Search Results (72)

This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (p < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) in high-density urban areas has surged exponentially. To address these computational and memory challenges in high-density urban PSInSAR processing, this paper proposes an efficient method for integrating regular grid partitioning and connected component constraints. First, adaptive dynamic regular grid partitioning was employed to divide monitoring areas into sub-blocks, balancing memory usage and computational efficiency. Second, a weighted least squares adjustment model using common PS points in overlapping regions eliminated systematic inter-sub-block biases, ensuring global consistency. A graph-based connected component constraint mechanism was introduced to resolve multi-component segmentation issues within sub-blocks to preserve discontinuous PS information. Experiments on TerraSAR-X data covering Fuzhou, China (590 km²), demonstrated that the method processed 1.4 × 10⁷ PS points under 32 GB memory constraints, where it achieved a 25-fold efficiency improvement over traditional global PSInSAR. The deformation rates and elevation residuals exhibited high consistency with conventional methods (correlation coefficient ≥ 0.98). This method effectively addresses the issues of memory overflow, connectivity loss between sub-blocks, and cumulative merging errors in large-scale PS networks. It provides an efficient solution for wide-area millimeter-scale deformation monitoring in high-density urban areas, supporting applications such as geohazard early warning and urban infrastructure safety assessment. Full article

The ground-based real aperture radar (GB-RAR), with its non-contact, high-precision, continuous monitoring capabilities, is widely used in bridge safety. To reduce noise interference in GB-RAR monitoring, a denoising method based on complementary ensemble empirical mode decomposition (CEEMD), wavelet threshold denoising (WTD), and principal component analysis (PCA) was applied to the safety monitoring of the East Lake High-tech Bridge in Wuhan. The method involved CECEEMD of GB-RAR data, WTD for high-frequency noise Intrinsic Mode Function (IMF) components, and PCA for low-frequency IMF power spectrum matrices to remove coloured noise. PCA shows a symmetric balance between noise removal and signal retention. The experimental results show that the proposed denoising method ensures the integrity of the reconstructed signal by symmetrically processing the IMF of high and low frequencies and improves the signal-to-noise ratio (SNR) of the three piers to 8.30, 19.87 and 15.06, respectively, and the Root Mean Square Errors (RMSE) are 0.10 mm, 0.06 mm and 0.09 mm, respectively. Noise removal reduced uncertainty by 42.3%, 35.8%, and 33.1%, demonstrating the method’s effectiveness in enhancing deformation monitoring precision. Full article

Integrated Sensing and Communication (ISAC) is an important trend for future commutation networks. The Communication Base Station (CBS) can be used as a Ground-Based Synthetic Aperture Radar (GB-SAR). By using Synthetic Aperture Radar (SAR) images obtained at a different time, GB-SAR will have the ability to detect millimeter-level ground deformations with Interferometric SAR (InSAR) processing through a phase difference operation. In this paper, we investigated the observation and performance for millimeter-level ground deformation detection based on the CBS with Differential InSAR (D-InSAR) for the first time. Building on the characteristics of short temporal sampling intervals, an in-depth investigation was conducted into the process of detecting deformations using the CBS. A practical experimental scenario was established, and the high coherence between adjacent images resulting from short temporal sampling intervals was leveraged to enhance the phase Signal-to-Noise Ratios (SNRs) through time series Differential Interferometric Phase sample averaging. On this basis, the first experimental result is given, which indicates that CBS can accurately capture millimeter-level deformations with a maximum error of 0.3437 mm. The experimental results confirm the feasibility and accuracy of employing CBSs as GB-SAR systems for monitoring ground deformations. Full article

The Daguangbao landslide (DGBL), triggered by the 2008 Wenchuan earthquake, is a rare instance of super-giant landslides globally. The post-earthquake evolution of the DGBL has garnered significant attention in recent years; however, its deformation patterns remain poorly characterized owing to the complex local topography. In this study, we present the first observations of the surface dynamics of DGBL by integrating satellite- and ground-based InSAR data complemented by kinematic interpretation using a LiDAR-derived Digital Surface Model (DSM). The results indicate that the maximum line-of-sight (LOS) displacement velocity obtained from satellite InSAR is approximately 80.9 mm/year between 1 January 2021, and 30 December 2023, with downslope displacement velocities ranging from −60.5 mm/year to 69.5 mm/year. Ground-based SAR (GB-SAR) enhances satellite observations by detecting localized apparent deformation at the rear edge of the landslide, with LOS displacement velocities reaching up to 1.5 mm/h. Our analysis suggests that steep and rugged terrain, combined with fragile and densely jointed lithology, are the primary factors contributing to the ongoing deformation of the landslide. The findings of this study demonstrate the effectiveness of combining satellite- and ground-based InSAR systems, highlighting their complementary role in interpreting complex landslide deformations. Full article

