Sentinel-1 Interferometric Wide Swath Research Articles

Submesoscale oceanic eddy detection in SAR images using context and edge association network

Oceanic eddies have a non-negligible impact on ocean energy transfer, nutrient distribution, and biological migration in global oceans. The fine detection of oceanic eddies is significant for the development of marine science. Remarkable achievements of eddy recognition were achieved by mining the satellite altimeter data and its derived data. However, due to the limited spatial resolution of the altimeters, it is difficult to detect the submesoscale oceanic eddies with radial dimensions less than 10 km. Different from the previous works, the context and edge association network (CEA-Net) is proposed to identify submesoscale oceanic eddies with high spatial resolution Sentinel-1 data. The edge information fusion module (EIFM) is designed to associate the context and edge feature more accurately and efficiently. Furthermore, a multi-scale eddy detection strategy is proposed and applied to Sentinel-1 interferometric wide swath data to solve the scale problem of oceanic eddy detection. Specifically, a manually interpreted dataset, SAR-Eddy 2019, was constructed to address the dilemma of insufficient datasets for submesoscale oceanic eddy detection. The experimental results demonstrate that CEA-Net can outperform other mainstream models with the highest mAP reaching 85.47% with SAR-Eddy 2019 dataset. The CEA-Net proposed in this research provides important significance for the study of submesoscale oceanic eddies.

Probabilistic SAR-based water segmentation with adapted Bayesian convolutional neural network

Geospatial resources, including satellite-based synthetic aperture radar (SAR) and optical data, have been instrumental in providing time-sensitive information about the extent and impact of natural hazard events, such as floods, to support emergency response and hazard management efforts. In effect, finite resources can be better optimized to support the needs of often extensively affected areas. However, the derivation of SAR-based flood information is not without its challenges and inaccurate flood detection can result in poor decision-making and non-trivial, adverse consequences. Reliable uncertainty quantification of flood extent estimates addresses this risk. In this context, our study presents the results of two probabilistic convolutional neural networks (CNNs) adapted for SAR-based water segmentation with freely available Sentinel-1 interferometric wide (IW) swath ground range detected (GRD) data. In particular, the performance of a variational inference-based Bayesian convolutional neural network (BCNN) is evaluated against that of a Monte Carlo dropout network (MCDN). In previous studies where uncertainty information has been generated along with segmentation results, MCDN has been more commonly applied as an approximation of Bayesian deep learning. Differences between the two approaches are highlighted with the application of a set of extended performance metrics. Both segmentation and uncertainty outputs are evaluated at data-, model-, tile- and scene-levels. The methods are demonstrated on the binary segmentation of a reference water dataset. We show that while different probabilistic techniques return comparable segmentation accuracies, they are differentiated based on their performance in assigning reliable probabilities. In particular, MCDNs characterized by more restrictive architectures generally lead to overconfident prediction intervals, whereas BCNNs have greater flexibility to learn the mean and the spread of the parameter posterior. Furthermore, we demonstrate that examining the (inaccurate, certain) metric is a better indicator of reliable uncertainty quantification and the BCNN is recommended to quantify uncertainties associated with SAR-based segmentation outputs. This information is especially valuable where the cost of inaccurate detections (false-positive and false-negative) is high. These preliminary findings highlight how the manner in which probabilities are properly assigned and their inclusion are instrumental and complementary to the production of flood masks and should be considered as a standard in the natural hazards domain.

Analysis of fine-scale dynamics of the Drygalski ice tongue in Antarctica using satellite SAR data

ABSTRACT This study focused on the analysis of the time variability of the morphology of the Drygalski ice tongue (DIT), Antarctica, using – for the first time – satellite synthetic aperture radar (SAR) images. A time series of Sentinel-1 interferometric wide swath SAR imagery collected from 2016 to 2021 is considered and an unsupervised methodology, based on a global threshold constant false alarm rate approach, is used to extract the boundary between the DIT and the surrounding ice-free/ice-infested sea water. The most prominent rifts/fractures identified on the extracted profiles and the ice front are selected to analyse the DIT time variability. The feature tracking allows deriving information on the morphological evolution of the DIT, including the annual displacement and average surface velocity. Experimental results show that the DIT ice front calls for a relatively stable motion trend towards the sea with an average surface velocity of about 670 m per year. Our outcomes show a fairly good agreement with similar studies appeared in the scientific literature, which are mostly based on optical imagery.

