C-band Interferometric Synthetic Aperture Radar Research Articles

Potential of APSIS-InSAR for measuring surface oscillations of tropical peatlands.

Tropical peatland across Southeast Asia is drained extensively for production of pulpwood, palm oil and other food crops. Associated increases in peat decomposition have led to widespread subsidence, deterioration of peat condition and CO2 emissions. However, quantification of subsidence and peat condition from these processes is challenging due to the scale and inaccessibility of dense tropical peat swamp forests. The development of satellite interferometric synthetic aperture radar (InSAR) has the potential to solve this problem. The Advanced Pixel System using Intermittent Baseline Subset (APSIS, formerly ISBAS) modelling technique provides improved coverage across almost all land surfaces irrespective of ground cover, enabling derivation of a time series of tropical peatland surface oscillations across whole catchments. This study aimed to establish the extent to which APSIS-InSAR can monitor seasonal patterns of tropical peat surface oscillations at North Selangor Peat Swamp Forest, Peninsular Malaysia. Results showed that C-band SAR could penetrate the forest canopy over tropical peat swamp forests intermittently and was applicable to a range of land covers. Therefore the APSIS technique has the potential for monitoring peat surface oscillations under tropical forest canopy using regularly acquired C-band Sentinel-1 InSAR data, enabling continuous monitoring of tropical peatland surface motion at a spatial resolution of 20 m.

Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses.

Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are −10 and −98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz–Shabestar and Tabriz–Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are −13 and −120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).

Characterizing marsh wetlands in the Great Lakes Basin with C-band InSAR observations

There is limited research focusing on Interferometric Synthetic Aperture Radar (InSAR) applications in the Great Lakes coastal wetlands with large water level fluctuations. In this study, we investigated the potential of using C-band SAR data to characterize marsh wetland and monitor water level changes along the coast of the Great Lakes. InSAR analysis was conducted using Radarsat-2 and Sentinel-1 data collected at Long Point, Ontario, Canada over the period of 2016–2018. Observations indicated that both backscattering coefficients and coherence from tall plants (e.g. cattail/Phragmites), short plants (e.g. grass), and water varied with different sensor modes (incidence angles and polarizations) in response to changes in phenology, disturbance, and water level. InSAR phase changes were closely related to fluctuations in water level and flow direction. We evaluated InSAR time series observations using measurements from water level loggers based on correlation and root mean square error (RMSE). It was found that correlation between InSAR measurements and water level changes in the field varied depending on the site, type of wetland vegetation, incidence angle and polarization. Although results from some sensor modes provided good correlation at a few locations, the low fringe rate and RMSE between 9 and 28 cm indicated that InSAR observations of water level changes were generally underestimated.

Extraction of digital elevation model from Chinese GF-3 C-band synthetic aperture radar data with a step-wise method focusing on facilitating phase unwrapping

GaoFen-3 (GF-3) is the first fully polarimetric C-band synthetic aperture radar (SAR) satellite in China. Interferometric observation is an important capability of GF-3. However, the long temporal baseline is a great challenge for repeat-pass interferometric synthetic aperture radar (InSAR) processing, especially over mountainous areas. Phase noise and complex topography make phase unwrapping difficult. This paper proposes a step-wise approach for high-resolution InSAR data processing focusing on facilitating phase unwrapping. First, the coarse GF-3 digital elevation model (DEM) is reconstructed by InSAR processing with multilooking of large looks, which is used to suppress phase noise. Second, the coarse GF-3 DEM is used to remove the terrain phase from the InSAR phase with multilooking of small looks, which can reduce phase gradient and improve the accuracy of phase unwrapping. After several iterations, the GF-3 DEM is finally inversed with multilooking of 2 looks, which is of high resolution and high accuracy. The performance of GF-3 C-band InSAR data on the extraction of DEM is examined over a deciduous forested area with the proposed method. The root-mean-square error of the extracted GF-3 DEM is 3.78 m in comparison with the LiDAR DEM.

