Spaceborne Synthetic Aperture Radar Missions Research Articles

Unified Framework for Ship Detection in Multi-Frequency SAR Images: A Demonstration with COSMO-SkyMed, Sentinel-1, and SAOCOM Data

In the framework of maritime surveillance, vessel detection techniques based on spaceborne synthetic aperture radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. This paper deals with this topic, presenting a novel approach that exploits a cascade application of a pre-screening algorithm and a discrimination phase. Pre-screening is based on a constant false alarm rate (CFAR) detector, whereas discrimination exploits sub-look analysis (SLA). For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images, demonstrating a significant reduction of up to 40% in false alarms within highly congested scenarios, along with a notable enhancement of the receiving operating characteristic (ROC) curves. For future synergic exploitation of multiple SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM, and COSMO-SkyMed images, is comprehensive, having images gathered over the same area with a short time lag (below 15 min). Finally, the diversified processing chains and the results for each mission product and scenario are discussed. Being the first dataset of single-look complex (SLC) SAR multi-frequency data, the present work intends to encourage additional investigation in this promising field of research.

Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube

Spaceborne Synthetic Aperture Radar (SAR) are well-established systems for flood mapping, thanks to their high sensitivity towards water surfaces and their independence from daylight and cloud cover. Particularly able is the 2014-launched Copernicus Sentinel-1 C-band SAR mission, with its systematic monitoring schedule featuring global land coverage in a short revisit time and a 20 m ground resolution. Yet, variable environment conditions, low-contrasting land cover, and complex terrain pose major challenges to fully automated flood monitoring. To overcome these issues, and aiming for a robust classification, we formulate a datacube-based flood mapping algorithm that exploits the Sentinel-1 orbit repetition and a priori generated probability parameters for flood and non-flood conditions. A globally applicable flood signature is obtained from manually collected wind- and frost-free images. Through harmonic analysis of each pixel’s full time series, we derive a local seasonal non-flood signal comprising the expected backscatter values for each day-of-year. From those predefined probability distributions, we classify incoming Sentinel-1 images by simple Bayes inference, which is computationally slim and hence suitable for near-real-time operations, and also yields uncertainty values. The datacube-based masking of no-sensitivity resulting from impeding land cover and ill-posed SAR configuration enhances the classification robustness. We employed the algorithm on a 6-year Sentinel-1 datacube over Greece, where a major flood hit the region of Thessaly in 2018. In-depth analysis of model parameters and sensitivity, and the evaluation against microwave and optical reference flood maps, suggest excellent flood mapping skill, and very satisfying classification metrics with about 96% overall accuracy and only few false positives. The presented algorithm is part of the ensemble flood mapping product of the Global Flood Monitoring (GFM) component of the Copernicus Emergency Management Service (CEMS).

An Advanced Echo Separation Scheme for Space-Time Waveform-Encoding SAR Based on Digital Beamforming and Blind Source Separation

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

An Extended Model of Ionospheric Dispersion Effects for Nonlinear Frequency Modulation Signal and Correction Method

Nonlinear frequency modulation (NLFM) signal can construct the signal’s power spectral density to reduce sidelobes without loss of signal-to-noise ratio. LuTan-1 (LT-1) is an L-band spaceborne synthetic aperture radar mission which is launched in the beginning of 2022, and a high-precision NLFM signal generator is developed in LT-1. However, the existing model, i.e., the traditional frozen ionosphere model, can not accurately describe ionospheric dispersion effects faced by the NLFM signal due to the non-linear characteristic of the instantaneous frequency. Thus, an extended model is established in this paper to describe ionospheric dispersion effects of the NLFM signal. Then, the differences of ionospheric dispersion effects on the NLFM and linear frequency modulation signals are compared. Afterwards, a method that embedded into the focusing procedure is proposed, which aims to eliminate ionospheric dispersion effects for the NLFM signal. Finally, the hardware-in-the-loop simulations of point targets and distributed targets are performed to verify the proposed method. The method proposed in this paper is used in the ground processing system of LT-1.