Accurate Azimuth Ground Deformation Estimation From Sentinel-1 Time Series

This letter proposes the exploitation of the overlapping areas between bursts of Sentinel-1 interferometric wide (IW) swath data for the estimation of ground azimuth deformation velocities from time series, which provides high sensitivity to the North–South direction. The availability of two separated Doppler spectral looks over these areas allows one to obtain high accurate estimates, overcoming the limited performance of classical correlation techniques with coarse resolution modes. Since the estimation is restricted to the (sparsely distributed) burst overlaps, its applicability is limited to geophysical phenomena characterized by a slow spatial variability, e.g., seismic scenarios. We demonstrate the suitability of this approach to time series over a region in Pakistan affected by post-seismic deformation. The estimated mean azimuth velocity performance from real data indicates an accuracy better than 7 mm/year over high long-term coherent areas for a three-year data stack.

On the Efficacy of Compact Radar Transponders for InSAR Geodesy: Results of Multiyear Field Tests

Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CR) for SAR interferometric (InSAR) deformation monitoring, datum connection, and geodetic data integration. Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven. Here we address these challenges using a multitude of short baseline experiments with four transponders and six corner reflectors deployed at test sites in the Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months. We find an average Radar Cross Section (RCS) of over 42 dBm2 within a range of up to 15 degrees of elevation misalignment, which is comparable to a triangular trihedral corner reflector with a leg length of 2.0 m. Its RCS shows temporal variations of 0.3--0.7~dBm2 (standard deviation) which is partially correlated with surface temperature changes. The precision of the InSAR phase double-differences over short baselines between a transponder and a stable reference corner reflectors is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to +/-3 mm, which should be modeled and corrected for in high precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant-range, i.e., within the range resolution of the Sentinel-1 Interferometric Wide Swath (IW) product, with a temporal variability of less than 20~cm. Comparing similar transponders from the same series, we observe distinct differences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.

Extreme Wind Speeds Retrieval Using Sentinel-1 IW Mode SAR Data

With the improvement in microwave radar technology, spaceborne synthetic aperture radar (SAR) is widely used to observe the tropical cyclone (TC) wind field. Based on European Space Agency Sentinel-1 Interferometric Wide swath (IW) mode imagery, this paper evaluates the correlation between vertical transmitting–horizontal receiving (VH) polarization signals and extreme ocean surface wind speeds (>40 m/s) under strong TC conditions. A geophysical model function (GMF) Sentinel-1 IW mode wind retrieval model after noise removal (S1IW.NR) was proposed, according to the SAR images of nine TCs and collocated stepped frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) radiometer wind speed measurements. Through curve fitting and regression correction, the new GMF exploits the relationships between VH-polarization normalized radar cross section, incident angle, and wind speed in each sub-swath and covers wind speeds up to 74 m/s. Based on collocated SAR and SFMR measurements of four TCs, the new GMF was validated in the wind speed range from 2 to 53 m/s. Results show that the correlation coefficient, bias, and root mean squared error were 0.89, −0.89 m/s, and 4.13 m/s, respectively, indicating that extreme winds can be retrieved accurately by the new model. In addition, we investigated the relationship between the S1IW.NR wind retrieval bias and the SFMR-measured rain rate. The S1IW.NR model tended to overestimate wind speeds under high rain rates.

Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration.

Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration. These methods generally follow the nonlocal filtering processing chain, aiming at circumventing the staircase effect and preserving the details of phase variations. In this article, we propose an alternative approach for InSAR phase restoration, that is, Complex Convolutional Sparse Coding (ComCSC) and its gradient regularized version. To the best of the authors' knowledge, this is the first time that we solve the InSAR phase restoration problem in a deconvolutional fashion. The proposed methods can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. Furthermore, they provide an insight into the elementary phase components for the interferometric phases. The experimental results on synthetic and realistic high- and medium-resolution data sets from TerraSAR-X StripMap and Sentinel-1 interferometric wide swath mode, respectively, show that our method outperforms those previous state-of-the-art methods based on nonlocal InSAR filters, particularly the state-of-the-art method: InSAR-BM3D. The source code of this article will be made publicly available for reproducible research inside the community.