L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska

Interferometric synthetic aperture radar (InSAR) provides the capability to detect surface deformation. Numerous processing approaches have been developed to improve InSAR results and overcome its limitations. Regardless of the processing methodology, however, temporal decorrelation is a major obstacle for all InSAR applications, especially over vegetated areas and dynamic environments, such as Interior Alaska. Temporal coherence is usually modeled as a univariate exponential function of temporal baseline. It has been, however, documented that temporal variations in surface backscattering due to the change in surface parameters, i.e., dielectric constant, roughness, and the geometry of scatterers, can result in gradual, seasonal, or sudden decorrelations and loss of InSAR coherence. The coherence models introduced so far have largely neglected the effect of the temporal change in backscattering on InSAR coherence. Here, we introduce a new temporal decorrelation model that considers changes in surface backscattering by utilizing the relative change in SAR intensity between two images as a proxy for the change in surface scattering parameters. The model also takes into account the decorrelation due to the change in snow depth between two images. Using the L-band Advanced Land Observation Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) data, the model has been assessed over forested and shrub landscapes in Delta Junction, Interior Alaska. The model decreases the RMS error of temporal coherence estimation from 0.18 to 0.09 on average. The improvements made by the model have been statistically proved to be significant at the 99% confidence level. Additionally, the model shows that the coherence of forested areas are more prone to changes in backscattering than shrub landscape. The model is based on L-band data and may not be expanded to C-band or X-band InSAR observations.

The present status of subsiding land vulnerable to roof collapse in the Jharia Coalfield, India, as obtained from shorter temporal baseline C-band DInSAR by smaller spatial subset unwrapped phase profiling

ABSTRACTIn this paper, we identified recently subsiding areas in Jharia Coalfield, Jharkhand, India from the shorter temporal baseline Radarsat-2 C-band interferometric synthetic aperture radar (InSAR) data pairs of 2012. Although shorter wavelength C-band differential InSAR (DInSAR) is more sensitive to slow deformation and better suited for higher precision land subsidence measurement, the dynamic and adverse land cover in mining areas and resulting temporal decorrelation problem poses a serious problem for DInSAR observation in mining areas. We used smaller temporal baseline data pairs and adopted InSAR coherence-guided incremental filtering with smaller moving windows to highlight the deformation fringes over temporal decorrelation noise. We identified the deformation fringes and validated them based on ground information to prepare the land subsidence map of the coalfield in 2012. Several new, previously unreported subsidence areas were detected in the present study with a total subsiding area of 6.9 km2. The recent incidence of roof collapse on 15 November 2014 at Angar Patra village in Katras region of the coalfield where 45 houses collapsed and 10 people were injured is situated in a highly subsiding vulnerable area as obtained from the present study. Due to spatial discontinuities of InSAR coherence, DInSAR phase unwrapping for the entire study area in one go did not appear feasible. To avoid this problem, we performed DInSAR processing in smaller spatial subsets and unwrapping of the subset interferograms by a ‘minimum cost flow’ algorithm. Subsequently, we plotted unwrapped phase profiles across the deformation fringes and retrieved the maximum deformation phase with respect to background phase and translated them into radar line of sight (LOS) displacement rates. For obtaining the average subsidence rates, we adopted InSAR coherence-weighted LOS displacement rates taking into account the contribution of each data pair as a function of DInSAR phase quality of the fringe areas. Ground-based subsidence measurements by precision levelling were conducted in four test sites that had been undergoing active underground mining during the observation period. We compared space-borne DInSAR-based subsidence rates obtained by the adopted technique with precision levelling measurements. Overall, the results are found to agree well. In the four test sites with gentle to flat topography, land subsidence occurs at slow to moderate rates due to compression of in-filled material (resulting from sand stowing in underground mining), without any evidence of roof collapse. In such cases, the horizontal displacement component is less significant, and overall surface displacement occurs essentially in the vertical direction. However, we assessed the nature of subtle horizontal strain to infer relative shrinkage or dilation of the land surface which could be additive or subtractive to vertical displacement in DInSAR-based LOS displacement.