An Advanced Scheme for Range Ambiguity Suppression of Spaceborne SAR Based on Blind Source Separation

Due to the minimum antenna area constrain of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Insipred by the cocktail party effect, an advanced scheme based on blind source separation is put forward to suppress the range ambiguity of spaceborne SAR in this paper. In the scheme, multiple subantennas are used to collect multiple echo data sets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the blind source separation algorithm is proposed to separate the desired echoes from those data sets. To verify the scheme, point target and scene simulations based on GF3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions.

Application of C-band sentinel-1A SAR data as proxies for detecting oil spills of Chennai, East Coast of India

This paper presents the utilization of Synthetic Aperture Radar (SAR) data for monitoring and detection of oil spills. In this work, a case study of an oil spill has been investigated using C-band Sentinel-1A SAR data to detect the oil spill that occurred on 28 January 2017, near Ennore port, Chennai, India. Oil spill damages marine ecosystems causing serious environmental effects. Quite often, oil spills on the sea/ocean surface are seen nowadays, mainly in major shipping routes. They are caused due to tanker collisions, illegal discharge from the ships, etc. An oil spill can be monitored and detected using various platforms such as vessel-based, airborne-based and satellite-based. Vessel based and airborne methods are expensive with less area coverage. This process also consumes more time. For ocean applications such as oil spill and Ship detection, optical sensors cannot image during bad weather. As SAR is an active sensor, weather independent, and has cloud penetrating capability, the images can be acquired during the day as well as at night. Radar Remote Sensing (RRS) has rapidly gained popularity for monitoring and detection of oil spills and ships for more than a decade. With the availability of the satellite images, detection of oil spill has improved due to its wide coverage and less revisit time. The present paper gives an overview of the methodologies used to detect oil spills on the SAR images using dual-pol Sentinel-1A Level 1 SLC data. This work clearly demonstrates the preprocessing steps of the Sentinel 1A data for oil spill detection. The oil spill was only visible in the VV channel, therefore, for ocean application VV channel image is preferred. SEASAT was the first space-borne SAR mission launched in 1978 by NASA to observe sea surface. The preprocessing was carried out at the European Space Agency (ESA), the Sentinel Application Platform (SNAP) toolbox and Envi 5.1 toolbox. Based on the Sigma naught values, oil spill can be discriminated with the ocean surface. The results obtained with the VV channel are satisfactory and one could map out the oil spill very well. Supervised classifiers SVM and NN were applied on the boxcar filtered 3 × 3 VV channel image to delineate the oil spill. The result of oil spill detection mapping is validated with Supervised SVM and Neural Network classifiers. The results show there is a good agreement between oil spill mapping and classified image using SVM and NN classified images. The Overall Accuracy (OA) obtained using SVM classifier is 98.13% with kappa coefficient as 0.95 and using NN classifier is 98.11% with kappa coefficients 0.95. This technique is considered to be a potential proxy for the detection and monitoring of Oil spills on water bodies. Application of SAR data for oil spill detection is considered to be first of its kind from Indian coasts. This study aims to detect the oil spill occurred due to collision of two LPG tankers with Sentinel-1A SLC data in Chennai coast area.

Evaluating NISAR's cropland mapping algorithm over the conterminous United States using Sentinel-1 data

