SAR Time Series Research Articles

Interferometric SAR Time Series Analysis for Ground Subsidence of the Abandoned Mining Area in North Peixian Using Sentinel-1A TOPS Data

The North Peixian mining area of China has rich coal resources, with total proven reserves of 2.37 billion tons. However, the underground coal mining activities have resulted in ground collapse, which has caused serious harm to the environment and threatened the lives and properties of local residents. In this study, 12 Sentinel-1A terrain observation by progressive scans (TOPS) mode acquisitions between 30 July 2015 and 13 May 2016 over the abandoned mining area in North Peixian were analyzed using the interferometric synthetic aperture radar (InSAR) time series method to detect the ground subsidence, with the maximum ground subsidence reaching 83 mm/a and an average value of about 12.7 mm/a. The subsidence results derived from the Sentinel-1A TOPS mode dataset were proven to be effective in investigating and monitoring the ground subsidence in the North Peixian mining area. Compared to the rapid deformation during the ongoing period of mining excavation, the ground subsides slowly in abandoned mining areas and shows a linear relationship with time over a relatively long period of time. Spatial correlation between the subsidence distribution and land cover was found, in that the magnitude of the subsidence in urban areas was smaller than that in rural areas, which is associated with the controlled coal mining activities under buildings, railways, and water bodies. The results demonstrate that Sentinel-1A TOPS SAR images can be used to effectively and accurately detect and monitor ground subsidence in a mining area, which is critically important when investigating land subsidence in a large-scale mining area.

Monitoring of post-fire forest regeneration under different restoration treatments based on ALOS/PALSAR data

Post-fire forest regeneration is crucial to forest management. Three different restoration treatments including natural regeneration (NR), artificial regeneration (AR), and artificial promotion (AP), were adopted in the Greater Hinggan Mountain area of China after a serious fire occurred on May 6, 1987. NR is a control treatment where recovery occurs naturally without intervention, AR comprises salvage logging followed by planting, while AP includes regeneration by removing dead trees, weeding, and tidying to promote seed germination. In this study, the objective was to detect and compare the effects of the three restoration treatments using radar indices derived from ALOS/PALSAR data. Four time-series SAR images were pre-processed to acquire the backscattering coefficients. Then the coefficients in both HH and HV polarization were examined and two radar vegetation indices were derived and evaluated, based on which, the post-fire forest dynamics under different restoration treatments were detected and compared. The results showed that the forests under NR presented a completely different recovery trajectory compared to those under the other two treatments. This difference could be characterized by both the backscattering intensity in HH and HV polarization and two radar indices. This study indicated the effects of different restoration treatments, as well as demonstrated the applicability and efficiency of radar remote sensing techniques in forest monitoring and management.

Practical Considerations before Installing Ground-Based Geodetic Infrastructure for Integrated InSAR and cGNSS Monitoring of Vertical Land Motion.

Continuously operating Global Navigation Satellite Systems (cGNSS) can be used to convert relative values of vertical land motion (VLM) derived from Interferometric Synthetic Aperture Radar (InSAR) to absolute values in a global or regional reference frame. Artificial trihedral corner reflectors (CRs) provide high-intensity and temporally stable reflections in SAR time series imagery, more so than naturally occurring permanent scatterers. Therefore, it is logical to co-locate CRs with cGNSS as ground-based geodetic infrastructure for the integrated monitoring of VLM. We describe the practical considerations for such co-locations using four case-study examples from Perth, Australia. After basic initial considerations such as land access, sky visibility and security, temporary test deployments of co-located CRs with cGNSS should be analysed together to determine site suitability. Signal to clutter ratios from SAR imagery are used to determine potential sites for placement of the CR. A significant concern is whether the co-location of a deliberately designed reflecting object generates unwanted multipath (reflected signals) in the cGNSS data. To mitigate against this, we located CRs >30 m from the cGNSS with no inter-visibility. Daily RMS values of the zero-difference ionosphere-free carrier-phase residuals, and ellipsoidal heights from static precise point positioning GNSS processing at each co-located site were then used to ascertain that the CR did not generate unwanted cGNSS multipath. These steps form a set of recommendations for the installation of such geodetic ground-infrastructure, which may be of use to others wishing to establish integrated InSAR-cGNSS monitoring of VLM elsewhere.