Investigating Ground Subsidence and the Causes over the Whole Jiangsu Province, China Using Sentinel-1 SAR Data

Interferometric synthetic aperture radar (InSAR) mapping of localized ground surface deformation has become an important tool to manage subsidence-related geohazards. However, monitoring land surface deformation using InSAR at high spatial resolution over a large region is still a formidable task. In this paper, we report a research on investigating ground subsidence and the causes over the entire 107, 200 km2 province of Jiangsu, China, using time-series InSAR. The Sentinel-1 Interferometric Wide-swath (IW) images of 6 frames are used to map ground subsidence over the whole province for the period 2016–2018. We present processing methodology in detail, with emphasis on the three-level co-registration scheme of S-1 data, retrieval of mean subsidence velocity (MSV) and subsidence time series, and mosaicking of multiple frames of results. The MSV and subsidence time series are generated for 9,276,214 selected coherent pixels (CPs) over the Jiangsu territory. Using 688 leveling measurements in evaluation, the derived MSV map of Jiangsu province shows an accuracy of 3.9 mm/year. Moreover, subsidence causes of the province are analyzed based on InSAR-derived subsidence characteristics, historical optical images, and field-work findings. Main factors accounting for the observed subsidence include: underground mining, groundwater withdrawal, soil consolidations of marine reclamation, and land-use transition from agricultural (paddy) to industrial land. This research demonstrates not only the capability of S-1 data in mapping ground deformation over wide areas in coastal and heavily vegetated region of China, but also the potential of inferring valuable knowledge from InSAR-derived results.

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

ABSTRACT A general framework for the integration of multi-sensor data for dry and fresh biomass retrieval is proposed and tested in Alpine meadows and pastures. To this purpose, hyperspectral spectroradiometer (as simulation of hyperspectral imagery) and biomass samples were collected in field campaigns and Copernicus Sentinel-1 Interferometric Wide (IW) swath SAR backscattering coefficients were used. First, a genetic algorithm feature selection was performed on hyperspectral data, and afterwards the resulting most sensitive bands where combined with SAR data within a support vector regression (SVR) model. The most sensitive hyperspectral bands were mainly located in different regions of the SWIR range for both fresh and dry biomass, and in the red and near-infrared regions mainly for dry biomass, but with less influence for fresh biomass. The R 2 correlation values between the sampled and the estimated biomass range from 0.24 to 0.71. The relatively low performances are mainly related to the saturation effect in the optical bands, as well as to the paucity of points for high values of biomass. The methodology allows a better understanding of the interaction between grassland systems and the electromagnetic spectrum by offering a model with a reduced number of narrow bands in the context of a multi-sensor integration.

On the Mutual Interference Between Spaceborne SARs: Modeling, Characterization, and Mitigation

Since by the International Telecommunications Union (ITU) regulatory the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of a SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR's antenna, causing radiometric artefacts in the focused image. This paper tries to model and characterize the artefacts, and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artefact, which reveals that the mutual interference can introduce a two-dimensional LFM radiometric artefact in image domain with a limited spatial extent. We show that the artefact is low-rank based on range-azimuth decoupling analysis and two-dimensional high-order Taylor expansion. Based on the low rank model, we show that two methods, i.e., principle component analysis and its robust variant, can be adopted to efficiently mitigate the artefact via processing in image domain. The former method has the advantage of fast processing speed, for example, a sub-swath of Sentinel-1 interferometric wide swath image can be processed within 70 seconds via block-wise operation, whereas the latter provides improved accuracy for sparse point-like scatterers. Experiment results demonstrate that the radiometric artefacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.

Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment

We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements.

