Synthetic Aperture Radar Algorithm Research Articles

Enhancement of penetration of electromagnetic waves by field focusing applied to GPR detection

Ground penetrating radar (GPR) is limited by challenges such as signal attenuation and inadequate target detection in lossy dielectrics, prompting research on data processing algorithms to enhance detection capability. In this study, we propose a method for enhancing GPR detection capability based on electromagnetic field focusing (FF). The method involves calculating the phase difference attributable to the range difference between each antenna element and the focal point and compensating its feed phase accordingly. This approach effectively concentrates the energy of electromagnetic waves at the target area. Tapered slot antennas operating within the 2.5–8 GHz frequency band were employed for transmitting and receiving, thus facilitating algorithm verification and target detection. The synthetic aperture radar algorithm executed pulse compression and range migration on the received data, thereby intensifying the signal at the target object’s position and achieving double focusing. Notably, this study uniquely employs FF to enhance the penetrability of electromagnetic waves and augment the detection capability of GPR. Both simulation and experimental results demonstrated the effectiveness of the proposed method in improving the GPR imaging quality.

High-resolution wide-swath SAR imaging with multifrequency pulse diversity mode in azimuth multichannel system

ABSTRACT High-resolution wide-swath (HRWS) imaging has emerged as a focal point in synthetic aperture radar (SAR) research. However, conventional SAR systems face challenges in achieving both high azimuth resolution and wide swath simultaneously due to the minimum antenna area constraint. In this paper, we propose a HRWS imaging method based on multifrequency pulse diversity (MFPD) and azimuth multichannel (AMC) technique, capable of resolving range and Doppler ambiguities concurrently. In MFPD mode, range ambiguous echoes can be separated by matched filters in the range frequency domain, as multifrequency pulses are transmitted by a single channel in the transmitter. To further enhance the ambiguity resolution ability of the system and address azimuth incoherence, a novel scheme applying the MFPD to AMC system is proposed, categorized into two distinct scenarios. 1) Uniform Sampling: This scenario satisfies the displaced phase centre antenna condition. Under this condition, high range resolution imaging can be achieved through spectrum splicing in range frequency domain, simultaneously resolving Doppler ambiguity. 2) Non-uniform Sampling: In this scenario, the ambiguous echoes are processed by spatial digital beamforming and spectrum splicing in two-dimensional frequency domain. Finally, the HRWS imaging can be obtained by performing the traditional SAR algorithm. Furthermore, the proposed method can synthesize a wideband signal from multifrequency signals, thereby enhancing the feasibility of super-high-resolution imaging. Simulation results demonstrate the effectiveness of the proposed method.

Helmholtz–Galerkin Technique in Dipole Field Scattering from Buried Zero-Thickness Perfectly Electrically Conducting Disk

Non-invasive concealed object detection, identification, and discrimination have been of interest to the research community for decades due to the needs to preserve infrastructures and artifacts, guarantee safe conditions for the detection and location of landmines, etc. A modern approach is based on the use of an unmanned aerial vehicle equipped with ground-penetrating radar, which has the advantage of not requiring direct contact with the ground. Moreover, high-resolution underground images are obtained by coherently combining measurements by using a synthetic aperture radar algorithm. Due to the complexity of the real scenario, numerical analyses have always been welcomed to provide almost real-time information to make the best use of the potential of such kinds of techniques. This paper proposes an analysis of the scattering from a zero-thickness perfectly electrically conducting disk buried in a lossy half-space surrounded by air and illuminated by a field generated by a Hertzian dipole located in the air. It is carried out by means of a generalized form of the analytically regularizing Helmholtz–Galerkin technique, introduced and successfully applied by the authors to analyze the plane-wave scattering from a disk and a holed plane in a homogeneous medium. As clearly shown in the numerical results, the proposed method is very effective and drastically outperforms the commercial software CST Microwave Studio 2023.

A Modified Frequency Nonlinear Chirp Scaling Algorithm for High-Speed High-Squint Synthetic Aperture Radar with Curved Trajectory

The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m.

