Synthetic Aperture Radar Missions Research Articles

Incidence angle dependency and seasonal evolution of L and C-band SAR backscatter over landfast sea ice

Abstract We estimate sea-ice type specific incidence angle (IA) dependencies for dual polarized (HH/HV) L and C-band synthetic aperture radar (SAR) for the winter, melt onset and advanced melt seasons for level and deformed ice, using time-series of Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1 imagery off the north-east coast of Greenland. The IA dependencies are used to radiometrically correct the L and C-band backscatter time-series, which enables analysis of their seasonal evolution. From this, we observe that the L-band backscatter intensity increases for both ice types at the transition from winter to melt onset. We use these results to estimate ice type separability and to train an IA aware Bayesian classifier at both frequencies. These results show that while both frequencies are capable of distinguishing level and deformed ice during the winter, only L-band SAR can reliably make this separation during the melt onset season. During the advanced melt season, the overall classification accuracies are similarly low for L and C-band. This study demonstrates the potential of L-band SAR for sea-ice mapping, which is highly relevant in the light of several upcoming L-band SAR missions.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics

Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging is essential for analyzing sea scenes, and the accuracy of sea surface and scattering models is crucial for effective SAR simulations. Traditional methods typically employ empirical formulas to fit sea surface scattering, which are not closely aligned with the principles of electromagnetic scattering. This paper introduces a novel approach by constructing multiple sea surface models based on the Pierson–Moskowitz (P-M) sea spectrum, integrated with the stereo wave observation projection (SWOP) expansion function to thoroughly account for the influence of wave fluctuation characteristics on radar scattering. Utilizing the shooting and bouncing ray-physical optics (SBR-PO) method, which adheres to the principles of electromagnetic scattering, this study not only analyzes sea surface scattering characteristics under various sea conditions but also facilitates the computation of scattering coupling between multiple targets. By constructing detailed scattering distribution data, the method achieves high-precision SAR simulation results. The scattering model developed using the SBR-PO method provides a more nuanced description of sea surface scenes compared to traditional methods, achieving an optimal balance between efficiency and accuracy, thus significantly enhancing sea surface SAR imaging simulations.

Advanced modern foreign mini- and microsatellite space synthetic aperture radar missions

Advanced modern foreign mini- and microsatellite space synthetic aperture radar missions

Utilising Sentinel-1's Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter.

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite's high orbital stability. By propagating Sentinel-1's orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10-50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.

A multi-scale algorithm for the NISAR mission high-resolution soil moisture product

This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m3/m3] with a bias of −0.051 [m3/m3]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m3/m3] and a low bias of 0.016 [m3/m3]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m3/m3] with an average bias of <0.07 [m3/m3]. The result confirms that the proposed algorithm meets the NISAR mission's accuracy goals, i.e., 0.06 [m3/m3] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m2]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.

An Advanced Non-Interrupted Internal Calibration Model Based on Azimuth Modulation and Waveform Diversity for SAR Systems

Internal calibration, designed for obtaining the gain and phase errors introduced by the fluctuations of the electronic instruments, is an essential operation to guarantee the imaging performance of modern synthetic aperture radar (SAR) systems. However, in most of the current internal calibration schemes, the internal calibration processes interrupt the normal SAR data acquisition, which results in a loss of azimuth samples and degradation of the SAR image quality. To this end, an advanced non-interrupted internal calibration model is proposed, performing the internal calibration during nominal data acquisitions. The calibration signals and echoes are orthogonal and are received simultaneously. Then, they can be separated by postprocessing via the waveform diversity concept. Two schemes based on the proposed model are demonstrated via theoretical analysis and simulation experiments. The proposed model can achieve a considerable signal-to-noise ratio (SNR) and reduce the influence of calibration signals on echoes, which have some potential applications for future SAR missions.

Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection

Across Canada, farmers are encouraged to adopt beneficial management practices (BMPs) to protect soil heath, reduce green house gas emissions and mitigate off-site impacts from agriculture. Measuring the uptake of BMPs, including the implementation of conservation tillage, helps gauge the success of policies and programs to promote adoption. Satellites are one way to monitor BMP adoption and Synthetic Aperture Radars (SARs) are of particular interest given their all-weather data collection capability. This research investigated coherent change detection (CCD) to determine when farmers harvest and till their fields. A time series of both Sentinel-1 and RADARSAT Constellation Mission (RCM) images was acquired over a site in the Canadian Lake Erie basin, during the autumn of 2021, when farmers were harvesting and tilling fields of corn, soybeans and wheat. 16 CCD pairs were created and coherence values were interpreted based on observations collected for 101 fields. An m-chi decomposition was applied to the RCM data, and the Volume/Surface (V/S) ratio was calculated as an additional source of information to interpret results. Change events due to harvest, tillage, autumn seeding and chemical termination resulted in coherence values below 0.20. The mean and standard deviation for fields with observed change was 0.18 ± 0.03. Coherence values were 0.42 ± 0.15 for fields where no change was noted. Tests confirmed that the coherence associated with changed and unchanged fields was significantly different. Coherence values could also differentiate between some types of management events, including tillage and harvest. CCD could also separate harvest as a function of crop type (corn or soybeans). V/S ratios declined after tillage events but increased after both harvesting and chemical termination. Narrowing the date of harvest and tillage is as important as detecting change. To meet this requirement, Sentinel-1 and RCM CCD products with values below 0.20 (indicating change had occurred), were graphically overlaid. With this approach, the timing of corn harvest was identified as occurring within a 5-day window. The tilling of corn, soybeans and wheat was narrowed to a 4-day window. The results of this research confirmed that CCD can be used to capture change due to autumn agricultural activities, and this technique can also separate change due to harvest and tillage. Finally, this study demonstrated that when data from different SAR missions are combined in a virtual constellation, timing of harvest and tillage can be more precisely defined.

