Ku-band Synthetic Aperture Radar Research Articles

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (σo) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized σo and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information.

Reservoir Ice Conditions from Multi-Sensor Remote Sensing and ERA5-Land: The Manicouagan Hydroelectric Reservoir Case Study

Reservoir ice can have an important impact on the watershed scale and influence hydraulic operations. On the other hand, hydropower generation can also impact the ice regime. In this study, multi-source satellite and ERA5-land data are used to evaluate ice conditions. Specifically, ice-controlling variables (temperature, water levels), ice regime (cover/deformation, thickness) and their interrelations are assessed for a 5-year period from 2017 to 2021. The methodology is applied to the Manicouagan reservoir, one of the largest hydropower reservoirs in Quebec, Canada. The satellite-based land surface temperatures (LSTs) suggest that winter 2021 was the hottest one. Overall, MODIS and Landsat LSTs agree with the ERA5-land temperatures. Ice backscatter from Sentinel-1 indicates that, in general, the reservoir is completely covered by ice from January to March. A correlation of 0.6 and 0.8 is observed between C- and Ku-band Synthetic Aperture Radar (SAR) signal and ice thickness, respectively. Important ice changes inferred from Differential Interferometric SAR (D-InSAR) occur approximately at the position where the largest ERA5-land ice thickness differences are observed. Winter water levels are also evaluated using satellite altimetric data to verify their influence on the ice dynamics. They show a decreasing tendency as the winter advances.

Snow profile reconstruction from tomographic UAV SAR

Snow profiles can provide reliable and detailed snow parameters for global change research, but it is still challenging to reconstruct large-scale spatiotemporally continuous snow profiles. Synthetic aperture radar (SAR) tomography (TomoSAR) has been proven to be a promising method for reconstructing the 3D structure of targets and has been used to reconstruct snow profiles from ground-based SAR. However, the experimental condition of ground-based SAR is ideal and the algorithms developed for ground-based SAR have some problems when applied to airborne and spaceborne sensors, such as large computational load and big angular diversity. In this paper, we first collected the Ku-band tomographic SAR dataset in the snow season of Altay, China using the Unmanned Aerial Vehicle (UAV) platform. Then, we reconstructed the snow profile from the dataset using three spectral estimation-based focusing algorithms. According to the non-stationary characteristic of the UAV platform and the dense medium property of snow, phase calibration and medium correction were performed, respectively. Finally, we assessed the reconstruction accuracy of the snow profile and snow depth using the ground synchronous measurement data. The reconstruction accuracy of the three focusing algorithms from high to low is the MUSIC algorithm, the Capon algorithm, and the Beamforming algorithm. Among them, the average error of the MUSIC algorithm to reconstruct the snow profile is 1.13 cm, and the RMSE and MAE of the snow depth retrieval are 3.71 cm and 3.26 cm, respectively. Although TomoSAR is still widely unexplored for snow using air-based sensors, the results show that it has the ability to reconstruct snow profiles accurately, and the proposed method can provide reliable support for reconstructing the large-scale spatiotemporal continuous snow profiles from subsequent spaceborne SAR.

A Probabilistic Approach for Stereo 3D Point Cloud Reconstruction from Airborne Single-Channel Multi-Aspect SAR Image Sequences

We investigate the problem of obtaining dense 3D reconstruction from airborne multi-aspect synthetic aperture radar (SAR) image sequences. Dense 3D reconstructions of multi-view SAR images are vulnerable to anisotropic scatters. To address this issue, we propose a probabilistic 3D reconstruction method based on jointly estimating the pixel’s height and degree of anisotropy. Specifically, we propose a mixture distribution model for the stereo-matching results, where the degree of anisotropy is modeled as an underlying error source. Then, a Bayesian filtering method is proposed for dense 3D point cloud generation. For real-time applications, redundancy in multi-aspect observations is further exploited in a probabilistic manner to accelerate the stereo-reconstruction process. To verify the effectiveness and reliability of the proposed method, 3D point cloud generation is tested on Ku-band drone SAR data for a domestic airport area.

Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth's surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth's climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world's population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth's cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements.