DERIVING EXCLUSION MAPS FROM C-BAND SAR TIME-SERIES: AN ADDITIONAL INFORMATION LAYER FOR SAR-BASED FLOOD EXTENT MAPPING

Abstract. Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images as floodwater often leads to a substantial decrease of backscatter. However, limitations still exist in many areas, such as shadow, layover, urban areas and densely vegetated areas, where the SAR backscatter is not sufficiently impacted by floodwater-related surface changes. This study focuses on these so-called exclusion areas, i.e. areas where SAR does not allow detecting water based on change detection. Our approach considers both pixel-based time series analyses and object-based spatial analyses using 20m Sentinel-1 Interferometric Wide Swath data, including 922 Sentinel-1 tiles covering the River Severn basin (UK) and the Lake Maggiore area (Italy). The results show that our exclusion map presents a good agreement (∼63%) with reference data derived from different data sources and indicate that it may complement SAR-derived flood extent maps. Allowing to accurately identify potential misclassifications in flood extent mapping, our exclusion map provides valuable information for flood management and, in particular, flood forecasting and prediction.

In-Depth Verification of Sentinel-1 and TerraSAR-X Geolocation Accuracy Using the Australian Corner Reflector Array

This article shows how the array of corner reflectors (CRs) in Queensland, Australia, together with highly accurate geodetic synthetic aperture radar (SAR) techniques-also called imaging geodesy-can be used to measure the absolute and relative geometric fidelity of SAR missions. We describe, in detail, the end-to-end methodology and apply it to TerraSAR-X Stripmap (SM) and ScanSAR (SC) data and to Sentinel-1 interferometric wide swath (IW) data. Geometric distortions within images that are caused by commonly used SAR processor approximations are explained, and we show how to correct them during postprocessing. Our results, supported by the analysis of 140 images across the different SAR modes and using the 40 reflectors of the array, confirm our methodology and achieve the limits predicted by theory for both Sentinel-1 and TerraSAR-X. After our corrections, the Sentinel-1 residual errors are 6 cm in range and 26 cm in azimuth, including all error sources. The findings are confirmed by the mutual independent processing carried out at University of Zurich (UZH) and German Aerospace Center (DLR). This represents an improvement of the geolocation accuracy by approximately a factor of four in range and a factor of two in azimuth compared with the standard Sentinel-1 products. The TerraSAR-X results are even better. The achieved geolocation accuracy now approaches that of the global navigation satellite system (GNSS)-based survey of the CRs positions, which highlights the potential of the end-to-end SAR methodology for imaging geodesy.

Discovering Potential Settlement Areas around Archaeological Tells Using the Integration between Historic Topographic Maps, Optical, and Radar Data in the Northern Nile Delta, Egypt

The primary objective of this study is to leverage the integration of surface mapping data derived from optical, radar, and historic topographical studies with archaeological sampling to identify ancient settlement areas in the Northern Nile Delta, Egypt. This study employed the following methods: digitization of topographic maps, band indices techniques on optical data, the creation of a 3D model from SRTM data, and Sentinel-1 interferometric wide swath (IW) analysis. This type of study is particularly relevant to the search for evidence of otherwise hidden ancient settlements. Due to its geographical situation and the fertility of the Nile, Egypt witnessed the autochthonous development of predynastic and dynastic civilizations, as well as an extensive history of external influences due to Greek, Roman, Coptic, Islamic, and Colonial-era interventions. Excavation work at Buto (Tell el-Fara’in) in 2017–18, carried out by the Kafrelsheikh University (KFS) in cooperation with the Ministry of Antiquities, demonstrated that remote sensing data offers considerable promise as a tool for developing regional settlement studies and excavation strategies. This study integrates the mission work in Buto with the satellite imagery in and around the area of the excavation. The results of the initial Buto area research serve as a methodological model to expand the study area to the North Delta with the goal of detecting the extent of the ancient kingdoms of Buto and Sakha. The results of this research include the creation of a composite historical database using ancient references and early topographical maps (1722, 1941, 1950, and 1997), Optical Corona (1965), Landsat MSS (Multispectral Scanner System) (1973, 1978, and 1988), TM (Thematic Mapper) (2005) data, and Radar SRTM (2014) and Sentinel1 (2018 and 2019) data. The data in this study have been analyzed using the ArcMap, Envi, and SNAP software. The results from the current investigation highlight the rapid changes in the land use/land cover in the last century in which many ancient sites were lost due to agriculture and urban development. Three potential settlement areas have been identified with the Sentinel1 Radar data, and have been integrated with the early maps. These discoveries will help develop excavation strategies aimed at elucidating the ancient settlement dynamics and history of the region during the next phase of research.

