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Sentinel-1 Wide Swath Interferometry: Processing Techniques and Applications
Abstract and Figures
The Sentinel-1 (S1) mission is a part of the European Space Agency (ESA) Copernicus program. In 2014 and 2016, the mission launched the twin Synthetic Aperture
Radar (SAR) satellites, Sentinel-1A (S1A) and Sentinel-1B (S1B). The S1 mission has
started a new era for earth observations missions with its higher spatial resolution,
shorter revisit days, more precise control of satellites orbits and the unprecedented
free-to-public distribution policy. More importantly, S1 adopts a new wide swath
mode, the TOPS mode as it default acquisition mode. The TOPS mode scans several
different subswaths for gaining a larger coverage. Because the S1 mission is aimed
at earth observation missions, for example, earthquakes, floods, ice sheets flow, etc.,
thus it is desired to have large monitoring areas. Although TOPS is still a relatively
new idea, the high quality data and wide application scopes from S1 has earned
tremendous attention in the SAR community.
The signal properties of wide swath mode such as TOPS are different from the
more conventional stripmap mode, and it requires special techniques for successfully
processing such data in the sense of interferometry. For the purpose of doing Interferometric SAR (InSAR), the coregistration step is of most critical because it requires a
1/1000 accuracy. In addition, processing wide swath mode requires special steps such
as bursts stitching, deramping and reramping, and so on. Compared with stripmap,
the processing techniques of wide swath mode are less developed. Much exploitations
are still needed for how to design a generic and robust wide swath interferometric
processing chain.
Driven by the application needs of S1 wide swath interferometric processing, this
research studies the key methodologies, explores and implements new processing
chain, designs a generic wide swath processing flow that would utilize the existing
stripmap processing platform, as well as carries out preliminary applications. For
studying key methods, this study carries out a quantitative analysis between two
different coregistration methods, namely the cross-correlation approach and the geometrical approach. The advantages and disadvantages for each method are given by
the author, and it is proposed to choose the suitable method based on one’s study
area. For the implementation of the new processing chain, the author proposes a
user-friendly stripmap-like processing flow with all the wide swath related process
done behind the scene. This approach allows people with basic knowledge in InSAR
and very few knowledge in wide swath mode to be able to process and get interferometric products. For designing the generic process flow, the author applied TOPS’s
workflow to the other wide swath mode, ScanSAR mode and demonstrated the feasibility of processing two different wide swath mode with the same processing chain.
For preliminary applications, the author shows a large number of interferometric data
throughout the research and presents a case study with multi temporal time series
analysis using a stack of S1 dataset.
This research is application oriented, which means the study serves for real-world
applications. Up to now, the processing chain and methodologies implemented in this
research has been shared by many research groups around the world and has seen a
number of promising outcomes. The recognition from others is also an affirmation to
the value of this research.
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... Sentinel-1 SAR data coregistration preprocessing, including data extraction, terrain observation with progressive scans (TOPS) splits and de-ramp, geometric coregistration, enhanced spectral diversity (ESD) fine coregistration, re-ramp, burst, and sub-swath merging [53,60], were applied according to the requirements [61]. ...
Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interfer-ometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the ro-bustness of deformation parameters estimation in the DTN, we combined the Nelder-Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from-150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.
... The European Space Agency (ESA) has developed the Copernicus program which is currently composed of 6 Sentinel missions. S-1 Terrain Observation with Progressive Scan (TOPS) [51], [52] mode data shows its importance for forest mapping and monitoring. S-1 maintains a systematic routine operation that covers almost the entire land area on Earth. ...
There is a need for mapping of forest areas with young stands under regeneration in Norway, as a basis for conducting tending, or precommercial thinning (PCT), whenever necessary. The main objective of this paper to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) data for characterization and detection of forest stands under regeneration. We identify the most powerful radar and optical features for discrimination of forest stands under regeneration versus other forest stands. A number of optical and radar features derived from multitemporal S-1 and S-2 data were used for the class separability and cross-correlation analysis. The analysis was performed on forest resource maps consisting of the forest development classes and age in two study sites from southeastern Norway. Important features were used to train the classical random forest (RF) classification algorithm. A comparative study of performance of the algorithm was used in three cases: I) using only S-1 features, II) using only S-2 optical bands, and III) using combination of S-1 and S-2 features. RF classification results pointed to increased class discrimination when using S-1 and S-2 data in relation to S-1 or S-2 data only. The study shows that forest stands under regeneration in the height interval for PCT can be detected with a detection rate of 91% and F-1 score of 73.2% in case III as most accurate, while tree density and broadleaf fraction could be estimated with coefficient of determination (R 2) of about 0.70 and 0.80, respectively.