Accurate knowledge of the distribution, breadth and change in agricultural activity is important to food security and the related trade and policy mechanisms. Routine observations afforded by spaceborne Synthetic Aperture Radar (SAR) allows for high-fidelity monitoring of agricultural parameters at the field scale. Here we evaluate the approach to be used for generating NASA's upcoming NASA ISRO SAR (NISAR) mission's L-band cropland product using Sentinel-1C-band data. This study uses all ascending Sentinel-1A/B data collected over the conterminous United States in 2017 to compute the coefficient of variation (CV) at 150 m × 150 m resolution and evaluates the overall accuracy (OA) of CV-based crop/non-crop classifications at 100 one-by-one degree tiles. We calculate accuracies using two approaches: (a) using a literature-recommended constant CV threshold of 0.5 (CVthr_0.5) and (b) determining optimal CV thresholds for every tile using Youden's J statistic (YJS), CVthr_YJS. These accuracy comparisons are important for determining (1) the viability of using a computationally inexpensive and straightforward approach for cropland classification over large spatial scales/diverse land covers (i.e., can accuracies ≥80% be routinely achieved?), (2) how closely OA0.5 compares to the performance ceiling (OAYJS). This information will help determine whether approach (a) is appropriate and how much potential room of improvement there could be in modifying it. Results for OA0.5 and OAYJS are 81.5% and 86.8%, respectively. A breakdown by census geographic region, showed that OA0.5 (OAYJS) exceeded 80% (90%) in the South and Midwest, but was only 76.1% (73.5%) in the West. The improvement in OAYJS mainly stems from tiles with >40% crop prevalence having about 10% greater OA values. To better examine the potential of the approach for land cover classification, results of approach (b) were also stratified by crop. Approach (b) accurately detected most non-crop classes as non-crop (>80%), but with low OAYJS values for grasslands/pasture, especially in the West. CV values for crop were distinct from non-crop indicating that the approach is suitable for crop/non-crop classifications. Because results CV values have substantial overlap within crop/non-crop classes, indicating the approach is poorly suited for land cover classifications. We also detected a strong geographic dependence of CVthr_YJS: values ranged from about 0.2 at the coasts and gradually increase to about 0.6 in the Central United States, most often falling close to 0.3 and 0.5.

Channel characterization of a dual-band dual-polarized SAR with digital beamforming

AbstractThis paper presents the integration and channel characterization of a highly integrated dual-band digital beamforming space-borne synthetic aperture radar (SAR) receiver. The proposed SAR sensor is a low-cost, lightweight, low-power consumption, and dual-band (X/Ka) dual-polarized module ready for the next-generation space-borne SAR missions. In previous works, by the authors, the design and experimental characterization of each sub-system was already presented and discussed. This work expands upon the previous characterization by providing an exhaustive experimental assessment of the fully integrated system. As it will be shown, the proposed tests were used to validate all the instrument channels in a set-up where the SAR sensor was illuminated by an external source minim the ground reflected waves. Test results demonstrate how the system channels are properly operating allowing the reception of the input signals and their processing in the digital domain. The possibility to easily implement a calibration procedure has also been validated to equalize, in the digital domain, the unavoidable amplitude differences between the different channels.

Calibration of a Ground-Based Array Radar for Tomographic Imaging of Natural Media

Ground-based tomographic radar measurements provide valuable knowledge about the electromagnetic scattering mechanisms and temporal variations of an observed scene and are essential in preparation for space-borne tomographic synthetic aperture radar (SAR) missions. Due to the short range between the radar antennas and a scene being observed, the tomographic radar observations are affected by several systematic errors. This article deals with the modelling and calibration of three systematic errors: mutual antenna coupling, magnitude and phase errors and the pixel-variant impulse response of the tomographic image. These errors must be compensated for so that the tomographic images represent an undistorted rendering of the scene reflectivity. New calibration methods were described, modelled and validated using experimental data. The proposed methods will be useful for future ground-based tomographic radar experiments in preparation for space-borne SAR missions.

Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km 2 at incidence angles below 15 ∘ . At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments.