A Registration Method for Dual-Frequency, High-Spatial-Resolution SAR Images

With the continuous development of synthetic-aperture-radar (SAR) technology, SAR-image data are becoming increasingly abundant. For the same scene, dual-frequency (high-frequency and low-frequency) SAR images can present different details and feature information. Image fusion of the two frequencies can combine the advantages of both, thus describing targets more comprehensively. Image registration is the key step of image fusion and determines the quality of fusion. Due to the complex geometric distortion and gray variance between dual-frequency SAR images with high resolution, it is difficult to realize accurate registration between the two. In order to solve this problem, this paper proposes a method to achieve accurate registration by combining edge features and gray information. Firstly, this paper applies the edge features of images and a registration algorithm based on fast Fourier transform (FFT) to realize rapid coarse registration. Then, combining a registration algorithm based on the enhanced correlation coefficient (ECC) with the concept of segmentation, the coarse-registration result is registered to achieve accurate registration. Finally, by processing the airborne L-band and Ku-band SAR data, the correctness, effectiveness, and practicability of the proposed method are verified, with a root mean square error (RMSE) of less than 2.

A Study on the Feasibility of Stop-and-Go Approximation in FMCW SAR

Synthetic aperture radar (SAR) specializes in capturing two-dimensional images of Earth’s surface. Because satellites or aircraft have mainly been used as SAR platforms, pulse radar systems with high peak transmitted power have been preferred for long-range detection. However, because systems based on pulse radar are generally too heavy and expensive, lightweight and low-cost frequency-modulated continuous-wave (FMCW) radar systems have attracted increasing interest, and many studies on FMCW SAR signal processing are being conducted. The pulse duration of FMCW radar is considerably longer than that of pulse radar. Therefore, it is necessary to determine whether stop-and-go approximation (SAG) is still valid for FMCW radar. If SAG is not applicable, an additional, time-consuming range cell migration correction process is required. In this study, the conditions under which SAG can be applied to FMCW SAR were analyzed. Moreover, Ku-band FMCW SAR field tests were conducted to experimentally validate the feasibility of SAG. Several quantitative parameter values demonstrating the advantages of applying SAG were identified.

Parameter Design and Imaging Method of Spaceborne Azimuth Interrupted FMCW SAR

Spaceborne azimuth interrupted frequency modulation continuous wave (AI-FMCW) synthetic aperture radar (SAR), known as a novel system to observe the characteristics of ground objects, employs a single antenna to transmit and receive continuous waves, thus different from traditional frequency modulated continuous wave (FMCW) SAR that needs two antennas. However, due to the single antenna, there is a phenomenon that the transmitting of several continuous waves could be unavailable while replaced by receiving radar echoes. The process of switching the transmitter ON and OFF will leave gaps in the synthetic aperture. In this case, traditional imaging algorithms such as the wavenumber domain algorithm (WDA) cannot acquire clear focused images, resulting in the blurred ones corrupted by ghost targets. To solve the problem mentioned above, this letter presents an image restoration algorithm to improve the AI-FMCW SAR image performance. In this letter, there are two main contributions: 1) some key parameters of the AI-FMCW SAR system are designed and 2) a method based on Richardson–Lucy (RL) deconvolution algorithm is applied to AI-FMCW SAR imaging to suppress ghost targets. Finally, a case of the Ku-band AI-FMCW SAR system is given, and experiments via simulated AI-FMCW SAR data are conducted to validate the proposed method.

Translation between High- and Low-frequency SAR Images using Cycle-Consistent Conditional Adversarial Network

The bi-frequency (high- and low) synthetic aperture radar (SAR) images cannot be directly compared due to their distinct statistical properties. To diminish their statistical difference, we manage to translate the bi-frequency SAR images into one another. Therefore, we propose a cycle-consistent conditional adversarial network to achieve the goal. The cycle-consistency criteria in the Cycle GAN and the conditional generation adversarial networks in the Pix2Pix are integrated to construct the cycle-consistent conditional adversarial network. Experiments on Ku-band and P-band SAR images validate that our method outperforms Cycle GAN and Pix2Pix.