SENTINEL-1 DATA TO MAP FLOODED AREAS: THE ROLE OF INSAR COHERENCE AND POLARIMETRIC INFORMATION

SAR earth observation data can provide high quality flood maps and information to better assess the flood risk accordingly planning as well as to support civil protection authorities during emergency phase. The scope of this paper is to create flood extent maps from a series of SAR scenes of the Evros basin which represents a transboundary floodplain. The study uses time series SAR images of Sentnel-1 ESA’s Copernicus satellite system covering the period October 2014 to May 2015. The methodology tries to identify the flood that occurs in three main land cover classes, such as urban areas, bare or poorly vegetated soil and vegetated areas, taking advantage of co- and cross-polarized SAR backscattering channels, and the InSAR coherence to better characterize the landscape. Dual-pol SAR data provides the opportunity to have a better understanding and interpretation of flood detection due to way different land cover react to different polarizations. Thus, with the implementation of InSAR coherence estimation we may achieve a better record and knowledge of the flooded areas, over time, in the specific region.

Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy

Differential Interferometric SAR (DInSAR) time-series techniques can be used to derive surface displacement rates with accuracies of 1 mm/year, by measuring the one-dimensional distance change between a satellite and the surface over time. However, the slanted direction of the measurements complicates interpretation of the signal, especially in regions that are subject to multiple deformation processes. The Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements (SISTEM) algorithm enables decomposition into a three-dimensional velocity field through joint inversion with GNSS measurements, but has never been applied to interseismic deformation where strain rates are low. Here, we apply SISTEM for the first time to detect tectonic deformation on the Hyblean Foreland Plateau in South-East Sicily. In order to increase the signal-to-noise ratio of the DInSAR data beforehand, we reduce atmospheric InSAR noise using a weather model and combine it with a multi-directional spatial filtering technique. The resultant three-dimensional velocity field allows identification of anthropogenic, as well as tectonic deformation, with sub-centimeter accuracies in areas of sufficient GPS coverage. Our enhanced method allows for a more detailed view of ongoing deformation processes as compared to the single use of either GNSS or DInSAR only and thus is suited to improve assessments of regional seismic hazard.

A modified vegetation backscattering model for leaf area index retrieval from SAR time series

ABSTRACTIn this study, a semi-empirical modified vegetation backscattering model was developed to retrieve leaf area index (LAI) based on multi-temporal Radarsat-2 data and ground observations collected in China. This model combined the contribution of the vegetation and bare soil at the pixel level by adding vegetation coverage and the influence of bare soil on the total backscatter coefficients. Then, a lookup table algorithm was applied to calculate the value of vegetation water content and retrieve the LAI based on the linear relationship between the vegetation water content and LAI. The results indicated that the modified model was effective in evaluating and reproducing the total backscatter coefficients. Meanwhile, the LAI retrieval was well conducted with coefficient of determination (R2) and root mean square error (RMSE) of 89% and 0.19 m2 m−2, respectively. Additionally, this method offers insight into the required application accuracy of LAI retrieval in the agricultural regions.

Detecting seasonal landslide movement within the Cascade landslide complex (Washington) using time-series SAR imagery

Abstract Detection of slow or limited landslide movement within broad areas of forested terrain has long been problematic, particularly for the Cascade landslide complex (Washington) located along the Columbia River Gorge. Although parts of the landslide complex have been found reactivated in recent years, the timing and magnitude of motion have not been systematically monitored or interpreted. Here we apply novel time-series strategies to study the spatial distribution and temporal behavior of the landslide movement between 2007 and 2011 using InSAR images from two overlapping L-band ALOS PALSAR-1 satellite tracks. Our results show that the reactivated part has moved approximately 700 mm downslope during the 4-year observation period, while other parts of the landslide complex have generally remained stable. However, we also detect about 300 mm of seasonal downslope creep in a terrain block upslope of the Cascade landslide complex—terrain previously thought to be stable. The temporal oscillation of the seasonal movement can be correlated with precipitation, implying that seasonal movement here is hydrology-driven. The seasonal movement also has a frequency similar to GPS-derived regional ground oscillations due to mass loading by stored rainfall and subsequent rebound but with much smaller magnitude, suggesting different hydrological loading effects. From the time-series amplitude information on terrain upslope of the headscarp, we also re-evaluate the incipient motion related to the 2008 Greenleaf Basin rock avalanche, not previously recognized by traditional SAR/InSAR methods. The approach used in this study can be used to identify active landslides in forested terrain, to track the seasonal movement of landslides, and to identify previously unknown landslide hazards.

Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis

Recent improvement to Advanced Differential Interferometric SAR (A-DInSAR) time series quality enhances the knowledge of various geohazards. Ground motion studies need an appropriate methodology to exploit the great potential contained in the A-DInSAR time series. Here, we propose a methodology to analyze multi-sensors and multi-temporal A-DInSAR data for the geological interpretation of areas affected by land subsidence/uplift and seasonal movements. The methodology was applied in the plain area of the Oltrepo Pavese (Po Plain, Italy) using ERS-1/2 and Radarsat data, processed using the SqueeSAR™ algorithm, and covering time spans, respectively, from 1992 to 2000 and from 2003 to 2010. The test area is a representative site of the Po Plain, affected by various geohazards and characterized by moderate rates of motion, ranging from −10 to 4 mm/yr. Different components of motion were recognized: linear, non-linear, and seasonal deformational behaviors. Natural and man-induced processes were identified such as swelling/shrinkage of clayey soils, land subsidence due to load of new buildings, moderate tectonic uplift, and seasonal ground motion due to seasonal groundwater level variations.

Subsidence Monitoring over the Southern Coalfield, Australia Using both L-Band and C-Band SAR Time Series Analysis

Land subsidence is a global issue and researchers from all over the world are keen to know the causes of deformation and its further influences. This paper reports the findings from time series InSAR (TS-InSAR) results over the Southern Coalfield, Australia using both ALOS-1 PALSAR (Phased Array type L-band Synthetic Aperture Radar) and ENVISAT ASAR (Advanced Synthetic Aperture Radar) datasets. TS-InSAR has been applied to both rural and urban areas with great success, but very few of them have been applied to regions affected by underground mining activities. The TS-InSAR analysis exploited in this paper is based on GEOS-ATSA, and Measurement Point (MP) pixels are selected according to different geophysical features. Three experiment sites with different geological settings within the study zone are analysed: (1) Wollongong city, which is a relatively stable area; (2) Tahmoor town, a small town affected by underground mining activities; and (3) the Appin underground mining site, a region containing multiple underground mining activities. The TS-InSAR results show that the performance of both C-band and L-band is equally good over Wollongong, where the subsidence gradient is not significant and most subsidence rates are between −10 mm∙yr−1 to 10 mm∙yr−1. However, over the Tahmoor and Appin sites, difference in performances has been observed. Since the maximum displacement gradients that can be detected are different for L-band and C-band-based TS-InSAR methods, some rapid changes could cause the TS-InSAR to fail to estimate the correct displacements. It is well known that L-band can perform better than C-band, especially in underground mining regions and mining-affected regions where the deformation rate is much higher than city areas because of its wavelength. Statistical analyses are also conducted to further prove the above statement.

Atmospheric correction in time-series SAR interferometry for land surface deformation mapping – A case study of Taiyuan, China

The dominant error source of Synthetic Aperture Radar Interferometry (InSAR) is atmospheric phase screen (APS), resulting in phase delay of the radar signal propagating through the atmosphere. The APS in the atmosphere can be decomposed into stratified and turbulent components. In this paper, we introduced a method to compensate for stratified component in a radar interferogram using ERA-Interim reanalysis products obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). Our comparative results with radiosonde data demonstrated that atmospheric condition from ERA-Interim could produce reasonable patterns of vertical profiles of atmospheric states. The stratified atmosphere shows seasonal changes which are correlated with time. It cannot be properly estimated by temporal high-pass filtering which assumes that atmospheric effects are random in time in conventional persistent scatterer InSAR (PSI). Thus, the estimated deformation velocity fields are biased. Therefore, we propose the atmosphere-corrected PSI method that the stratified delay are corrected on each interferogram by using ERA-Interim. The atmospheric residuals after correction of stratified delay were interpreted as random variations in space and time which are mitigated by using spatial–temporal filtering. We applied the proposed method to ENVISAT ASAR images covering Taiyuan basin, China, to study the ground deformation associated with groundwater withdrawal. Experimental results show that the proposed method significantly mitigate the topography-correlated APS and the estimated ground displacements agree more closely with GPS measurements than the conventional PSI.

Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series

High temporal and spatial resolution optical image time series have been proven efficient for crop type mapping at the end of the agricultural season. However, due to cloud cover and image availability, crop identification earlier in the season is difficult. The recent availability of high temporal and spatial resolution SAR image time series, opens the possibility of improving early crop type mapping. This paper studies the impact of such SAR image time series when used in complement of optical imagery. The pertinent SAR image features, the optimal working resolution, the effect of speckle filtering and the use of temporal gap-filling of the optical image time series are assessed. SAR image time series as those provided by the Sentinel-1 satellites allow significant improvements in terms of land cover classification, both in terms of accuracy at the end of the season and for early crop identification. Haralik textures (Entropy, Inertia), the polarization ratio and the local mean together with the VV imagery were found to be the most pertinent features. Working at at 10 m resolution and using speckle filtering yield better results than other configurations. Finally it was shown that the use of SAR imagery allows to use optical data without gap-filling yielding results which are equivalent to the use of gap-filling in the case of perfect cloud screening, and better results in the case of cloud screening errors.