Research Pathways of Forest Above-Ground Biomass Estimation Based on SAR Backscatter and Interferometric SAR Observations

Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evaluated primarily (i) L-band observations of SAR backscatter; and, (ii) single-image or multi-polarized retrieval schemes. The use of multi-temporal or multi-frequency data improved the biomass estimates when compared to single-image retrieval. Low frequency SAR backscatter contributed the most to the biomass estimates. Single-pass InSAR height was reported to be a more reliable predictor of biomass, overcoming the loss of sensitivity of SAR backscatter and coherence in high biomass forest. A variety of empirical and semi-empirical regression models relating biomass to the SAR observables were proposed. Semi-empirical models were mostly used for large-scale mapping because of the simple formulation and the robustness of the model parameters estimates to forest structure and environmental conditions. Non-parametric models were appraised for their capability to ingest multiple observations and perform accurate retrievals having a large number of training samples available. Some studies argued that estimating compartment biomass (in stems, branches, foliage) with different types of SAR observations would lead to an improved estimate of total biomass. Although promising, scientific evidence for such an assumption is still weak. The increased availability of free and open SAR observations from currently orbiting and forthcoming spaceborne SAR missions will foster studies on forest biomass retrieval. Approaches attempting to maximize the information content on biomass of individual data streams shall be pursued.

Staggered SAR: Performance Analysis and Experiments With Real Data

Synthetic aperture radar (SAR) remote sensing allows high-resolution imaging independent of weather conditions and sunlight illumination and is therefore very attractive for the systematic observation of dynamic processes on the earth’s surface. Conventional SAR systems are, however, limited in that a wide swath can only be imaged at the expense of a degraded azimuth resolution. This limitation can be overcome by using systems with multiple receive subapertures displaced in along-track, but a very long antenna is required to map a wide swath. If a relatively short antenna with a single aperture in along-track is available, it is still possible to map a wide area: multiple subswaths can simultaneously be imaged using digital beamforming in elevation, but “blind ranges” are present between adjacent swaths, as the radar cannot receive while it is transmitting. Staggered SAR overcomes the problem of blind ranges by continuously varying the pulse repetition interval (PRI). A proper selection of the PRIs, together with moderate oversampling in azimuth, allows an accurate interpolation of the nonuniformly sampled raw data into a uniform grid, so that resampled data can then be focused with a conventional SAR processor. This approach thereby allows high-resolution imaging of a wide continuous swath without the need for a long antenna with multiple subapertures. In this paper, the performance of staggered SAR is thoroughly discussed and novel methods for the evaluation of the range and azimuth ambiguity-to-signal ratios in staggered SAR are proposed. An L-band design example based on a reflector antenna with multiple feeds shows that outstanding ambiguity performance is obtained, provided that data are moderately oversampled in azimuth. As an additional benefit, the energy of range and azimuth ambiguities is spread over large areas: ambiguities therefore appear in the image as a noise-like disturbance rather than localized artifacts. The impact of staggered SAR operation on image quality is furthermore assessed with experiments using real data. As the first step, highly oversampled F-SAR airborne data have been used to generate equivalent staggered SAR data sets and to evaluate the performance for different oversampling factors and interpolation methods. Then, the radar instrument of the German satellite TerraSAR-X has been commanded to acquire data over the lake Constance in staggered SAR mode. Measurements on these data show very good agreement with predictions from simulations. Staggered SAR is currently being considered as the baseline acquisition mode for Tandem-L, a proposal for a polarimetric and interferometric spaceborne SAR mission to monitor dynamic processes on the earth’s surface with unprecedented accuracy and resolution.

Volume Decorrelation Effects in TanDEM-X Interferometric SAR Data

Among the several factors that may affect the quality of interferometric synthetic aperture radar (SAR) products, volume decorrelation represents the coherence loss contribution due to the presence of multiple scatterers within a single resolution cell, which results in an increase in the interferometric phase uncertainty. In this letter, we investigate the effects of volume decorrelation on X-band TanDEM-X interferometric data. TanDEM-X is the first bistatic spaceborne SAR mission and provides a unique, global, and manifold interferometric data set to be exploited for a variety of scientific and commercial applications. The main goal of this letter is to provide the scientific community with a characterization of volume decorrelation effects occurring at $\mbox {X-band}$ for different land cover types and acquisition geometries. The potentials of volume decorrelation contribution at X-band for land classification are discussed as well and some application examples are presented.