Maritime Moving Target Reconstruction via MBLCFD in Staggered SAR System

Imaging maritime targets requires a high resolution and wide swath (HWRS) in a synthetic aperture radar (SAR). When operated with a variable pulse repetition interval (PRI), a staggered SAR can realize HRWS imaging, which needs to be reconstructed due to echo pulse loss and a nonuniformly sampled signal along the azimuth. The existing reconstruction algorithms are designed for stationary scenes in a staggered SAR mode, and thus, produce evident image defocusing caused by complex target motion for moving targets. Typically, the nonuniform sampling and complex motion of maritime targets aggravate the spectrum aliasing in a staggered SAR mode, causing inevitable ambiguity and degradation in its reconstruction performance. To this end, this study analyzed the spectrum of maritime targets in a staggered SAR system through theoretical derivation. After this, a reconstruction method named MBLCFD (Modified Best Linear Unbaised and Complex-Lag Time-Frequency Distribution) is proposed to refocus the blurred maritime target. First, the signal model of the maritime target with 3D rotation accompanying roll–pitch–yaw movement was established under the curved orbit of the satellite. The best linear unbiased (BLU) method was modified to alleviate the coupling of nonuniform sampling and target motion. A precise SAR algorithm was performed based on the method of inverse reversion to counteract the effect of a curved orbit and wide swath. Based on the hybrid SAR/ISAR technique, the complex-lag time-frequency distribution was exploited to refocus the maritime target images. Simulations and experiments were carried out to verify the effectiveness of the proposed method, providing precise refocusing performance in staggered mode.

Reconstruction of synthetic aperture radar data using hybrid compressive sensing and deep neural network algorithm

SummaryThe reconstruction of reflectivity profile of the synthetic aperture radar (SAR) is an important field of research. SAR tomography is an advanced 3D imaging technique for the spectrum estimation in the elevation direction for each azimuth resolution cell. This work presents the processing chain for the tomographic reconstruction from ALOS PALSAR data for an urban region. First, the data are preprocessed by removing the speckle noise followed by atmospheric phase screen and topographic correction. Then the SAR images are stacked together with one master image and the remaining slave images on the baseline value. After the images are coregistered, the interferogram is generated from the image to obtain the difference of the phase value. Then the proposed super resolution SAR (SRS) algorithm is attempted for TomoSAR processing, which combines the functionality of modern machine learning method like deep learning with parametric block‐based compressive sensing approach. Finally, a 3D image is reconstructed from the input data. Evaluation is carried out by comparing the results of the proposed method with other spectrum estimation methods such as nonlinear least square, Capon, and multisignal classification. The normal baseline of the interferometric fringes is about 368.54 m. The proposed SRS algorithm gives improved results with less mean elevation error of 1.8 m and the less standard deviation error of 4.85 m. Finally, the result reveals that the SRS algorithm performed better than other TomoSAR algorithms with the less relative error 0.003.

Enhancing Road Extraction in Large-Scale Complex Terrain through Multi-Source Remote Sensing Image Fusion and Optimization

In large-scale remote sensing scenarios characterized by intricate terrain, the straightforward road imaging features in synthetic aperture radar (SAR) images make them susceptible to interference from other elements such as ridges, compromising the robustness of conventional SAR image road extraction methods. This paper introduces a method that integrates Gaofen-3 (GF-3) with a resolution of 3.0 m, Digital Elevation Models (DEMs), and Gaofen-2 (GF-2) remote sensing image data with a resolution of 4.0 m, aiming to improve the performance of road extraction in complex terrain. Leveraging DEMs, this study addresses the limitations in feature-based SAR algorithms, extending their application to complex remote sensing scenarios. Decision-level fusion, integrating SAR and multispectral images, further refines road extraction precision. To overcome issues related to terrain interference, including fragmented road segments, an adaptive rotated median filter and graph-theory-based optimization are introduced. These advancements collectively enhance road recognition accuracy and topological precision. The experimental results validate the effectiveness of the multi-source remote sensing image fusion and optimization methods. Compared to road extraction from multispectral images, the F1-score of the proposed method on the test images increased by 2.18%, 4.22%, and 1.4%, respectively.

Comparative analysis of MT-InSAR algorithms supported by GNSS data and corner reflectors: Assessing performance and accuracy

This paper presents a comparative analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) algorithms in a corner reflector located in Lisbon, Portugal. The reflector was monitored using daily observations from the Global Navigation Satellite System (GNSS) technique with submillimeter precision. The study focuses on comparing the performance and accuracy of different MT-InSAR approaches i) the Persistent Scatterer Interferometry (PSI) method using the open-source software StaMPS, ii) the PSI method using the commercial software SARPROZ, iii) the Quasi-PS method implemented with the SARPROZ software, as well as iv) a hybrid method Persistent scatterer – Distributed Scatterer (PS-DS) obtained from the European Ground Motion Service (EGMS) with SqueeSAR algorithm. The study period assumed was from October 2017 to January 2019, by considering the same initial information (ascending orbit Sentinel images). Statistical analysis of the time series was also performed, and the density of points in the vicinity was evaluated. To provide a comprehensive evaluation of accuracy, the uncertainties associated with both the GNSS and InSAR techniques were assessed. The findings of this comparative analysis offer valuable insights into the strengths and limitations of various MT-InSAR algorithms by using a more precise technique as a benchmark. The results improve the understanding of deformation monitoring in geodetic applications and highlight the potential for enhanced accuracy in such assessments.

