Sentinel-1 (S-1) has an unparalleled mapping capacity. In interferometric wide swath (IW) mode, three subswaths imaged in the novel Terrain Observation by Progressive Scans (TOPS) SAR mode result in a total swath width of 250 km. S-1 has become the European workhorse for large area mapping and interferometric monitoring at medium resolution. The interferometric processing of TOPS data however requires special consideration of the signal properties, resulting from the ScanSAR-type burst imaging and the antenna beam steering in azimuth. The high Doppler rate in azimuth sets very stringent coregistration requirements, making the use of enhanced spectral diversity (ESD) necessary to obtain the required fine azimuth coregistration accuracy. Other unique aspects of processing IW data, such as azimuth spectral filtering, image resampling, and data deramping and reramping, are reviewed, giving a recipe-like description that enables the user community to use S-1 IW mode repeat-pass SAR data. Interferometric results from S-1A are provided, demonstrating the mapping capacity of the S-1 system and its interferometric suitability for geophysical applications. An interferometric evaluation of a coherent interferometric pair over Salar de Uyuni, Bolivia, is provided, where several aspects related to coregistration, deramping, and synchronization are analyzed. Additionally, a spatiotemporal evaluation of the along-track shifts, which are directly related to the orbital/instrument timing error, measured from the SAR data is shown, which justifies the necessity to refine the azimuth shifts with ESD. The spatial evaluation indicates high stability of the azimuth shifts for several slices of a datatake.
Advanced Interferometric SAR (InSAR) technique, namely, Persistent Scatterer Interferometry (PSI), allows long term deformation time series analysis with millimeter accuracy. Reference network arcs construction, arcs estimation and integration for PSs are an important step in PSI. In rural regions, low density of PSs leads to separate clusters during reference network construction. Also, in case of wide-area PSI using ERS-1/2 or Sentinel-1 data, the computational load can be very high. Due to this, the reference network processing is usually divided into overlapping blocks and merged later. This can however lead to spatial error propagation. This paper presents algorithms for improving the reference network in wide-area PSI, with a focus on non-urban areas.
<p>A better understanding of the short term and seasonal peat surface vertical displacements (bog breathing) (Roulet 1991) initiated by changes in the water table is needed to improve spatial models of greenhouse gas emissions (Dise 2009). Synthetic Aperture Radar Interferometry (InSAR) is a promising tool for the task but accounting for relatively large peat surface displacements (Fritz 2006, Howie & Hebda 2018) may cause propagation of ambiguity errors and unreliability (Alshammari <em>et al</em>. 2018, Heuff & Hanssen 2021). This is usually overlooked and the absence of ground levelling data for validation is characteristic of InSAR research in peatlands (Cigna & Sowter 2017, Alshammari <em>et al</em>. 2018).</p><p>We calculated distributed scatterer (DS) time series over 2014&#8211;2020 for Sentinel-1 relative orbits number (RON) 80 (descending) and 160 (ascending). The high frequency continuous in situ ground levelling measurements cover the snow and ice-free period of 2016 (April&#8211;October). Limited by the availability of Sentinel-1 data, 13 images from both stacks were evaluated against the levelling of a hummock plot. DS points used in the comparison were located around the plot at 125&#8211;315 m. The bog points were referenced to the stable DS points from a nearby village (4 km away) to account for atmospheric effects. InSAR line of sight deformation results were projected to vertical dimension (u<sub>LOS</sub>).</p><p>Concerning only the dates when we had SAR acquisitions, the largest change relative to the maximum surface level of the period is -6.6 cm and median change -2.4 cm for RON 80, and -7.5 cm and -2.4 cm for RON 160. The maximum deviation between the u<sub>LOS </sub>and the levelling is 5.6 cm and median 2.11 cm for RON 80. For RON 160, the maximum deviation is 5.85 cm and median 2.81 cm. The Spearman correlation coefficient (r<sub>s</sub>) between the u<sub>LOS </sub>and the levelling is 0.84 for RON 80 and r<sub>s </sub>= 0.81 for RON 160 (<em>p</em>-value <em>< </em>0.001 in both cases).</p><p>To reduce the need for ambiguity resolution in the DS time series, we used relative changes between two consecutive acquisitions (baseline of 12 or 6 days) instead of accounting for the absolute change. The in situ relative surface changes between the consecutive acquisition dates of RON 80 are -2.55...2.1 cm (median -0.08 cm) and the deviation of the DS from the levelling is -1.17...1.28 cm (median 0.38 cm). For RON 160, levelling values are -0.9...3.3 cm (median -0.3 cm) and the deviation -3.06&#8230;0.81 cm (the former is -0.45 if the 12-day image pair corresponding to the change larger than the u<sub>LOS </sub>height of ambiguity is removed), median 0.23 cm. Between the levelling and DS data r<sub>s </sub>= 0.67 (<em>p</em>-value 0.035) and 0.77 (<em>p</em>-value 0.005), respectively for RON 80 and 160. Based on the in situ levelling, we demonstrated that 1) Sentinel-1 DS time series severely underestimate real surface changes over the bog and 2) despite a serious ambiguity problem, DS time series contain the useful signal because 6-day surface changes are relatively small and usually do not need ambiguity resolution.</p>