Effective noise reduction strategies are crucial for improving the precision of Ground-Based Synthetic Aperture Radar (GB-SAR) technology in bridge deflection measurement, particularly in mitigating the signal noise introduced by complex environmental factors, and thereby ensuring reliable structural health assessments. This study presents an innovative gradual de-noising method that integrates an Improved Second-Order Blind Identification (I-SOBI) algorithm with Fast Fourier Transform (FFT) featuring Adaptive Cutoff Frequency Selection (A-CFS) for reducing the complex environmental noises. The novel method is a two-stage process. The first stage employs the proposed I-SOBI to preserve the contribution of effective information in separated signals as much as possible and to recover pure signals from noisy ones that have nonlinear characteristics or are non-Gaussian in distribution. The second stage utilizes the FFT with the A-CFS method to further deal with the residual high-frequency noises still within the signals, which is conducted under a proper cutoff frequency to ensure the quality of de-noised outputs. Through meticulous simulation and practical experiments, the effectiveness of the proposed de-noising method has been comprehensively validated. The experimental results state that the method performs better than the traditional Second-Order Blind Identification (SOBI) method in terms of noises reduction capabilities, achieving a higher accuracy of bridge deflection measurement using GB-SAR. Additionally, the method is particularly effective for de-noising nonlinear time-series signals, making it well-suited for handling complex signal characteristics. It significantly contributes to the provision of reliable bridge dynamic-behavior information for infrastructure assessment. Full article

The Tibetan Plateau (TP) is the largest glacier reserve outside the Antarctic and Arctic regions. Climate warming has affected the reserve of freshwater resources and led to frequent glacier disasters. However, due to its extreme environment of hypoxia and low pressure, it is extremely difficult to obtain data. Compared with other traditional monitoring methods such as makers and satellite remote sensing technology, Ground-Based (GB) radar systems have the advantages of convenient carrying and installation, sub-second level sampling, and sub-millimeter measurement accuracy. They can be used as an effective way to study the short-term rapid movement changes in glaciers. Based on a self-built GB radar system, monitoring experiments were conducted on two glacier termini on the TP. The movement speed of the Rongbuk glacier terminus on Mount Qomolangma was obtained through time-series interferometric measurement as 4.10 cm/day. When the altitude was about 5200 m, the glacier movement speed was 7.74 cm/day, indicating the spatial differences with altitude changes. And in another region, the movement speed of the Yangbulake glacier terminus on Mount Muztag Ata was 198.96 cm/day, indicating significant changes in glacier movement. The cross-validation of Sentinel-1 data during the same period proved the effectiveness of GB radar system interferometry in measuring glacier movement speed and also provided field validation data for remote sensing inversion. Full article

This study assesses the relative performance of Sentinel-1 and -2 and their combination with topographic information for plow agricultural land soil salinity mapping. A learning database made of 255 soil samples’ electrical conductivity (EC) along with corresponding radar (R), optical (O), and topographic (T) information derived from Sentinel-2 (S2), Sentinel-1 (S1), and the SRTM digital elevation model, respectively, was used to train four machine learning models (Decision tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB). Each model was separately trained/validated for four scenarios based on four combinations of R, O, and T (R, O, R+O, R+O+T), with and without feature selection. The Recursive Feature Elimination with k-fold cross validation (RFEcv 10-fold) and the Variance Inflation Factor (VIF) were used for the feature selection process to minimize multicollinearity by selecting the most relevant features. The most reliable salinity estimates are obtained for the R+O+T scenario, considering the feature selection process, with R² of 0.73, 0.74, 0.75, and 0.76 for DT, GB, RF, and XGB, respectively. Conversely, models based on R information led to unreliable soil salinity estimates due to the saturation of the C-band signal in plowed lands. Full article