Evaluating Moisture and Geometry Effects on L-Band SAR Classification Performance Over a Tropical Rain Forest Environment

Multitemporal single (HH) and dual-polarization (i.e., HH, HV) L-band spaceborne synthetic aperture radar (SAR) scenes were evaluated under different moisture conditions caused by precipitation prior to data acquisition at varying incidence angles. The changes affecting backscattering intensity, polarimetric decomposition, backscattering mechanism, and land use/land cover classification performance were evaluated. The study area is a shifting-cultivation environment in the eastern Amazon (Brazil). Several data input scenarios were proposed in the classification scheme (i.e., backscattering intensity alone and combined with alpha/entropy decomposition parameters, band ratios, and textural parameters) using a random forest classifier framework. Integration with optical data was also examined. The classification accuracy scores were then compared with accumulated precipitation data. The results showed that the variation in both the vegetation moisture and incidence angle increases the backscattering intensity for pasture, riparian forest and young regenerated forest by at least 1 dB compared with old successional forest stages due to its more uniform vertical structure and the landscape's increased dielectric constant. The overall classification accuracy proved low for each SAR acquisition date compared with the performance of the Landsat data. Based on SAR data, misclassification occurs for the young successional forest stages and increases in scenes with higher moisture conditions. The classification performance benefits from data integration only for one SAR scene acquired in the dry season. The results highlight the importance of selecting proper temporal intervals for the different SAR polarization modes of the forthcoming SAR missions. Further investigations should address both multitemporal at a single frequency as well as multifrequency SAR approaches.

Quantization Effects in TanDEM-X Data

TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) is an innovative spaceborne bistatic SAR system comprising the twin satellites TerraSAR-X and TanDEM-X (TSX and TDX, respectively). The primary objective of the mission is the generation of a worldwide, timely, and consistent digital elevation model (DEM) in a bistatic synthetic aperture radar (SAR) configuration with unprecedented accuracy. For TanDEM-X and for future spaceborne SAR missions, an increasing volume of onboard data is going to be demanded. This is due to the employment of large bandwidths, high pulse repetition frequencies, and multiple polarizations, which implies inevitably hard requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect. The data rate employed for the digitization of the recorded radar signal affects both the amount of data to be stored and transmitted to the ground and the quality of the resulting SAR products. In this paper, the impact of quantization on TanDEM-X monostatic and interferometric data is evaluated. Key quantities in estimating interferometric and SAR performance, such as coherence and phase errors, are investigated in detail. Based on the obtained results, an optimization of the resource-allocation strategy for the global DEM acquisition of TanDEM-X is discussed.

Generation and Calibration of High-Resolution DEM From Single-Baseline Spaceborne Interferometry: The “Split-Swath” Approach

This paper focuses on the generation of very high resolution digital elevation models (DEMs) mainly from single-baseline spaceborne synthetic aperture radar (SAR) systems. The stringent requirements on vertical accuracy demand for frequent and accurate measures of the baselines, subject to mechanical vibrations of the boom that may be severe when long booms are exploited. This paper proposes a solution that exploits an existent coarse-resolution DEM, for example, the Shuttle Radar Topography Mission, as a set of ground control points (GCPs). A computational model and an inversion scheme are introduced to jointly take into account the information coming from the grid of GCP and the local shifts measured during image coregistration. The model properly takes account for the quality of the GCP, its location with respect to the target of interest, and the interferogram noise in correspondence to the GCP. A novel “split-swath” approach is proposed to enhance the quality of the baseline estimates. The mode exploits two narrow strips with significant diversity in incidence angle, achieved, for example, by ScanSAR or TOPSAR mode. An example of design is provided by assuming a Ka-band topographic spaceborne SAR mission.