Theory of arbitrary rigid object motion autofocus for non‐uniform target rotation and translation

Moving targets in synthetic aperture radar (SAR) imagery often yield signatures that are smeared beyond recognition. One mitigation strategy is to attempt to refocus such smears as if the corresponding targets were stationary. This problem is especially difficult for cases in which the target executes time-varying rotation rates for roll, pitch and yaw, as well as for simultaneous non-linear target translational motion. The current analysis develops an arbitrary rigid object motion autofocus (AROMA) for performing automatic refocus of such targets having non-uniform rotation and translation profiles. AROMA uses a maximum likelihood signal-theoretic analysis of the physical rotation angles and translation distances arising from the unknown rigid object motions in order to generate refocused target imagery. The efficacy of this methodology is demonstrated via the injection of a simulated rotating and translating target comprised of multiple scattering centres into a cluttered background of measured Ku-band SAR data. Furthermore, the application of AROMA to an input defocused smear corresponding to a ‘target-of-opportunity’ vehicle in measured Ka-band SAR imagery yields significant improvement in the target focus, thus further validating the underlying theoretical analysis developed herein.

Theory of data‐driven SAR autofocus to compensate for refraction effects

There are challenges in attempting to acquire sufficient atmospheric measurements that are required in order to model the physical atmospheric refraction effects for all transmission pulses within a typical synthetic aperture radar (SAR) collection interval. In particular, atmospheric turbulence can induce spatial variations of the air density, temperature, and other physical variables along the synthetic aperture, which can have differing effects on the local refraction-induced bending and delay of each of the transmission pulses. The primary goal of the current investigation is to develop an alternate approach based on data-driven techniques in order to develop methods for automatically correcting such refraction-induced defocus effects on SAR image data. This alternate methodology applies two-dimensional maximum likelihood signal-theoretic techniques in order to estimate and compensate for the refraction-induced phase errors of the input defocused SAR imagery, thus yielding improved scene refocus wherein the majority of the refraction-induced defocusing effects have been corrected. The efficacy of this data-driven approach is demonstrated through the injection of known bending and delay effects applied to measured Ku-band SAR data.

Implementation of Vortex Electromagnetic Waves High-Resolution Synthetic Aperture Radar Imaging

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) offers a new degree of freedom for radar techniques, although to date its application to synthetic aperture radar (SAR) imaging has not yet been widely reported. In this letter, a SAR imaging model based on vortex EM waves is derived. Ku-band vehicle-borne vortex EM waves SAR experiment using phased uniform circular array antennas is reported for the first time. A detailed comparison with conventional plane EM waves SAR imaging is given in terms of signal-to-noise ratio and azimuth resolution. The experimental results demonstrate the effectiveness of the proposed imaging model, and indicate that vortex EM waves can be exploited in SAR imaging with higher azimuth resolution than plane EM waves. OAM, which is completely independent of frequency and polarization, is a new degree of freedom in SAR imaging. This work shows the new applications of vortex EM waves, which paves the way for the development of novel SAR systems.

SAR Focus Theory of Complicated Range Migration Signatures Due to Moving Targets

Recent studies have revealed that the residual range migration effects of synthetic aperture radar (SAR) imagery smears induced by moving targets can exhibit complicated shapes that are not limited to that of parabolas. This letter demonstrates that automatic focusing methods can remove such range migration effects by estimating and compensating for phase errors directly in the radar video phase history domain. This approach is validated using measured Ku-band SAR clutter data containing buildings and foliage.

A Modified Equivalent Range Model and Wavenumber-Domain Imaging Approach for High-Resolution-High-Squint SAR With Curved Trajectory

In a synthetic aperture radar (SAR) system, the radar platform may move with a curved trajectory due to the existence of vertical velocity and acceleration, which may result in the failure of the conventional imaging methods. In order to deal with this problem, this paper proposes an improved wavenumber-domain imaging algorithm for high-resolution-high-squint SAR with a curved trajectory. It mainly includes three aspects. First, a modified equivalent range model for a curved trajectory is derived. Second, an improved wavenumber domain imaging algorithm based on the proposed range model is analyzed in detail. Finally, the imaging distortion caused by vertical velocity and acceleration is corrected via geometry and inverse projection. Simulated results and Ku-band real SAR data processing are used to validate the proposed model and imaging algorithm.

Compact waveguide broadband dual-polarized horn array with a novel orthomode transducer in Ku-band for high-power SAR systems

A novel compact waveguide-based broadband and dual-polarized Ku-band Synthetic Aperture Radar antenna with a high level of isolation between polarizations and high input power level has been designed for small platforms and unmanned aerial vehicles (UAV). The antenna design is composed of 8 dual-polarized horn antennas in an array configuration fed through a very efficient and novel structure to generate two orthogonal polarizations based on a orthomode transducer to achieve a large bandwidth and an excellent isolation between polarizations needed in a polarimetric system. Finally, as the antenna will be placed inside a pod under the UAV wings it must be lightweight, robust and of small size.