Atmospheric phase error is the main factor affecting the accuracy of ground-based synthetic aperture radar (GB-SAR). The atmospheric phase screen (APS) may be very complicated, so the atmospheric phase correction (APC) model is very important; in particular, the parameters to be estimated in the model are the key to improving the accuracy of APC. However, the conventional APC method first performs phase unwrapping and then removes the APS based on the least-squares method (LSM), and the general phase unwrapping method is prone to introducing unwrapping error. In particular, the LSM is difficult to apply directly due to the phase wrapping of permanent scatterers (PSs). Therefore, a novel methodology for estimating parameters of the APC model based on the maximum likelihood estimation (MLE) and the Gauss-Newton algorithm is proposed in this paper, which first introduces the MLE method to provide a suitable objective function for the parameter estimation of nonlinear far-end and near-end correction models. Then, based on the Gauss-Newton algorithm, the parameters of the objective function are iteratively estimated with suitable initial values, and the Matthews and Davies algorithm is used to optimize the Gauss-Newton algorithm to improve the accuracy of parameter estimation. Finally, the parameter estimation performance is evaluated based on Monte Carlo simulation experiments. The method proposed in this paper experimentally verifies the feasibility and superiority, which avoids phase unwrapping processing unlike the conventional method. Full article

The radio frequency (RF) data generated from a single-chip millimeter-wave (mmWave) ground-based multi-input multi-output (GB-MIMO) radar can provide a highly robust, precise measurement for deformation in harsh environments, overcoming challenges such as different lighting and weather conditions. Monitoring deformation is significant for safety factors in different applications, such as detecting and monitoring the ground stability of underground mines. However, radar images can experience different types of clutter and artifacts besides the spreading effects caused by the side lobes, resulting in the foremost challenge of suppressing clutter and monitoring deformation.In the state of the art, the introduced frameworks usually include many filters proposed for different types of noise, with commercial systems typically using an amplitude threshold. This paper proposes a framework for monitoring the deformation, where the essential process is to apply a data-driven threshold to the amplitude heatmap, detect the deformation, and eliminate noise. The proposed threshold is an iterative approach based on radar imagery statistics, and it performs well for the collected dataset. The principal advantage of our proposed framework is simplicity, reducing the burden of using different filters. We can consider the dynamic threshold based on data statistics as a data-driven machine learning tool. The results show promising performance for our method in monitoring the deformation and removing clutter compared to the benchmark method. Full article

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional $\omega$K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the $Group$-$Nstream$ processing model, and the $Fthread$-$Group$-$Nstream$ processing model. The $Fthread$-$Group$-$Nstream$ processing model utilizes $Fthread$, $Group$, and $Nstream$ for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm’s performance in dynamic parallelism and alleviating the issue of serial execution within the $Group$-$Nstream$ processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. Full article

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority. Full article

This paper presents three refinements in ground-based radar interferometer (GB-radar) measurement for bridge load testing: (1) GB-radar phase jumps were detected for the first time on bridge tower displacement monitoring, and a recovery method is presented to obtain the correct unwrapped value; (2) a precise displacement projection method considering target deformation was exploited, and a case study of the Fifth Nanjing Yangtze River Bridge (FNYRB) GB-radar campaign shows that a centimeter-level compensation can be achieved; (3) a post-construction settlement phenomenon was found during the FNYRB static load tests, characterized by 0.31 mm/min, which accumulated up to 25 mm. In addition, the dynamic monitoring capabilities of GB-radar for the bridge tower and girder were verified, highlighting its potential for bridge structural health monitoring (SHM). The insights gained from this study offer valuable recommendations for future GB-radar bridge displacement monitoring. Full article

Ground-Based Synthetic Aperture Radar (GB-SAR), due to its high precision, high resolution, and real-time capabilities, is widely used in the detection of slope deformations. However, the weak scattering coefficient in low-coherence areas poses a great challenge to the observation point selection accuracy. This paper introduces a selection process for reliable observation points that integrates phase and spatial information. First, for various observation points with differentiated stability, we propose to utilize maximum likelihood estimation (MLE) methods to achieve stability assessment. Second, a phase correction approach is proposed to address unwrapped phase errors encountered at less stable points. Third, adaptive filtering for deformation information at observation points is achieved using estimated variance combined with wavelet filtering thresholds. Finally, in dealing with unknown deformation trends, we propose utilizing a clustering method to accurately identify these trends, thereby enhancing the precision in identifying reliable observation points (ROPs). The experimental results demonstrate that this method enhances the accuracy of observation point selection in low-coherence areas, providing a broader observational field for deformation detection. Full article