On accuracy of SAR wind speed retrieval in coastal area

In coastal region, the overestimation of wind speed are observed in SAR wind speed retrievals and the contamination from land and ships are considered as one of the main reasons. A novel method for calculating the value of normalized radar cross section (NRCS) cell has been developed to improve high-resolution synthetic aperture radar (SAR) wind retrieval in the case of contamination. The ratio of median and mean (Rmmpp) has been proposed to assess the effect of the distribution of NRCS on wind retrievals quantitatively. In this research, Sentinel-1 interferometric wide-swath (IW) dual polarization SAR data were collocated with in situ wind measurements from National Data Buoy Center (NDBC) for comparison. CMOD5, CMOD7 and the C-band Cross-Polarization Ocean (C-2PO) were used for wind retrievals. The statistic validation results show that the proposed method is more robust than the conventional method for wind speed retrievals. In addition, the Rmmpp could be used as an indicator for the reliability of SAR wind and the accuracy of wind speed increase with the Rmmpp increasing from 0.2 to 1.0 in the coastal area.

The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

Building a SAR-Enabled Data Cube Capability in Australia Using SAR Analysis Ready Data

A research alliance between the Commonwealth Scientific and Industrial Research Organization and Geoscience Australia was established in relation to Digital Earth Australia, to develop a Synthetic Aperture Radar (SAR)-enabled Data Cube capability for Australia. This project has been developing SAR analysis ready data (ARD) products, including normalized radar backscatter (gamma nought, γ0), eigenvector-based dual-polarization decomposition and interferometric coherence, all generated from the European Space Agency (ESA) Sentinel-1 interferometric wide swath mode data available on the Copernicus Australasia Regional Data Hub. These are produced using the open source ESA SNAP toolbox. The processing workflows are described, along with a comparison of the γ0 backscatter and interferometric coherence ARD produced using SNAP and the proprietary software GAMMA. This comparison also evaluates the effects on γ0 backscatter due to variations related to: Near- and far-range look angles; SNAP’s default Shuttle Radar Topography Mission (SRTM) DEM and a refined Australia-wide DEM; as well as terrain. The agreement between SNAP and GAMMA is generally good, but also presents some systematic geometric and radiometric differences. The difference between SNAP’s default SRTM DEM and the refined DEM showed a small geometric shift along the radar view direction. The systematic geometric and radiometric issues detected can however be expected to have negligible effects on analysis, provided products from the two processors and two DEMs are used separately and not mixed within the same analysis. The results lead to the conclusion that the SNAP toolbox is suitable for producing the Sentinel-1 ARD products.

Seasonal dynamics of a permafrost landscape, Adventdalen, Svalbard, investigated by InSAR

Nordenskiöld Land in Central Spitsbergen, Svalbard is characterized as a high latitude, high relief periglacial landscape with permafrost occurring both in mountains and lowlands. Freezing and thawing of the active layer causes seasonal frost heave and thaw subsidence, while permafrost-related mass-wasting processes induce downslope ground displacements on valley sides. Displacement rate varies spatially and temporally depending on environmental factors. In our study, we apply Satellite Synthetic Aperture Radar Interferometry (InSAR) to investigate the magnitude, spatial distribution and timing of seasonal ground displacements in and around Adventdalen using TerraSAR-X StripMap Mode (2009–2017) and Sentinel-1 Interferometric Wide Swath Mode (2015–2017) SAR images. First, we show that InSAR results from both sensors highlight consistent patterns and provide a comprehensive overview of the distribution of displacement rates. Secondly, two-dimensional (2D) TerraSAR-X InSAR results from combined ascending and descending geometries document the spatial variability of the vertical and east-west horizontal displacement rates for an average of nine thawing seasons. The remote sensing results are compared to a simplified geomorphological map enabling the identification of specific magnitudes and orientations of displacements for 14 selected geomorphological units. Finally, June to December 2017 6-day sampling interval Sentinel-1 time series was retrieved and compared to active layer ground temperatures from two boreholes. The timing of the subsidence and heave detected by InSAR matches the thawing and freeze-back periods measured by in-situ sensors. Our results highlight the value of InSAR to obtain landscape scale knowledge about the seasonal dynamics of complex periglacial environments.

The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment

Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.