Revisiting the shallow Mw 5.1 Lorca earthquake (southeastern Spain) using C-band InSAR and elastic dislocation modelling

Space geodetic techniques such as interferometric synthetic aperture radar (InSAR) and global positioning systems (GPS) have demonstrated to be useful in mapping the displacement fields of large earthquakes (moment magnitude (Mw) ∼6 or higher). However, the displacement fields of smaller earthquakes (Mw < 5.5), such as those that typically result from the collision of the European and African plates, are less often analysed by space geodetic techniques, and their characterization, in terms of slip along the fault plane at depth and focal depth location, often challenges current seismological techniques. This letter presents the results of InSAR analysis of the 11 May 2011, Mw 5.1, Lorca earthquake. The Lorca earthquake occurred close to an area undergoing rapid subsidence due to sediment compaction related to water pumping. Therefore, it is challenging to separate the InSAR signals due to the earthquake from those due to human activity. We used four sets of SAR data acquired from the European C-band Advanced SAR (ASAR) sensor on board the Environmental Satellite (ENVISAT) to map the surface-displacement field in the Lorca region. Then, we use a simple elastic dislocation model to characterize the fault plane geometry and the fault slip at depth. We find that the InSAR signals can be explained by ∼21 cm reverse slip with a ∼6 cm left-lateral component on a 3 km × 3 km segment centred at 4.2 km depth dipping 45° NW and striking N65° E, consistent with the rupture of a segment of the Alhama de Murcia fault and consistent with recent published analyses. Interestingly, the InSAR signal can also be explained by ∼21 cm reverse slip with a ∼6 cm left-lateral component on a 3 km × 3 km segment centred at ∼4.2 km depth dipping 50° SE and striking N230° E, consistent with preliminary focal plane solutions indicating a rupture on a previously unmapped blind structure. We conclude that the second model cannot be rejected on the base of the InSAR results, the complex surface-displacement pattern (containing both seismic and non-seismic displacement), the different preliminary moment tensor solutions and the published locations of aftershocks at depth.

Error analysis and correction for extracting the forest height from airborne C-band interferometric SAR and national elevation datasets

The Interferometric Synthetic Aperture Radar (InSAR) signal is returned from the canopy of the obscuring trees instead of bare ground when land is covered by forests. Therefore, the difference between an InSAR elevation and a bare earth model might contain information on forest height. The objective of this paper was to investigate if the difference between an airborne C-band InSAR from the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) and a bare earth model of 1/3 arcsecond National Elevation Datasets can be used for regional forest height estimation. The error sources of vertical offset, uncompensated roll angle, residual vertical bias, and scattering phase centre height conversion were analysed and corrected in this estimation. The results were validated by the least-square linear regression analysis between Light Detection and Ranging (LiDAR) and the estimated height at different forest stand sizes within different slope categories. In areas with slopes less than 5°, the correlation coefficients increased when forest stand sizes increased. In the area of slope ranging from 5 to 10°, a similar trend of increasing correlation coefficients with increasing stand size could also be observed, but with smaller corresponding correlation coefficients than those of slope 0–5°. In areas with slopes larger than 10°, the correlation coefficients were very poor. These results indicate the difference between airborne C-band InSAR and the accurate bare earth model has the potential for regional forest height estimation in flat areas with a minimum unit of 3750–5000 m2. However, to accurately estimate forest height in a mountainous terrain a solution must be found to correct the significant amount of noise caused by the terrain in these areas.

