High-precision applications supported by Low Earth Orbit (LEO) satellites rely heavily on accurate spatiotemporal references—comprising both orbit and clock offset—typically established through Precise Orbit Determination (POD). Conventional POD models often assume that Receiver Dependent Biases (RDBs), both the Receiver Code Bias (RCB) and Receiver Phase Bias (RPB), remain stable over short time span, which is not necessarily true in real situation. In this paper, we refine the conventional POD model by explicitly parameterizing the time-varying difference between the RCB and RPB, referred as Dynamic RDB (DRDB), and then a DRDB-Assimilated (float) model is proposed. Moreover, a constrained DRDB-Assimilated model is presented by physically modelling the DRDB behavior with particularly considering the thermal environment of LEO. Using data from a commercial SAR LEO satellite, both models successfully captured the evident DRDB variations ranging approximately from − 25 to 25 m, following an asymmetric periodic trend closely tied to the relative motion among the Sun, Earth, and satellite. With respect to the conventional model, both new models can achieve the comparable orbit accuracies at the centimeter level, but substantial improvements in timing performance. Both models reduced arc-boundary receiver clock discontinuities, from tens of nanoseconds to sub-nanosecond levels. In terms of frequency stability, the constrained DRDB-Assimilated model showed the similar short-term stability to the conventional one but notable improvements in medium- and long-term stability (beyond 102 s). These research findings underscore the importance of processing RDBs dynamics in LEO POD and highlight the constrained DRDB-Assimilated model as a robust model to obtain accuracy orbits and time for LEO satellites.

The South-to-North Water Diversion Project (SNWDP) is a strategic national project in China. In recent years, uneven surface deformation has occurred frequently along the project route, potentially causing leakage, fractures, and other hazards that threaten operational safety. The high-precision routine deformation monitoring is crucial to ensure the stable operation of this significant hydraulic infrastructure. Interferometric synthetic aperture radar (InSAR) can capture subtle ground deformations over extensive areas. However, conventional image datasets often suffer from limitations such as low spatial and temporal resolution or high costs. In this study, Chinese small SAR satellites, including "Fucheng-1" and "Shenqi" series, were employed to monitor large-scale water diversion projects for the first time. Distributed scatterer InSAR (DS-InSAR) technology under multi-source SAR imagery was utilized to monitor the Tianjin section of the SNWDP, supplemented with corner reflector (CR) for analyzing deformation results in low-coherence agricultural areas along the route. The monitoring results indicate a high spatial consistency between the results derived from Sentinel-1A and the "Fucheng-1" and "Shenqi" satellites. The average coherence coefficients of the dual-star constellation show an improvement compared to Sentinel-1A with a similar temporal baseline. Among the selected monitoring points, the minimum Maximum Absolute Error (MaxAE) value is 1.699 mm/yr, and the highest Pearson correlation coefficient (PCC) reaches 0.977, which indicates stable orbit control capabilities, dual-star constellation interferometry capabilities, and time-series resolution capabilities of Chinese small SAR satellites. The results indicate significant regional ground subsidence happened in Xiong County and Gu'an area of Langfang, Hebei Province, although a recent trend of subsidence mitigation has emerged. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Time-series Interferometric Synthetic Aperture Radar (TS-InSAR) enables precise, wide-area ground deformation monitoring but suffers from decorrelation and heavy computation with large archives of satellite imagery. To address these challenges, this study applies temporal dimension image compression to 199 Sentinel-1 A scenes (March 25, 2017–May 11, 2024) covering the Jinchuan mining area, China. Specifically, through the construction of a covariance matrix, PL (Phase-Linking) for phase compensation, and dimensionality reduction and reconstruction processes, the time-series image datasets are compressed into 22 virtual images. These virtual images are then processed within the Persistent Scatterer Interferometry (PS-InSAR) framework, referred to as mini stack technology. Results show that (1) the time-series image compression mini stack technology significantly enhances computational efficiency compared to traditional time-series InSAR (TS-InSAR) methods, relieving the decorrelation issue caused by long time spans in conventional interferograms; (2) The average coherence coefficient obtained from the virtual image stack improved by 32.8

Distributed scatterer (DS) possesses a medium signal-to-noise ratio, and its interferometric phase is typically estimated from sample covariance matrix (SCM) using phase linking algorithm. However, the phase estimation of DS is very time consuming. The eigenvalue decomposition (EVD) methods benefit from highly optimized eigen-decomposition libraries and therefore have a higher computational efficiency. Nevertheless, EVD methods are still insufficient due to the massive data provided by modern SAR satellites with multipolarizations and high spatiotemporal resolution. The essence of conventional EVD methods is to decompose the SCM (or modified SCM) with a series of eigenvalue-eigenvector pairs and then select only the eigenvector corresponding to the largest eigenvalue as the optimal estimate. Inspired by that, we proposed a fast phase estimation approach based on power method (PM), a straightforward algorithm that approximates the largest eigenvalue and its associated eigenvector. The convergence rate of PM depends on the ratio between the second and the first largest eigenvalue, i.e., lambda(2)/lambda(1). We demonstrate that there is an intrinsic correlation between lambda(2)/lambda(1 )and the traditional quality measure (gamma(PTA)). Based on this correlation, we design two termination criteria for PM to enable fast and accurate phase estimation. Experiments with simulated and real SAR data demonstrate that PM achieves comparable phase estimation accuracy to EVD but is 3-6 times faster. In addition, we compare the computational efficiency and accuracy of EVD, PM, and the eigendecomposition-based maximum likelihood estimator of interferometric phase, providing practical guidance for different application scenarios.