Subsidence in California as measured by InSAR
Terrain observation by progressive scans (TOPS)-mode synthetic aperture radar interferometry requires high accuracy of burst alignments. Geometrical burst alignment relying on precise orbits and digital topography is not always sufficient for Sentinel-1A TOPS-mode interferometry. Enhanced spectral diversity (ESD) method was proposed to estimate a constant azimuth shift between radar images that minimizes phase discontinuities across the bursts. In some cases, however, the ESD refinement fails to align the bursts in Sentinel-1 interferograms, possibly because of ionospheric propagation effects. Here, we show that in such cases, a bivariate shift (that depends on both azimuth and range) can efficiently remove phase discontinuities across the bursts. The bivariate shift can be derived from the double-differenced radar phase in the burst overlap regions.
Post-event Interferometric Synthetic Aperture Radar (InSAR) analysis on a stack of 45 C-band SAR images acquired by the ESA Sentinel-1 satellites from 9 October 2014 to 19 June 2017 allowed the identification of a clear precursory deformation signal for the Maoxian landslide (Mao County, Sichuan Province, China). The landslide occurred in the early morning of 24 June 2017 and killed more than 100 people in the village of Xinmo. Sentinel-1 images have been processed through an advanced multi-interferogram analysis capable of maximising the density of measurement points, generating ground deformation maps and displacement time series for an area of 460 km2 straddling the Minjiang River and the Songping Gully. InSAR data clearly show the precursors of the slope failure in the source area of the Maoxian landslide, with a maximum displacement rate detected of 27 mm/year along the line of sight of the satellite. Deformation time series of measurement points identified within the main scarp of the landslide exhibit an acceleration starting from April 2017. A detailed time series analysis leads to the classification of different deformation behaviours. The Fukuzono method for forecasting the time of failure appear to be applicable to the displacement data exhibiting progressive acceleration. Results suggest that satellite radar data, systematically acquired over large areas with short revisiting time, could be used not only as a tool for mapping unstable areas, but also for landslide monitoring, at least for some typologies of sliding phenomena.
The Qinghai Tibetan Plateau permafrost has been the largest permafrost region in middle-low latitude in the world for its high altitude. For the large area permafrost, especially surface deformation brought by it, have serious influence on the road engineering, road maintaining and regional economic development. Consequently, it is essential to monitor the surface deformation and study factors that influent it. We monitored an area named Wudaoliang from July 25, 2015 to June 1, 2016 and 15 Sentinel images were obtained during this time. The area we chose is about 35 kilometers long and 2 kilometers wide, and the national road 109 of China passes through the area. The traditional PS-INSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) method is not suitable because less historical images in the research area and leading to the number of PS (Persistent Scatterer) points is not enough to obtain accurate deformation results. Therefore, in this paper, we used another method which named QUASI-PSInSAR (QUASI Persistent Scatterer Interferometric Synthetic Aperture Radar) to acquire deformation for it has the advantage to weaken or eliminate the effects of spatial and temporal correlation, which has proved by other scholar. After processing 15 images in the SARproz software, we got the conclusions that, 1) the biggest deformation velocity in the whole area was about 127.9mm/year and about 109.3 mm/year in the road; 2) apparent deformation which have surface deformation more than 30mm/year was about 1.7Km in the road. Meanwhile, soil moisture(SM), Land surface temperature (LST) and surface water(SW), which are primary parameters of the land surface over the same time were reversed by using Sentinel data, Landsat data and ZY-3 data, respectively. After analyzing SM, LST , SW and deformation, we obtained that wet areas which had bigger SM, lower LST and more SW, had greater percentage of severe deformation than arid areas; besides, deformation pattern were different in arid areas and wet areas. During the research time, frost heaving firstly accounted for a large proportion both in the arid and wet areas with the decrease of downward radiation from July to December; after December, thaw settlement came into prominence with the increase downward radiation in the arid areas, while in the wet areas, surface put into diverse situations because of water transformation leading to severe deformation. In summary, soil moisture is an important factor that influences the surface deformation. This relationship between deformation process and soil moisture will be researched more in our further work.