Feasibility Study of C- and L-band SAR Time Series Data in Tracking Indonesian Plantation and Natural Forest Cover Changes

Tropical coverage by Envisat ASAR is sparse in space and time and has limited value for monitoring deforestation. The only available dual-polarized multitemporal dataset over Riau province, Indonesia (nine images in a single year), is used to distinguish and monitor tropical plantations and their dynamics and is compared with annual L-band PALSAR data and land cover maps derived from Landsat data. For the ASAR data, both VV and VH are important in discriminating different types of forest cover; whereas, at L-band, most of the relevant information is in the cross-polarized channel. The ASAR VV (but not the VH) backscatter from acacia plantations is strongly affected by whether the underlying soil is peat or nonpeat, which affects the separability of acacia from oil palm. Maximum likelihood classification of the C-band data gave overall accuracies of 86.2% and kappa coefficient of 0.78 by comparison with land cover maps derived from optical data. This was not improved by combining C- and L-band data. Classification of the C-band time series allows the rotation cycle of acacia plantations to be tracked. The available 4-year annual L-band time series shows potential for monitoring these dynamics, but the 1-year time spacing increases the risk of missing changes masked by the rapid growth of acacia.

TerraSAR-X dual-pol time-series for mapping of wetland vegetation

Mapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using Support Vector Machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restoration) and biodiversity studies using remote sensing data.

New evidence for active tectonics at the boundary of the Kashi Depression, China, from time series InSAR observations

Kashi Depression is one of the most complex active tectonic areas in the southern flank of Tianshan, China. Due to the lack of ground observations, the boundary of basin mountain transition zone and the interseismic activity of the Tianshan have not been clearly determined. In this study, 48 Envisat Advanced Synthetic Aperture Radar (ASAR) imagery acquired from 2003 to 2010 are used to construct interferograms for measuring high-resolution interseismic deformation in the Kashi Depression area. A global atmospheric model ERA-Interim provided by the European Center for Medium Range Weather Forecast (ECMWF) and a global network orbital correction are applied to remove atmospheric effect, and the long-wavelength orbital errors, respectively, for the interferograms. Interferometric SAR time series with Atmospheric Estimation Model (InSAR TS+AEM) are then used to obtain a deformation rate map for the Kashi Depression area. The InSAR rate map indicates that the north part of South Atushi Fault has ~3mm/year uplift relative to that of the south part. This result manifests the main tectonic deformation potentially occurs along the Southern Atushi Fault. Based on a simple edge dislocation model, the dip angle of 31±0.6°, slip rate of 2.3±0.1mm/year, and locking depth of 10.6±0.4km for the Southern Atushi Fault between Tianshan Orogenic Belt and the Kashi Depression are obtained. This modeling result shows in good agreement with the InSAR derived rates. Our results show that the Southern Atushi Fault is the main active fault in block boundary region between the south of Tianshan and the Tarim Basin.

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

Fusing Landsat and SAR time series to detect deforestation in the tropics

Fusion of optical and SAR time series imagery has the potential to improve forest monitoring in tropical regions, where cloud cover limits optical satellite time series observations. We present a novel pixel-based Multi-sensor Time-series correlation and Fusion approach (MulTiFuse) that exploits the full observation density of optical and SAR time series. We model the relationship of two overlapping univariate time series using an optimized weighted correlation. The resulting optimized regression model is used to predict and fuse two time series. Using the MulTiFuse approach we fused Landsat NDVI and ALOS PALSAR L-band backscatter time series. We subsequently used the fused time series in a multi-sensor change detection framework to detect deforestation between 01/2008 - 09/2010 at a managed forest plantation in the tropics (Pinus caribea; 2859ha). 3-monthly reference data covering the entire study area was used to validate and assess spatial and temporal accuracy. We tested the impact of persistent cloud cover by increasing the per-pixel missing data percentage of the NDVI time series stepwise from ~53% (~6 observations/year) up to 95% (~0.5 observation/year) while fusing with a consistent PALSAR time series of ~2 observations/year. A significant linear correlation was found between the Landsat NDVI and ALOS PALSAR L-band SAR time series observables for logged forest. The multi-temporal filtered PALSAR HVHH backscatter ratio time series (HVHHmt) was most strongly correlated with the NDVI time series. While for Landsat-only the spatial and temporal accuracy of detected deforestation decreased significantly with increasing missing data, the accuracies for the fused NDVI-PALSAR case remained high and were observed to be above the NDVI- and PALSAR-only cases for all missing data percentages. For the fused NDVI-HVHHmt time series the overall accuracy was 95.5% with a 1.59month mean time lag of detected changes. The MulTiFuse approach is robust and automated, and it provides the opportunity to use the upcoming data streams of free-of charge, medium resolution optical and SAR satellite imagery in a beneficial way for improved tropical forest monitoring.