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s−1 and negative biases of −0.4 m s−1 and −1.0 m s−1, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.

Design considerations and performance analysis for RODiO distributed SAR mission

The paper introduces RODiO (Radar for Earth Observation by synthetic aperture DIstributed on a cluster of cubesats equipped with high-technology micro-propellers for new Operative services), an innovative mission concept selected by the Italian Space Agency (ASI) for a phase A study in the framework of Alcor program. The mission relies on a cluster of 4 passive CubeSats flying in formation with ASI PLATiNO-1 (PLT-1) Synthetic Aperture Radar (SAR) mission. The mission is aimed at demonstrating from space the concept of Distributed Synthetic Aperture Radar (DSAR) through the application of multi-platform image synthesis, i.e. the generation of an improved performance image through the coherent combination, so in both amplitude and phase, of a stack of low performance bistatic raw data collected by formation-flying cubesat receivers. RODiO system parameters are discussed and preliminary performance evaluated. Results confirm that multi-static beamforming is able to both improve the Signal-to-Noise Ratio (SNR) and the level of ambiguities in the generated DSAR images, making the relevant values compliant with high-quality SAR imaging standards.

Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We have quantified the loss of precision due to the application of static flattening factors, and show that these are well below the stated requirements of change-detection algorithms. Finally, we have discussed the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth-Observation Satellites (CEOS) Analysis-Ready Dataset (ARD) families—Geocoded Single-Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework.

Investigating the dielectric properties of lunar surface regolith fines using Mini-RF SAR data

Polarimetric synthetic aperture radar (SAR) is an effective technique to retrieve physical properties of planetary surfaces, such as the dielectric constant and surface roughness. Dielectric properties of lunar regolith are quite attractive for future lunar SAR missions. In this paper, we investigate the dielectric properties of lunar regolith by the Mini-RF SAR data. First, a new model of dielectric constant inversion for hybrid polarimetric SAR is proposed, in which the hybrid polarimetric scattering similarity parameter is first introduced. Second, the dielectric constants of Apollo 14, 16, 17 and Chang’E-5 landing sites are estimated through the proposed model. The inversion results fit well with the laboratory measurements of lunar samples, with an estimated root mean square error (RMSE) of 0.53. In addition, we analyze the dielectric properties of regolith on crater floors in different geologic settings, including the lunar maria, highlands, and permanently shadowed regions (PSRs) near the lunar poles. The results indicate that for craters with diameters of 5–25 km, the real part of the dielectric constant of regolith fines increases with crater depth-to-diameter (d/D) ratio, while no apparent correlation is found with crater diameter. Furthermore, the average dielectric constant of regolith fines is 3.01 in PSRs, which is less than that in the lunar maria and highlands (3.43 and 4.13, respectively). This implies that craters in PSRs may possess a looser regolith material compared to the mid-latitude craters. In a word, the proposed method is useful for estimating the dielectric properties of lunar regolith, and it is promising for future lunar SAR applications.

Modeling the Effects of Oscillator Phase Noise and Synchronization on Multistatic SAR Tomography

Recent results have highlighted the potential ability of bi- and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and a low frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multi-platform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the point target response may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in SIMO mode are the most affected by oscillator phase noise error, followed by MIMO, with negligible effects on the SAR-SISO mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.

Interferometric Orbit Determination System for Geosynchronous SAR Missions: Experimental Proof of Concept

Future Geosynchronous Synthetic Aperture Radar (GEOSAR) missions will provide permanent monitoring of continental areas of the planet with revisit times of less than 24 h. Several GEOSAR missions have been studied in the USA, Europe, and China with different applications, including water cycle monitoring and early warning of disasters. GEOSAR missions require unprecedented orbit determination precision in order to form focused Synthetic Aperture Radar (SAR) images from Geosynchronous Orbit (GEO). A precise orbit determination technique based on interferometry is proposed, including a proof of concept based on an experimental interferometer using three antennas separated 10–15 m. They provide continuous orbit observations of present communication satellites operating at GEO as illuminators of opportunity. The relative phases measured between the receivers are used to estimate the satellite position. The experimental results prove the interferometer is able to track GEOSAR satellites based on the transmitted signals. This communication demonstrates the consistency and feasibility of the technique in order to foster further research with longer interferometric baselines that provide observables delivering higher orbital precision.