Design, implementation, and measurements of a novel broadband dual-polarized patch array in Ku band for polarimetric SAR systems

A novel compact broadband and dual-polarized Ku-band synthetic aperture radar (SAR) antenna with a high level of isolation between polarizations has been developed by INTA Radar Laboratory as an advanced sensor for the QUASAR project, which involves INTA new SAR developments for small platforms and unmanned aerial vehicle (UAV). The antenna design is composed by a 4 × 8 dual-polarized square patch array fed through a very efficient and novel structure based on a cross-shape slot to achieve a large bandwidth and overall to achieve an excellent isolation between polarizations needed in a polarimetric system such as the one INTA has developed. Finally, the antenna must be placed inside a pod which is going to be placed under the UAV wings, so the antenna must be lightweight, robust, and with small size. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:2537–2543, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26313

SAR PROCESSING ALGORITHM WITH BUILT-IN GEOMETRIC CORRECTION

Synthetic aperture radar (SAR) systems onboard small aircrafts suffer from trajectory deviations and instabilities of antenna orientation. These kinds of motion errors lead to significant geometric distortions in SAR images. In order to correct the distortions, we propose a time-domain multi-look stripmap SAR processing algorithm with built-in geometric correction. In the algorithm, the azimuth reference functions and range migration curves are designed to produce SAR images directly on a correct rectangular grid on the ground plane. The proposed technique has been successfully tested by using a Ku-band airborne SAR system installed onboard light-weight aircraft.

SAR Calibration Aided by Permanent Scatterers

We propose a calibration method suitable for a set of repeated synthetic aperture radar (SAR) acquisitions that uses both absolute calibrated devices (such as corner reflectors) and stable targets identified in the scene [the permanent scatterers (PSs)]. Precisely, the role of the PS is to extend the initial calibration sequence by monitoring the radiometric stability of the system throughout the whole mission life span. At a first step, this paper approaches the problem of PS-based normalization by an iterative maximum-likelihood method that exploits the stack of complex interferometric SAR images. Two solutions are given based on different assumptions on the PS phases. As a second step, the merging of these estimates with the available calibration information is discussed. Results achieved by experimental acquisitions are shown in two different SAR systems: 1) a C-band spaceborne SAR and 2) a Ku-band ground-based SAR.

Retrieving 3-D Topography by Using a Single-Antenna Squint-Mode Airborne SAR

In this paper, a novel method is proposed for retrieving 3-D topography of the ground surface by using a single-antenna squint-mode airborne synthetic aperture radar (SAR). The main idea of the method is based on the fact that the spatial 3-D location of a target can be found as the intersection of the range sphere, the Doppler cone, and the elevation plane of the antenna pattern. An accurate estimation of the Doppler centroid for each resolution cell is required to realize this method. Experimental results obtained with a Ku-band airborne SAR are presented. The method calls for using a precise calibrated attitude determination unit.

C- and Multiangle Ku-Band Synthetic Aperture Radar Data for Bare Soil Moisture Estimation in Agricultural Areas

A sensitivity analysis of C-band (5.3 GHz) and Ku-band (14.85 GHz) synthetic aperture radar (SAR) data to the bare soil moisture content of agricultural fields was conducted in this study. The C-band data were obtained with a 23° incidence angle, whereas the Ku-band data were obtained with 35°, 55°, and 75° incidence angles. The fields presented either a small-scale or an intermediate-scale periodic soil roughness components, associated with level-basin and furrow irrigation systems, respectively. For fields with a small-scale roughness component, the SAR data were sensitive to soil moisture, particularly at the C-band with a 23° incidence angle and Ku-band with a 35° incidence angle. For fields with a intermediate-scale roughness component, both C- and Ku-band data were nearly insensitive to soil moisture. By using a theoretical surface scattering model, this study also analyzed the effects of different soil roughness components [root mean square (RMS) height h, correlation length, and periodic row structure] in the SAR data. For fields with RMS height <0.3 cm, a small variation in h (from 0.1 to 0.3 cm) provoked a significant variation in the SAR data (up to 8 dB).