Scalloping Correction in TOPS Imaging Mode SAR Data

This paper presents an investigation on scalloping correction in the TOPS imaging mode for SAR systems with electronically steered phased array antennas. A theoretical simulation of the scalloping is performed and two correction methods are introduced. The simulation is based on a general cardinal sine (sinc) antenna model as well as on the TerraSAR-X antenna model. Real TerraSAR-X data acquired over rainforest are used for demonstration and verification of the scalloping simulation and correction. Furthermore a calibration approach taking into account the special TOPS imaging mode properties is introduced

Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

A Network-Based Enhanced Spectral Diversity Approach for TOPS Time-Series Analysis

For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6-2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD

Processing of Sliding Spotlight and TOPS SAR Data Using Baseband Azimuth Scaling

This paper presents an efficient phase preserving processor for the focusing of data acquired in sliding spotlight and TOPS (Terrain Observation by Progressive Scans) imaging modes. They share in common a linear variation of the Doppler centroid along the azimuth dimension, which is due to a steering of the antenna (either mechanically or electronically) throughout the data take. Existing approaches for the azimuth processing can become inefficient due to the additional processing to overcome the folding in the focused domain. In this paper a new azimuth scaling approach is presented to perform the azimuth processing, whose kernel is exactly the same for sliding spotlight and TOPS modes. The possibility to use the proposed approach to process ScanSAR data, as well as a discussion concerning staring spotlight, are also included. Simulations with point-targets and real data acquired by TerraSAR-X in sliding spotlight and TOPS modes are used to validate the developed algorithm

Direct comparison of sea surface velocity estimated from Sentinel-1 and TanDEM-X SAR data

This paper presents the first direct comparison of the sea surface radial velocity (RVL) derived from the two satellite SAR systems Sentinel-1 and TanDEM-X, operating at different frequencies and imaging modes. The RVL is derived from the Doppler centroid (Dc) provided in the Sentinel-1 OCN product and from the along-track interferometric phase of the TanDEM-X. The comparison is carried out using opportunistic acquisitions, collocated in space and time, over three different sites. First, it is observed that the RVL derived from both satellites is biased, thus calibration is applied using the land as a reference. The comparison shows that the correlation and the mean RVL bias between the two datasets depend on the differences in acquisition time, incidence angle and azimuth angle, and on wind and current speed and direction. It is found that, given a time difference of < 20 min, the spatial correlation coefficient is relatively high (between 0.7 and 0.93), which indicates that the two SAR systems observe similar sea surface current fields. The spatial correlation degrades primarily due to increasing time difference and decreasing current magnitudes. The mean RVL bias increases primarily with the radial wind speed, which suggests that the RVL bias is mainly due to the wave-induced Doppler shift. This study shows that under certain conditions, i.e. similar acquisition geometry and short time delay, a good agreement between the two independently derived RVL is achieved. This encourages a synergistic use of the sea surface velocity estimated from different C- and X-band SAR systems

First multi-year assessment of Sentinel-1 radial velocity products using HF radar currents in a coastal environment

Direct sensing of total ocean surface currents with microwave Doppler signals is a growing topic of interest for oceanography, with relevance to several new ocean mission concepts proposed in recent years. Since 2014, the spaceborne C-band SAR instruments of the Copernicus Sentinel-1 (S1) mission routinely acquire microwave Doppler data, distributed to users through operational S1 Level-2 ocean radial velocity (L2 OCN RVL) products. S1 L2 RVL data could produce high-resolution maps of ocean surface currents that would benefit ocean observing and modelling, particularly in coastal regions. However, uncorrected platform effects and instrument anomalies continue to impact S1 RVL data and prevent direct exploitation. In this paper, a simple empirical method is proposed to calibrate and correct operational S1 L2 RVL products and retrieve two-dimensional maps of surface currents in the radar line-of-sight. The study focuses on the German Bight where wind, wave and current data from marine stations and an HF radar instrumented site provide comprehensive means to evaluate S1 retrieved currents. Analyses are deliberately limited to Sentinel-1A (S1A) ascending passes to focus on one single instrument and fixed SAR viewing geometry. The final dataset comprises 78 separate S1A acquisitions over 2.5 years, of which 56 are matched with collocated HF radar data. The empirical corrections bring significant improvements to S1A RVL data, producing higher quality estimates and much better agreement with HF radar radial currents. Comparative evaluation of S1A against HF radar currents for different WASV corrections reveal that best results are obtained in this region when computing the WASV with sea state rather than wind vector input. Accounting for sea state produces S1 radial currents with a precision (std of the difference) around 0.3 m/s at ∼1 km resolution. Precision improves to ∼0.24 m/s when averaging over 21 × 27 km2, with correlations with HF radar data reaching up to 0.93. Evidence of wind-current interactions when tides and wind align and short fetch conditions call for further research with more satellite data and other sites to better understand and correct the WASV in coastal regions. Finally, 1 km resolution maps of climatological S1A radial currents obtained over 2.5 years reveal strong coastal jets and fine scale details of the coastal circulation that closely match the known bathymetry and deep-water coastal channels in this region. The wealth of oceanographic information in corrected S1 RVL data is encouraging for Doppler oceanography from space and its application to observing small scale ocean dynamics, atmosphere and ocean vertical exchanges and marine ecosystem response to environmental change

Analysis and evaluation of Terrain Observation by Progressive Scans (TOPSAR) mode in Synthetic Aperture Radar

Synthetic Aperture Radar (SAR) is a technique used in radar (RAdio Detection And Ranging) [1] systems to get high resolution images which are impossible to obtain with a conventional radar. This method allows us to acquire images from the surface of the Earth or other planets from large distances. In SAR [2], a single antenna is used to get information of the targets, and the platform movement, where the antenna is fixed, is used to spread the Doppler history of received echoes improving the resolution of processed images. Remote sensing is a wide area which studies different techniques to acquire information about targets situated at far distances. These techniques can be classified in two different areas according to their basic operation. The first group, called passive remote sensing [3] [4], uses passive sensors to acquire the energy radiated by the targets. This energy can come from an external source, such as Sun radiation, being reflected by the object or it can be emitted by the target itself. On the other hand, active remote sensing systems [4] emit pulses to illuminate the scanned area, providing their own energy. So, although it requires a more complex system, active sensing does not require an external source to operate which is an advantage when the conditions are not favourable. SAR and other radar techniques are examples of active sensors, working at frequencies between 0.3 GHz and 300GHz. These systems send pulses towards the scanned area, the interaction of each pulse with the surface originates an echo which arrives to the receiver. This echo is originated by the energy backscattered by the objects in the scene and it will be dependant of the backscattering profile of the targets (radar cross-section) [5] [6]. The time delay and strength of power received as well as frequency properties of the returns are processed to determine the target locations and characteristics. Synthetic aperture is similar to a conventional real aperture radar (RAR) antenna but it is achieved by signal processing. In a SAR, the antenna, installed in a moving platform, sends pulses to the scene and receives backscattered returns. The movement of the platform makes possible to illuminate the targets at different positions of the satellite trajectory, which is equivalent to have multiple antennas illuminating the scene at the same time. Thus, SAR is a fairly recent acquisition method that has some advantages in comparison with other remote sensing techniques. The most significant are: · Day/Night and all weather condition imaging since it does not depend on external power sources to detect the targets. · Geometric resolution independent of altitude or wavelength. · Signal data characteristic unique to the microwave region of EM spectrum which has suffers less deterioration in atmosphere propagation. The SAR systems started with aero-transported missions and later, first space missions were sent. The SAR beginning dates back to 1951 when C. Wiley postulated the Dopple