Very-High-Resolution Airborne Synthetic Aperture Radar Imaging: Signal Processing and Applications

During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions.

Development of a Global Backscatter Model in support to the Sentinel-1 mission design

The high data throughput of spaceborne Synthetic Aperture Radar (SAR) instruments introduces stringent requirements on the end-to-end system, from the on-board storage capacity and data downlink to processing power and archiving capacity in the ground segment. An efficient raw data compression scheme on-board the satellite is therefore required to maximise the return on investment for SAR missions. The next generation of European SAR satellites, the Sentinel-1 mission, will employ the Flexible Dynamic Block Adaptive Quantization (FDBAQ) compression scheme. In order to validate the implementation of the FDBAQ, expected radar reflectivity levels for the global land surface were needed.The paper presents the development of the Global Backscatter Model (GBM) which has been used to simulate realistic radar reflectivity acquisitions in support of performance validation of the Sentinel-1 FDBAQ implementation. The GBM consists of a parameter database and SAR image simulation software. The model parameter database characterises C-band radar reflectivity as a function of local incidence angle with a resolution of 1km for 85% of the global land surface. The parameter database was derived from a global multi-temporal coverage of ENVISAT Advanced Synthetic Aperture Radar (ASAR) data. The SAR image simulation software uses the parameter database together with orbit propagation and swath determination algorithms in order to account for the instrument acquisition geometry and the location specific backscattering characteristics. The model has the potential to simulate acquisitions for other SAR instruments. Furthermore, the parameter database could be a valuable source of information for global land cover studies.

A Space Adaptive Quantizer for Spaceborne SAR

A space adaptive flexible block quantizer (SA-FBQ), suited for spaceborne synthetic aperture radar missions, is presented. This quantizer is actually an extension of the flexible dynamic block adaptive quantizer (FDBAQ), proposed in , which, in turn, is an extension of the block adaptive quantizer (BAQ). The BAQ is the optimal quantizer for a homogeneous target. The FDBAQ gets better performances on heterogeneous targets by adaptively selecting the best BAQ according to the local signal-to-thermal-noise ratio: The worst the SNTR, the lower the quantizer rate. The quantizer selection is precomputed in a lookup table (LUT), by assuming a fixed and known probability distribution function (pdf) of the reflectivity σ0. The SA-FBQ extends further this concept allowing the reflectivity pdf to vary, coping with this by exploiting many LUTs (i.e., quantizer set), each adapted to the local statistics. In this paper, we introduce an algorithm to adaptively find the best set of quantizers constrained on the mean bit rate; we discuss the implementation of the SA-FBQ, and we estimate its performances in comparison with the FDBAQ and the BAQ under different scenarios. Preliminary results are shown by exploiting the worldwide mosaic of C-band reflectivity derived from European Space Agency ENVISAT data and Sentinel-1 system parameters.

Tight formation flying for an along-track SAR interferometer

While space-borne synthetic aperture radar (SAR) has evolved into a mature technology over the past two decades, there is a growing interest in interferometric SAR applications. Especially along-track interferometry with its capability to resolve the velocity of on-ground objects and ocean currents is of high interest for scientific applications. The accuracy of the resolved velocity on ground scales directly with the along-track separation between adjacent SAR antennas. Since space vehicles are quite limited in size, a formation flying approach with two SAR instruments distributed onto two spacecraft thus appears to be an innovative approach to along-track SAR interferometry. In the framework of an ESA study, this paper discusses the potential benefits, drawbacks and problems associated with a close formation flight for an along-track interferometry SAR mission. To this end, the absolute and relative orbit reconstruction requirements for the SAR processing chain are derived from basic interferometric principles as well as appropriate baselines of the satellite formation in L-Band and X-Band. A discussion of potential space-borne navigation sensors is presented along with the accuracy of state-of-the-art relative orbit reconstruction. Finally, appropriate thrusters for formation acquisition and control are discussed together with approaches to formation flying guidance and control as well as fuel consumption.