Mining activities can trigger geological disasters, including slope instability and surface subsidence, posing a serious threat to the surrounding environment and miners’ safety. Consequently, the development of reasonable, effective, and rapid deformation monitoring methods in mining areas is essential. Traditional synthetic aperture radar(SAR) satellites are often limited by their revisiting period and image resolution, leading to unwrapping errors and decorrelation issues in the central mining area, which pose challenges in deformation monitoring in mining areas. In this study, persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology is used to monitor and analyze surface deformation of the Jinchuan mining area in Jinchang City, based on SAR images from the small satellites “Fucheng-1” and “Shenqi”, launched by the Tianyi Research Institute in Hunan Province, China. Notably, the dual-star constellation offers high-resolution SAR data with a spatial resolution of up to 3 m and a minimum revisit period of 4 days. We also assessed the stability of the dual-star interferometric capability, imaging quality, and time-series monitoring capability of the “Fucheng-1” and “Shenqi” satellites and performed a comparison with the time-series results from Sentinel-1A. The results show that the phase difference (SPD) and phase standard deviation (PSD) mean values for the “Fucheng-1” and “Shenqi” interferograms show improvements of 21.47% and 35.47%, respectively, compared to Sentinel-1A interferograms. Additionally, the processing results of the dual-satellite constellation exhibit spatial distribution characteristics highly consistent with those of Sentinel-1A, while demonstrating relatively better detail representation capabilities at certain measurement points. In the context of rapid deformation monitoring in mining areas, they show a higher revisit frequency and spatial resolution, demonstrating high practical value.

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.

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Interferometric Synthetic Aperture Radar (InSAR) is capable of detecting crust deformation. However, the accuracy is limited by spatiotemporal changes in the lower troposphere. In this paper, we constructed a periodic zenith total delay negative exponential function (PZTD-NEF) model of atmospheric spatiotemporal variation characteristics based on ERA-5 data to alleviate the temporal oscillation bias introduced by tropospheric delay and improve the accuracy of time series InSAR (TS-InSAR) inversion of surface deformation. We evaluated the model's performance using the phase standard deviation (STD), atmospheric delay correlation coefficient with topography and the spatial structure function. The results were compared with a linear topography-dependent empirical model, generic atmospheric correction online service (GACOS) and ERA-5 methods. Our method reduces the STD of the phase of 83% of the interferograms by 12.8%. For vertical stratification delay correction, the correlation between the proposed method and the Linear, GACOS, and ERA-5 reached 0.734, 0.708, and 0.729, respectively. We found that accounting for spatiotemporal variation characteristics of tropospheric delay can alleviate the seasonal oscillations of vertical stratification delay and improve the accuracy of the deformation time series solution by 40.04%. We also used the Kunming continuous operation reference station system (KMCORS) to verify the displacement results of our method.

As an important infrastructure for air transportation and cities, airports are an important part of the comprehensive transportation system. Therefore, land subsidence monitoring of airports is of extremely important significance. This article uses 32 scenes of Sentinel-1A data and based on Time Series InSAR (Interferometric Synthetic Aperture Radar) technology to obtain land subsidence information of Zhengzhou Xinzheng International Airport from November 2020 to December 2021.The results show that there is no significant surface settlement in the airport terminal area, but the density of target points obtained from the airport runway is insufficient and cannot well reflect the deformation on the runway.

In recent years, Point Cloud signal processing has received increased attention. Airborne LiDAR can measure the ground and generate point clouds in a cost-effective and rapid way. In order to generate an accurate Digital Terrain Model (DTM), non-ground point such as buildings, vehicles, and vegetation must be removed, that is, point cloud filtering. In this paper, we propose an adaptive morphological filtering algorithm designed specifically for urban area. The morphological filtering algorithm transforms the point cloud into a grayscale image and identifies non-ground locations using morphological operations. However, the method requires setting the filter window size manually. Improper parameters will affect the accuracy of DTM. In order to improve the adaptivity and robustness of the morphological filtering algorithm. In this paper, we first segment the point cloud images using the integral image adaptive segmentation algorithm, and then detect the buildings using the proposed connected sets detection algorithm to automatically determine the size of the filtering window. In addition to this, we also propose dynamic thresholding for point cloud filtering, which performs better compared to previous methods where it is set to a constant threshold. The experimental results on 15 samples demonstrate the effectiveness of our proposed method. Our code will be available at https://github.com/xdu-whh/ICCCS2023-point-cloud-filtering.