Evolution of the magma system at Makushin volcano, Alaska, from 2004 to 2021

Located in the northeast of Unalaska Island in the central part of the Aleutian, Alaska, Makushin volcano is one of the more active volcanoes in the United States, with the most recent eruption occurred in 1995. Pre-eruptive inflation and post-eruptive deflation were identified from 1993 to 1995 and 1996 to 2000, respectively, and were interpreted as volume changes associated with magma movement within the plumbing system. In this study, we investigate the surface deformation of Makushin from 2004 to 2021 to evaluate its present-day activity, using Synthetic Aperture Radar (SAR) interferometry technique. SAR images acquired from C-band Envisat and Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 satellites are analyzed using an advanced multiple temporal Interferometric SAR (InSAR) algorithm. The produced deformation time series are then used to track the temporal evolution of the volcanic system with Ensemble Kalman Filter (EnKF) assuming Mogi sources. The results show that Makushin is characterized by time-varying deformation episodes including quiescence, and inflation/deflation cycles at varied rates since 2004. The derived deformation histories can be well reproduced by a spatially stationary pressure source located to the east-northeast of the summit, at a depth about 6 km beneath the sea level (BSL). The inflation/deflation cycles with distinct temporal patterns are interpreted as results from volatile-dominated volume accumulation/loss in the magmatic reservoir. A secondary reservoir connected to the main magma chamber is revealed from the deformation time series. No surface displacement associated with the large earthquake swarm in 2020 is identified from the InSAR-derived deformation. The cumulative volume change in the magma reservoir is about −6×106m3 from 2004 to 2021, indicating a volume/pressure deficit in the magma plumbing system.

Frequency Domain Imaging Algorithms for Short-Range Synthetic Aperture Radar

In order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail.

Proposed SAR range focusing algorithm based on simulation analysis and SDR implementation

Synthetic aperture radar (SAR) imaging systems use pulse focusing techniques for achieving fine range resolution and large range detection. Stretch processing and correlation pulse focusing techniques are the most popular algorithms used in the radar signal processing stage. The architecture of the stretch processing technique makes it commonly used in frequency modulated continuous wave (FMCW) radar systems due to its simple architecture, low sampling rate, and inexpensive hardware. This paper discusses how the stretch range focusing approach can be used instead of the matched filter technique in the processing stage of the SAR Range-Doppler algorithm (RDA) to generate images of finer resolution and higher quality. In addition, validating the proposed algorithm and evaluating its performance according to some evaluation indices such as peak sidelobe ratio (PSLR), integrated sidelobe ratio (ISLR), and the impulse response width (IRW). Also, this paper presents a comprehensive analysis and hardware implementation of the two SAR algorithms, based on software-defined radio (SDR) architecture where it is characterized by less complexity, low cost, and high flexibility due to the usage of both software data processing and software waveform generation. Two Universal Software Radio Peripheral (USRP-2932) and NI-LabVIEW® software development environment are the platforms used for the hardware validation process. The transmitted waveform is a linear frequency modulated (LFM) signal generated by the first USRP (transmitter) which will hit a target located at a certain distance. The second USRP is used as a receiver for target’s echo processing and detection. This processing stage contains two architectures, which are the matched filter pulse compression architecture and the stretch range focusing approach. Finally, the validation process of the hardware results is performed based on the pre-mentioned evaluation indices.

Hybrid Domain Efficient Modulation-Based Deceptive Jamming Algorithm for Nonlinear-Trajectory Synthetic Aperture Radar

Deception jamming of synthetic aperture radar (SAR) has attracted extensive attention due to its low power consumption and high fidelity advantages. However, existing SAR deception jamming algorithms assume that SAR operates on a linear trajectory. In practice, SAR trajectories often become nonlinear due to factors such as atmospheric turbulence, which results in the jamming signals lacking the two-dimensional spatial variability of nonlinear-trajectory SAR echo signal and affects the imaging quality of deception jamming. This paper proposes a new algorithm for nonlinear-trajectory airborne SAR deception jamming based on hybrid domain efficient (HDE) modulation. This algorithm derives the jamming frequency response (JFR) with SAR trajectory deviation in the azimuth time–frequency hybrid domain. Based on the hybrid domain modulation, the jammer calculates the JFR of the linear trajectory in the azimuth frequency domain and constructs for the real-time trajectory deviation pulse by pulse at each azimuth moment. The real-time modulation process of the algorithm only involves range domain Fourier transform and complex multiplication, combining computational efficiency and modulation flexibility. The validity constraints of the algorithm have been analyzed to ensure the focusing ability of the jamming signal. Simulation and computational complexity analysis validate the excellent performance of the algorithm in imaging quality and efficiency.

