years, several SAR satellites such as TerraSAR-X, COSMO-SkyMed and Radarsat-2 have been launched. These satellites provide high resolution data suitable for sophisticated interferometric applications. With shorter repeat cycles, smaller orbital tubes and higher bandwidth of the satellites; deformation time series analysis of distributed scatterers (DSs) is now supported by a practical data basis. Techniques for exploiting DSs in non-urban (rural) areas include the Small Baseline Subset Algorithm (SBAS). However, it involves spatial phase unwrapping, and phase unwrapping errors are typically encountered in rural areas and are difficult to detect. In addition, the SBAS technique involves a rectangular multilooking of the differential interferograms to reduce phase noise, resulting in a loss of resolution and superposition of different objects on ground. In this paper, we introduce a new approach for deformation monitoring with a focus on DSs, wherein, there is no need to unwrap the differential interferograms and the deformation is mapped at object resolution. It is based on a robust object adaptive parameter estimation using single look differential interferograms, where, the local tilts of deformation velocity and local slopes of residual DEM in range and azimuth directions are estimated. We present here the technical details and a processing example of this newly developed algorithm.

An advanced algorithm for deformation estimation in non-urban areas

This paper presents an advanced differential SAR interferometry stacking algorithm for high resolution deformation monitoring in non-urban areas with a focus on distributed scatterers (DSs). Techniques such as the Small Baseline Subset Algorithm (SBAS) have been proposed for processing DSs. SBAS makes use of small baseline differential interferogram subsets. Singular value decomposition (SVD), i.e. L2 norm minimization is applied to link independent subsets separated by large baselines. However, the interferograms used in SBAS are multilooked using a rectangular window to reduce phase noise caused for instance by temporal decorrelation, resulting in a loss of resolution and the superposition of topography and deformation signals from different objects. Moreover, these have to be individually phase unwrapped and this can be especially difficult in natural terrains. An improved deformation estimation technique is presented here which exploits high resolution SAR data and is suitable for rural areas. The implemented method makes use of small baseline differential interferograms and incorporates an object adaptive spatial phase filtering and residual topography removal for an accurate phase and coherence estimation, while preserving the high resolution provided by modern satellites. This is followed by retrieval of deformation via the SBAS approach, wherein, the phase inversion is performed using an L1 norm minimization which is more robust to the typical phase unwrapping errors encountered in non-urban areas. Meter resolution TerraSAR-X data of an underground gas storage reservoir in Germany is used for demonstrating the effectiveness of this newly developed technique in rural areas.

The Stripmap–ScanSAR SBAS Approach to Fill Gaps in Stripmap Deformation Time Series With ScanSAR Data

We present a simple approach to jointly exploit stripmap and ScanSAR acquisitions to generate differential synthetic aperture radar interferometry (DInSAR) time series. In particular, we extend the capability of the Small BAseline Subset (SBAS) approach to compute deformation time series from a set of stripmap images by filling possible temporal gaps in the available SAR data sequence with ScanSAR acquisitions. The starting point of our approach is the raw data focusing step, which is properly carried out to align the characteristics of the ScanSAR images to those of the stripmap ones. To achieve this task, we exploit stripmap processing codes to focus both SAR data types, the ScanSAR ones being processed on a burst-by-burst basis, accounting also for possible differences of the pulse repetition frequency with respect to that of the stripmap data. The coherent combination of the focused bursts generates phase-preserved ScanSAR images with the same output geometry and pixel spacing as the stripmap ones. This allows a straightforward implementation of the next steps of the SBAS processing chain, including the interferogram generation operation. In this case, we concentrate on a selection of small baseline (SB) stripmap-stripmap multilook interferograms identified through a Delaunay triangulation, which are complemented with a set of hybrid SB stripmap-ScanSAR interferograms. This interferogram selection permits us to develop an effective phase unwrapping algorithm based on a two-step processing strategy. Finally, the whole data set of unwrapped interferograms is inverted through the SBAS technique to retrieve the final deformation time series, including both stripmap and ScanSAR data. The proposed stripmap-ScanSAR SBAS process ing approach is particularly attractive because it is very easy to implement since it requires only limited modifications with respect to the conventional stripmap-based SBAS algorithm. Our approach has been applied to descending and ascending hybrid stripmap-ScanSAR data sets of Envisat/ASAR C-band acquisitions from the Big Island of Hawaii. In spite of not including any common-band azimuthal filtering, which would account for the ScanSAR burst spectral properties at the expense of the algorithm simplicity, the presented results show that we may retrieve DInSAR time series with an accuracy ranging between 5 and 10 mm, consistent with previous C-band data analyses using only stripmap data.

Topographic Correction for ALOS PALSAR Interferometry

L-band synthetic aperture radar (SAR) interferometry is very successful for mapping ground deformation in densely vegetated regions. However, due to its larger wavelength, the capacity to detect slow deformation over a short period of time is limited. Stacking and small baseline subset (SBAS) techniques are routinely used to produce time series of deformation and average deformation rates by reducing the contribution of topographic and atmospheric noise. For large sets of images that are presently available from C-band European Remote Sensing Satellites (ERS-1/2) and Environmental Satellite (ENVISAT), the standard stacking and SBAS algorithms are accurate. However, the same algorithms are often inaccurate when used for processing of interferograms from L-band Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS PALSAR). This happens because only a limited number of interferograms is acquired and also because of large spatial baselines often correlated with the time of acquisition. In this paper two techniques are suggested that can be used for removing the residual topographic component from stacking and SBAS results, thereby increasing their accuracy.

Time series analysis of subsidence at Tauhara and Ohaaki geothermal fields, New Zealand, observed by ALOS PALSAR interferometry during 2007–2009

We present a modification of the Small Baseline Subset (SBAS) technique and apply it to L-band ALOS PALSAR data for time series analysis of subsidence at Tauhara and Ohaaki geothermal fields in the Taupo Volcanic Zone, North Island, New Zealand. We noticed that residual topographic noise presented in most ALOS PALSAR interferograms that have been acquired with large perpendicular baselines is a significant limiting factor that complicates their interpretation. We modified the original SBAS technique in such a way that the residual topographic contribution is accurately estimated and removed. For testing purposes we acquired 12 ALOS PALSAR images between 13 January 2007 and 18 January 2009 and created 33 differential interferograms for use in SBAS processing. The calculated time series of deformation at both geothermal fields show steady subsidence with rates of 5–6 cm/year that is clearly mapped with the proposed technique. A comparison of results calculated with the proposed technique and with the original SBAS algorithm shows that the proposed approach is more accurate and produces results of higher quality. The technique presented in this paper can be used for time series analysis of any SAR data; however, it is most appropriate for L-band ALOS PALSAR that acquires coherent images with large spatial baselines that also correlate with the time of acquisition.