<p>TecVolSA (Tectonics and Volcanoes in South America) is a project dedicated to the development of an intelligent Earth Observation (EO) data exploitation system for monitoring various geophysical activities in South America. Three partners from the German Aerospace Center (DLR) and the German Research Centre for Geosciences (GFZ) are involved to combine their expertise in signal processing, geophysics and Artificial Intelligence (AI).</p><p>The first milestone of the project is to perform interferometric processing on tens of terabytes of SAR data to generate deformation products. Efficient algorithms have been designed to accommodate big data processing. Employing these algorithms, five-year data archives of Sentinel-1 have been processed thus far. The data archives span an area of over 770,000 km&#178; surrounding the central volcanic zone of the Andes. Products in the form of surface deformation velocity and displacement time series are generated as point-wise measurements. To ensure highly accurate deformation estimates, two novel techniques have been utilized: large-scale atmospheric correction and covariance-based phase estimation for distributed scatterers.</p><p>The second milestone is automatic mining of the wealth of the deformation products to gain insights about anthropogenic and geophysical signals in the region. Here two challenges are faced: the variety of crustal deformation processes as well as the sheer volume of the data. A closer analysis of the estimated deformation velocity verifies the presence of various signals including tectonic movements, volcanic unrest and slope-induced deformations. Such variety requires the classification of the observed signals. Furthermore, the dataset includes displacement time series and velocity estimates of over 750 million data points. This data volume necessitates the incorporation of AI for efficient mining of the products. The aforementioned challenges are met by combining geophysical and signal processing expertise of the project partners, and translating them to the AI algorithms.</p><p>The use of AI in EO is a growing topic with numerous successful applications. However, compared to the well-established AI applications of cartography and ground cover classification, there is not enough training data available for the analysis of tectonic and volcanic signals. Therefore, there is a need for synthetic data generation. GFZ produces geophysical models for the simulation of a diverse database that is used for the training of neural networks to autonomously discover significant events in deformation products.</p><p>DLR employs supervised machine learning techniques based on simulated data to automatically detect volcanic deformation from InSAR products. Apart from this application, signals which are not attributed to volcanic deformation are automatically clustered for further studies by expert geologists. For this approach, we depend on InSAR and geometrical feature engineering as well as advanced unsupervised learning algorithms. In the presentation, examples of clustering similar points in terms of temporal progression and a prototype system for the automatic detection of volcanic deformations will be illustrated.</p><p>Our system is being developed with scalability and transferability in mind. South America serves as a generic and challenging case for this development, as it reveals manifold geophysical and anthropogenic signals. Our ultimate goal is to apply the developed AI-assisted system for global processing.</p><p>&#160;</p>
Accounting for relatively large seasonal and short term peatland surface vertical displacements with Synthetic Aperture Radar Interferometry (InSAR) poses a problem of possible propagation of ambiguity errors. Notwithstanding, the absence of continuous high temporal resolution peatland surface levelling measurements for validation has been something characteristic. Based on the ground levelling from a raised bog, we demonstrate the Sentinel-1 distributed scatterer (DS) time-series InSAR technique underestimates real surface displacements and hereby we question the accuracy of the approach over peatlands. When the relative surface change from 6-day interferograms is used instead of accounting for the absolute change, the estimation accuracy improves (Spearman's rho 0.82, p-value < 0.002) because 6-day in situ surface changes are usually small and do not need InSAR phase unwrapping. Despite a serious unwrapping problem in peatlands, DS time series contain useful signal and differential InSAR (DInSAR) might have potential for assessment of short term peatland surface displacements in favourable conditions.
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