Subsidence Monitoring of Tianjin Suburbs by TerraSAR-X Persistent Scatterers Interferometry

This paper illustrates the first wide area study over Tianjin suburbs of China using persistent scatterers interferometry (PSI) technique for ground subsidence monitoring via high-resolution TerraSAR-X (TSX) SAR data. The deformation rate and the displacement history of the subsiding areas from April 29, 2009 to November 11, 2011 have been reconstructed. The results demonstrate the capability of applying PSI technique to high-resolution SAR time series imageries for monitoring of the subsidence of multiple towns and large-scale man-made linear features (LMLFs) such as railways, highways, and power lines. The comprehensive uncertainties were analyzed between PSI results and ground-leveling data whose densities are very different in both spatial and temporal domains. The overall results show a good agreement with each other. We also found that the underground water extraction and lithological character are the two important potential explanations to the location and shape of the subsiding centers.

Interseismic strain accumulation across the North Tabriz Fault (NW Iran) deduced from InSAR time series

We present the surface deformation along the North Tabriz Fault (NTF) deduced from Synthetic Aperture Radar Interferometry (InSAR) technique. The NTF, a major right-lateral strike-slip fault within the active Arabia–Eurasia collision zone, is located 40–45km southwest of the Mw 6.5 and Mw 6.3, August 11, 2012 earthquake sequence that caused heavy damage and more than 300 deaths in Ahar, NW Iran. InSAR time series analysis of 17 ENVISAT radar images acquired between 2004 and 2010 using combination of the permanent scatterers InSAR (PSI) and the small baseline InSAR (SBAS) approach reveals sub-centimeter interseismic strain accumulation across the NTF and rapid subsidence in the Tabriz basin. Elastic dislocation modeling of the mean line-of-sight velocity field estimated from SBAS time series yields an average slip rate of 8.7±2.5mm/year with a locking depth of 15.8±10.8km. This rate is consistent with previous geodetic estimates based on recent Global Positioning System measurements, and suggests a recurrence interval of 250–300 years for major earthquakes of Mw 7.0–7.3 on the NTF, much shorter than those estimated from paleoseismic investigations (821±176 years). This in turn implies a high seismic potential on the NTF taking into account the occurrence of the two last earthquakes on the NTF in 1721 and 1780. SAR time series analysis also reveals three regions of rapid subsidence with a maximum rate of 20mm/year near the Tabriz thermal power plant in the Tabriz basin. Piezometric data from groundwater wells suggest that accelerated subsidence over the last several years may result from fluctuations in the ground water table.

Postseismic motion after the 2001 MW 7.8 Kokoxili earthquake in Tibet observed by InSAR time series

On November 14th 2001, a Mw 7.8 earthquake occurred in the Kokoxili region of northern Tibet. The earthquake ruptured more than 400 km along the western part of the Kunlun fault with a maximum of 8 m left‐lateral slip. In this paper, we use a multitemporal Interferometric SAR (InSAR) time series technique to map the postseismic motion following the large Kokoxili event. SAR data from Envisat descending orbits along five adjacent tracks covering almost the entire ruptured fault length are used to calculate the displacement time series for a period between 2 and 6 years after the earthquake. A peak‐to‐trough signal of 8 cm in the radar line of sight is observed during the period between 2003 and 2008. Two different mechanisms are employed to explain the observed surface displacements, namely afterslip and viscoelastic relaxation. The observations inverted for afterslip on and below the coseismic rupture plane shows that the maximum slip in the afterslip model is 0.6 m. The position of the maximum postseismic slip is located in the middle of two relatively high coseismic slip patches, which suggests that afterslip is a plausible mechanism. Models of viscoelastic stress relaxation in a Maxwell half‐space give a best fitting viscosity for the mid‐to‐lower crust of 2–5 × 1019 Pa s, and the principal postseismic relaxation process is due to viscous flow in the lower crust to upper mantle. However, the InSAR observations are incapable of distinguishing between localized (afterslip) and distributed (viscoelastic relaxation) deformation. And the lowest misfits are produced by mixed models of viscoelastic relaxation in the mantle below 70 km and afterslip in the crust. Modeling of viscoelastic relaxation in a Maxwell half‐space, and also a mixed mechanism model, enables us to place an effective viscosity of 2 × 1019 Pa s on the lower crust to mantle of northern Tibet.