Additive Manufacturing of Compliant Mechanisms for Deployable Aerospace Structures

In the past 10 years, complex deployable structures have become common on CubeSats and large-scale spacecraft. As new missions are pursued, there is an increased need for more mass and volume efficient deployments. Over the same period, metal additive manufacturing (AM) has enabled new forms of spaceflight hardware. However, AM of compliant mechanisms has not been fully leveraged for deployable aerospace structures. The Surface Water Ocean Topography and NASA-ISRO Synthetic Aperture Radar mission missions launching in 2022 both utilize large deployable masts. Each mast deployment is driven by numerous spring and damper mechanisms. Because of volume constraints, the spring mechanisms designed utilize high aspect ratio rectangular cross section torsion springs that represent the state of the art of manufacturing. This extreme spring design resulted in manufacturing difficulties and hardware failures during ground mechanism testing. Upon re-examining the mechanism design, AM enables torque performance, mass, and complexity improvements. AM allows for torsion spring cross sections not otherwise possible with traditional spring manufacturing methods. Prototype springs of various cross sections were printed in maraging steel and tested. Results confirmed design analysis, and doubling of the spring constant was achieved when compared to the traditional springs. The use of AM also allows springs to be built monolithically with surrounding structure. Design, manufacturing, and test findings will be discussed along with future implications for deployable aerospace structures.

Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data

Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.

Processing Techniques for Nadir Echo Suppression in Staggered Synthetic Aperture Radar

Synthetic aperture radar (SAR) is a class of high-resolution imaging radar particularly suitable for satellite remote sensing with diverse applications, such as biomass and ice monitoring, generation of digital elevation models, and measuring of subsidence. Staggered SAR is a novel mode of operation under consideration for the next-generation SAR missions such as Tandem-L and NASA-ISRO SAR. It uses digital beamforming and a continuous variation of the pulse repetition interval (PRI) to achieve high azimuth resolution over a much wider, continuous swath than is traditionally possible. This PRI variation renders infeasible use of existing methods to avoid nadir echoes, which might impair the quality of staggered SAR images. This work proposes processing techniques that mitigate the impact of nadir echoes in staggered SAR through localization and thresholding-and-blanking of these echoes in range-compressed data and recovery of part of the underlying useful signal through interpolation. The performance of these processing techniques is evaluated through simulations using real TerraSAR-X data. The proposed technique can be implemented as an optional stage in the processing chain of future staggered SAR missions and leads to improved image quality at a reasonable additional computational cost.

An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding

This article describes a projection algorithm between radar and map coordinates based on the representation of radar samples as area elements (AEs) rather than point elements. Each AE on the map grid (geographic grid) is associated with a number of radar grid samples that intersect completely or partially the AE. The association enables the geocoding (i.e., the map projection of radar imagery) with adaptive multilooking, accurately accounting for all radar samples contributing to the geocoded elements according to topography and radar geometry. By using averaging rather than interpolation, the proposed projection does not suffer from interpolation overfitting. The area-based geocoding also enables the generation of the geocoded polarimetric covariance matrix (GCOV) and geocoded synthetic aperture radar (SAR) interferograms with adaptive multilooking. Analogously, the slant-range projection of geocoded data is improved by projecting geographic grid pixels onto the radar grid according to their corresponding location based on the radar geometry without leaving gaps. This approach is used to reduce the computation time of previously published radiometric terrain correction (RTC) algorithms, performing 3.6–6.5 times faster over multilooked data and up to 26.3 times faster over single-look data. We demonstrate the strengths of the proposed area projection (AP) algorithm for RTC and geocoding using Uninhabited Aerial Vehicle SAR (UAVSAR), Sentinel-1B, and ALOS-2/PALSAR-2 data, and evaluate the results in the context of the upcoming NASA-ISRO SAR (NISAR) mission.

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.

System Concepts and Potential Applications of a Tri-Beam Spaceborne SAR Mission

Multitemporal synthetic aperture radar interferometry (MT-InSAR), capable of detecting both surface deformation and elevation with high precision, is used for many applications in earth observation. Conventional synthetic aperture radar (SAR) missions with a single beam only detect deformation along the line of sight (LOS) and relative elevation due to the undetermined model of phase wrapping. In a multisatellite SAR mission, measurements from different SAR geometry improve the sensitivity of the detectable deformation, especially to the deformation along the north–south (N-S) direction. However, it is difficult to combine the measurements from varying viewing angles since the absolute phase cannot be reconstructed without a ground control point. In this article, a tri-beam SAR system is introduced to detect 3-D deformation and derive multiview 3-D surface model from a single spaceborne platform. The accuracy of the 3-D deformation from the tri-beam SAR is exploited with varying squint and incident angles to obtain the optimal parameters of the three beams. Then a multidimensional coherent scattering model is used to simulate the multitemporal SAR data with different viewing angles. Regarding the tri-beam SAR, potential applications in earth observation including 3-D deformation monitoring, geodetic stereo SAR, and multiview 3-D forest reconstruction are investigated subsequently. The results of this study indicate that the tri-beam SAR is able to measure 3-D deformation and reconstruct 3-D surface model without ground control point.