Improved ship imaging algorithm for geosynchronous synthetic aperture radar (SAR) featured by subaperture time window selection optimization and inverse SAR translational compensation

An inverse synthetic aperture radar processing-based algorithm is improved for ship imaging in geosynchronous synthetic aperture radars (GEO SARs) to solve the technical challenges including long accumulation time and complex target motion characteristics. The improvement on the existing algorithm mainly involves four aspects. First, the formula for calculating the appropriate time length of subaperture is derived, according to the principle that the accumulated power of the ship’s echo corresponding to the subaperture duration ensures that the signal-to-noise ratio satisfies the requirement of GEO SARs. The derived formula solves the problem that how long the subaperture length should be taken with regards to a long accumulation time of GEO SAR when imaging ships. Second, we propose an optimization criterion for determining the appropriate subaperture time window by ensuring that the maximum number of ship pixels is detected in the subaperture image. The proposed criterion is used to optimize the location of the subaperture time window in the long accumulation time. Third, the SPECAN algorithm used in the scene imaging process is replaced by an improved nonlinear chirp scaling (NLCS) algorithm to improve the imaging accuracy of the scene. Our improvement on the traditional NLCS algorithm focuses on improving precisions of some of the formulas in the traditional algorithm. Furthermore, a translational compensation algorithm is proposed by considering the influence of spatial variance of slant range history of GEO SARs. The proposed translational compensation algorithm improves the compensation accuracy since the spatial variance of different scatterers in the scene is considered. A series of simulation experiments related with ship imaging in GEO SARs were carried out to verify the correctness of the proposed algorithm and the improvement on the imaging quality compared with that of the existing algorithm.

Oceanic Internal Wave Signature Extraction in the Sulu Sea by a Pixel Attention U-Net: PAU-Net

Oceanic internal waves (IWs) are an important oceanic phenomenon, and the realization of the fast and efficient extraction of IWs is of fundamental significance. The development of deep learning techniques provides new opportunities for the signature extraction of oceanic IWs. In this letter, we propose a two-stage oceanic IW signature segmentation algorithm for synthetic aperture radar (SAR) images. The algorithm includes a decision fusion-based oceanic IW images classification stage and a pixel attention U-Net (PAU-Net)-based stripe segmentation stage. First, we adopt an IW classification algorithm by fusing the weak decision results of two different classifiers to get the final strong decision result to screen the image blocks containing oceanic IWs. Then we develop a PAU-Net to segment the IWs stripe. Finally, we concatenate them to obtain the extract results of the whole image. Experiments are performed using 527 image scenes from the Sulu Sea that contain IWs. The results show that the proposed algorithm can achieve the performance of oceanic IW signature extraction from SAR images.

A Novel Motion Compensation Method Applicable to Ground Cartesian Back-Projection Algorithm for Airborne Circular SAR

The Ground-Cartesian factorized back-projection (G-CFBP) is an efficient time-domain processing algorithm without image interpolation, and can realize accurate imaging for curved trajectory synthetic aperture radar (SAR). Its superiority shows good potential in airborne circular SAR (CSAR) imaging. However, the motion compensation (MoCo) based on ground Cartesian back-projection (GCBP) in the airborne CSAR is still a challenge. There are two main problems: one is that the existence of the image spectrum aliasing makes the processing of the phase error estimation inaccurate; the other is that the mapping relationship of the phase error between the image spectrum domain and the azimuth time domain still needs to be studied within GCBP processing chain. To tackle the above two problems, a novel MoCo method applicable to the GCBP algorithm is proposed and can be mainly divided into two steps: the first step is to remove the sub-aperture image spectrum aliasing by a spectrum compression operation; the second step is to establish an analytical phase error structure, which includes an auto-selection criterion of the effective support region for GCBP image. The first step ensures the accuracy of the phase error estimation, and the second step establishes the inverse-mapping relationship of the phase error between the image spectrum and azimuth time. These two procedures are both vital in improving the accuracy and robustness of the GCBP-based MoCo for the CSAR imaging. The processed results of simulated and real data are provided to verify the effectiveness of the proposed method.