Sentinel-1B is the second of two C-Band Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 mission, launched in April 2016—two years after the launch of the first satellite, Sentinel-1A. In addition to the commissioning of Sentinel-1B executed by the European Space Agency (ESA), an independent system calibration was performed by the German Aerospace Center (DLR) on behalf of ESA. Based on an efficient calibration strategy and the different calibration procedures already developed and applied for Sentinel-1A, extensive measurement campaigns were executed by initializing and aligning DLR’s reference targets deployed on the ground. This paper describes the different activities performed by DLR during the Sentinel-1B commissioning phase and presents the results derived from the analysis and the evaluation of a multitude of data takes and measurements.
The coregistration of synthetic aperture radar images is of fundamental importance for the generation of interferograms. The high azimuth coregistration requirements imposed by the TOPS acquisition mode imply that an advanced approach for the coregistration of stacked time series images is needed due to temporal decorrelation effects. In some scenarios, the conventional approach of estimating the shifts pairwise with respect to the same master might result insufficient. Therefore, a joint estimation is proposed here, which exploits jointly all interferograms in order to retrieve more accurate results. Simulated data and Sentinel-1A images acquired in IW mode are used to validate this procedure, demonstrating the better performance of the joint approach when compared to the standard single-master approach.
The Sentinel-1 (S-1) Instrument Processing Facility (IPF) is responsible for generating the complete family of Level-1 and Level-2 operation products [AD- 1]. It is the first SAR instrument featuring the TOPS [AD- 3] mode. As a matter of fact, S-1 features two TOPS mode being EWS and IWS for which the mode definition can be found in [AD- 4].
The particularity of TOPS is the electronic steering of the antenna in azimuth (inverse to SPOT) to illuminate the targets with the same azimuth antenna pattern. The Time /Frequency support of such data is featuring a linear frequency modulation with time in RAW data but also in the Level-1 SLC data as generated by S-1 IPF.
The removal of this linear frequency modulation (also called deramping) is a key step for performing basic operation like interpolation/resampling of the data that are often used in the interferometric processing chains.
This document defines the procedure for performing the deramping of S-1 TOPS IWS and EWS of Level-1 SLC products generated by the S-1 IPF.
The document is available here: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/document-library/-/asset_publisher/1dO7RF5fJMbd/content/definition-of-the-tops-slc-deramping-function-for-products-generated-by-the-sentinel-1-ipf
This chapter presents the stochastic part of the Gauss-Markoff model for radar interferometry. It revisits the necessary theoretical concepts before focusing on the single-point statistics and multiple-point statistics. The quality of an interferogram is described in terms of the first and second moments. Error sources introduced by scattering, geometry, instrument, orbit, atmosphere, and signal processing are discussed. Models are proposed to describe atmospheric signal due to turbulent mixing and vertical stratification. Error propagation is used to derive a generic stochastic model.
Repeat pass interferometric synthetic aperture radar (InSAR) observations may be significantly impacted by the propagation delay of the microwave signal through the ionosphere, which is commonly referred to as ionospheric delay. The dispersive character of the ionosphere at microwave frequencies allows one to estimate the ionospheric delay from InSAR data through a split range-spectrum technique. Here, we extend the existing split range-spectrum technique to InSAR time-series. We present an algorithm for estimating a time-series of ionospheric phase delay that is useful for correcting InSAR time-series of ground surface displacement or for evaluating the spatial and temporal variations of the ionosphere's total electron content (TEC). Experimental results from stacks of L-band SAR data acquired by the ALOS-1 Japanese satellite show significant ionospheric phase delay equivalent to 2 m of the temporal variation of InSAR time-series along 445 km in Chile, a region at low latitudes where large TEC variations are common. The observed delay is significantly smaller, with a maximum of 10 cm over 160 km, in California. The estimation and correction of ionospheric delay reduces the temporal variation of the InSAR time-series to centimeter levels in Chile. The ionospheric delay correction of the InSAR time-series reveals earthquake-induced ground displacement, which otherwise could not be detected. A comparison with independent GPS time-series demonstrates an order of magnitude reduction in the root mean square difference between GPS and InSAR after correcting for ionospheric delay. The results show that the presented algorithm significantly improves the accuracy of InSAR time-series and should become a routine component of InSAR time-series analysis.