Integrated analysis of PALSAR/Radarsat-1 InSAR and ENVISAT altimeter data for mapping of absolute water level changes in Louisiana wetlands

Interferometric Synthetic Aperture Radar (InSAR) has been used to detect relative water level changes in wetlands. We developed an innovative method to integrate InSAR and satellite radar altimetry for measuring absolute or geocentric water level changes and applied the methodology to remote areas of swamp forest in coastal Louisiana. Coherence analysis of InSAR pairs suggested that the HH polarization is preferred for this type of observation, and polarimetric analysis can help to identify double-bounce backscattering areas in the wetland. ENVISAT radar altimeter-measured 18-Hz (along-track sampling of 417 m) water level data processed with regional stackfile method have been used to provide vertical references for water bodies separated by levees. The high-resolution (~ 40 m) relative water changes measured from ALOS PALSAR L-band and Radarsat-1 C-band InSAR are then integrated with ENVISAT radar altimetry to obtain absolute water level. The resulting water level time series were validated with in situ gauge observations within the swamp forest. We anticipate that this new technique will allow retrospective reconstruction and concurrent monitoring of water conditions and flow dynamics in wetlands, especially those lacking gauge networks.

Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets

A study was conducted to determine the feasibility of obtaining estimates of vegetation canopy height from digital elevation data collected during the 2000 Shuttle Radar Topography Mission (SRTM). The SRTM sensor mapped 80% of the Earth's land mass with a C-band Interferometric Synthetic Aperture Radar (InSAR) instrument, producing the most complete digital surface map of Earth. Due to the relatively short wavelength (5.6 cm) of the SRTM instrument, the majority of incoming electromagnetic energy is reflected by scatterers located within the vegetation canopy at heights well above the “bald-Earth” surface. Interferometric SAR theory provides a basis for properly identifying and accounting for the dependence of this scattering phase center height on both instrument and target characteristics, including relative and absolute vertical error and vegetation structural attributes. An investigation to quantify the magnitude of the vertical error component was conducted using SRTM data from two vegetation-free areas in Iowa and North Dakota, revealing absolute errors of −4.0 and −1.1 m, respectively. It was also shown that the relative vertical error due to phase noise can be reduced significantly through sample averaging. The relative error range for the Iowa site was reduced from 13 to 4 m and for the North Dakota site from 7 to 3 m after averaging of 50 samples. Following error reduction, it was demonstrated that the SRTM elevation data can be successfully correlated via linear regression models with ground-measured canopy heights acquired during the general mission timeframe from test sites located in Georgia and California. Prior to outlier removal and phase noise reduction, initial adjusted r 2 values for the Georgia and California sites were 0.15 and 0.20, respectively. Following outlier analysis and averaging of at least 20 SRTM pixels per observation, adjusted r 2 values for the Georgia and California sites improved to 0.79 (rmse=1.1 m) and 0.75 (rmse=4.5 m), respectively. An independent validation of a novel bin-based modeling strategy designed for reducing phase noise in sample plot data confirmed both the robustness of the California model (adjusted r 2=0.74) as well as the capacity of the binning strategy to produce stable models suitable for inversion (validated rmse=4.1 m). The results suggest that a minimum mapping unit of approximately 1.8 ha is appropriate for SRTM-based vegetation canopy height mapping.

Topographic mapping using polarimetric SAR data

A new remote sensing technique using polarimetric synthetic aperture radar (SAR) data has been developed which can measure terrain slopes in the azimuthal, or along-track, direction. Terrain elevation maps can then be generated by integrating these slopes. The processing of both single-pass, and orthogonal two-pass, datasets is investigated. When single-pass SAR data is used elevation groundtruth must be available for at least one point of each profile formed in the azimuthal direction. When orthogonal two-pass slope data is employed, the elevation surface may be generated as an iterative solution of the Poisson equation and only a single elevation tie-point is required. The results of two studies are presented. In the first study slopes have been measured using single-pass P-band data for a forested area in northern California. The elevation surface generated with this data has been compared with a Digital Elevation Model (DEM) produced by a C-band interferometric SAR. The second study uses orthogonal two- pass P-band data as a test of the Poisson equation approach for an area in Death Valley National Park, California. The orthogonal two-pass results have been compared with a co-registered, conventional, U.S. Geological Survey (USGS) product. Technique accuracy and potential applications are discussed.