Imaging Algorithm for Inverse Synthetic Aperture Radar in Condition of Non-Uniform Data Rate

Inverse synthetic aperture radar imaging is an important technique for radar detection and recognition of non-cooperative targets. However, for most of the existing imaging methods, pulse signals are transmitted and received at equal intervals, without considering the possible problems under condition of non-uniform data rate, such as accuracy decline of echo's range alignment and high side lobes in Doppler spectrum. The resulting vertical and horizontal defocusing can reduce the imaging quality and affect the feature extraction of the target. Therefore, an inverse synthetic aperture radar imaging method for non-uniform data rate is proposed. First, by designing the weight to simulate the alignment situation of uniform data rate, the correlation method with weighted sliding window is used to solve the problems of declined range alignment accuracy and vertical defocusing. Then, based on the linear form of non-uniform Fourier transform, the Fourier base matrix of non-uniform data rate is built, and CLEAN technology is used to complete the projection of echo signal on the bases iteratively. Finally, the Fourier basis matrix with uniform data rate is used to reconstruct the echo signal, which solves the problem of horizontal defocusing and makes the data rate of echo signal uniform. The effectiveness of the proposed method is verified by experiments using measured data.

Snow Depth Measurements With Ultra-Wideband Compact FMCW Radar on a Small Unmanned Aircraft System

This paper presents results from a field deployment of a small Unmanned Aircraft System (sUAS) radar in Colorado-Grand Mesa during Spring-2022 for measurements over snow. The stand-alone, low-power, compact, and Frequency Modulated Continuous Wave (FMCW) radar on a sUAS is used for these measurements. The radar operates over the frequency range of 2.8-5.8 GHz. The transmit signal is obtained by down-converting 77-81 GHz chirp generated with an automotive radar. The radar operates with a low output power of only 3 dBm (2 mW) to mitigate interference to nearby communication systems with a chirp duration of 250 μs. The received signal is up-converted back to 77-81 GHz for digitization and processing using the automotive radar data capture board. The radar data are processed with a fully focused Synthetic Aperture Radar (SAR) algorithm after applying phase and amplitude corrections to the transmitter chirp to obtain nearly ideal point target responses. The radar mapped air-snow and snow-ground surface interfaces as well as the snow internal layers with snow depth exceeding 2 m in areas covered with 15-25 m tall trees. In addition, the radar-generated snow thicknesses are within ±10 cm of in-situ measurements.

Efficient ISAR Imaging Approach for Nonuniformly Rotating Targets Based on ICPF-PPP

In this article, we introduce an improved algorithm for practical inverse synthetic aperture radar (ISAR) imaging, by utilizing the advantages of prominent point processing (PPP) and integrated cubic phase function (ICPF) in an efficient way. The proposed ICPF-PPP algorithm achieves a significant improvement compared to the conventional range-Doppler processing for ISAR imaging of a nonuniformly rotating target in terms of both image quality and computational efficiency. The experimental results using real datasets show that the proposed autofocus chain is highly efficient in forming ISAR images of a nonuniformly rotating target.

Fast Line Segment Detection and Large Scene Airport Detection for PolSAR

In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR.

Monitoring Surface Deformation Associated With Soil Erosion in Loess Plateau With an Improved Small Baseline Subset Interferometric Synthetic Aperture Radar Algorithm

As a geomorphic process persistently occurring over the earth’s surface, soil erosion is one of the most serious environmental problems in the world nowadays, seriously threatening agriculture, natural resources, and ecosystem environments. Monitoring surface deformation associated with soil erosion will help to understand the dynamics of erosion process and the erosion mechanism. In this article, an improved small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique is proposed to monitor the surface deformation over the wind-water erosion crisscross region in the Loess Plateau, China. In particular, a new deformation model considering both of the internal factors and external factors of soil erosion is introduced into the SBAS-InASR algorithm. The results show that the cumulative deformation is close to −200 mm during the investigated period. The deformation rate caused by precipitation is between −0.03 and 0.02 mm/mm. When the wind erosion factor changes by 1 unit, the deformation range is approximately −0.3–0.2 mm. Also, researchers found that the surface deformation is highly correlated to precipitation and wind. The root mean square errors (RMSEs) of the residual deformations estimated from the proposed model are smaller than those estimated from the conventional linear and period models, demonstrating that the proposed method is more suitable to model and analyze the surface deformation in wind-water erosion crisscross region.