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude.

InSAR technology provides a powerful tool for detecting large-scale surface deformation. In particular, the newly developed DS-InSAR method fused PS points has obvious advantages in monitoring bare land and vegetation covered areas, but at present, there is a lack of effectiveness evaluation of this method. Therefore, this paper takes Hongta District of Yuxi City as an example. The 29 scenes sentinel-1A data from January 2019 to December 2019 were processed and analyzed using the DS-InSAR method fused PS points. The research results show that the position and deformation trend of the inversion results of the three methods are highly consistent, the correlation between DS-InSAR method fused PS points and PS- InSAR and SBAS-InSAR methods is 0.9473 and 0.8583, respectively. But the spatial density of the measurement points (MPs) obtained by the DS-InSAR method of PS points is 11 times and 4 times that of the PS-InSAR method and SBAS-InSAR method respectively. It clearly shows that DS-InSAR method fused PS points has greater advantages than PS-InSAR and SBAS-InSAR methods in deformation mapping displacement, because the MPs density is higher. It is conducive to the detailed analysis of the spatio-temporal characteristics and deformation mechanism of deformation.

The conventional multi-temporal InSAR (MT-InSAR) technology suffers from the problems of low spatial density of monitoring points (MPs) and poor interferogram quality in monitoring reservoir bank landslides in mountainous areas. To address it, this study builds upon the Eigendecomposition-based Maximum-likelihood estimator of Interferometric phase (EMI) method, introduces Fisher information to adjust the weight of each interferometric pair, and further develops a new distributed scatterer (DS) phase optimization method, which is referred to as FEMI-DSInSAR in the text. We applied the FEMI-DSInSAR to retrieve the deformation history of landslides along a 100 km section of the Lancang River using 33 C-band Sentinel-1 images (January 2019–January 2022). The accuracy and reliability were validated by comparing the results with those obtained with EMI-DSInSAR and Stanford Method for Persistent Scatterers-Small Baseline Subset (StaMPS-SBAS) methods in terms of both interferogram quality and large-area deformation results. The differential interferograms obtained by the FEMI-DSInSAR method not only show better quality fringes, but also reduce the sum of phase difference (SPD) and the standard deviation of the phase (PSD) values by 8.9

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

Monitoring the surface subsidence in mining areas is conducive to the prevention and control of geological disasters, and the prediction and early warning of accidents. Hunan Province is located in South China. The mineral resource reserves are abundant; however, large and medium-sized mines account for a low proportion of the total, and the concentration of mineral resource distribution is low, meaning that traditional mining monitoring struggles to meet the needs of large-scale monitoring of mining areas in the province. The advantages of Interferometric Synthetic Aperture Radar (InSAR) technology in large-scale deformation monitoring were applied to identify and monitor the surface subsidence of coal mining fields in Hunan Province based on a Sentinel-1A dataset of 86 images taken from 2018 to 2020, and the process of developing surface subsidence was inverted by selecting typical mining areas. The results show that there are 14 places of surface subsidence in the study area, and accidents have occurred in 2 mining areas. In addition, the railway passing through the mining area of Zhouyuan Mountain is affected by the surface subsidence, presenting a potential safety hazard.

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.

Bridge deformation monitoring usually adopts contact sensors,and the implementation process is often limited by the environment and observation conditions,resulting in unsatisfactory monitoring accuracy and effect.Ground-Based Synthetic Aperture Radar(GBSAR)combined with corner reflectors was used to perform static load-loaded deformation destruction experiments on solid model bridges in a non-contact manner.The semi parametric spline filtering and its optimization method were used to obtain the monitoring results of the GBSAR radar's line of sight deformation,and the relative position of the corner reflector and the millimeter level deformation signals under different loading conditions were successfully extracted.The deformation transformation model from the radar line of sight direction to the vertical vibration direction was deduced.The transformation results of deformation monitoring and the measurement data such as the dial indicator were compared and analyzed.The occurrence and development process of bridge deformation and failure were successfully monitored,and the deformation characteristics of the bridge from continuous loading to eccentric loading until bridge failure were obtained.The experimental results show that GBSAR combined with corner reflector can be used for deformation feature acquisition,damage identification and health monitoring of bridges and other structures,and can provide a useful reference for design,construction and safety evaluation.

日兰高铁巨野煤田段农田遍布,合成孔径雷达干涉(InSAR)的时间失相干严重,可用于时序InSAR(MT-InSAR)分析的永久散射体(PS)稀少.将SAR数据限制在失相干影响较弱的10月至次年4月初并联合PS和分布式散射体(DS)有望解决该问题.然而,受限于SAR卫星的重访周期,仅采用10月至次年4月初的SAR影像会导致数据量变少.而当SAR数据较少、相干性较低时,难以准确估计协方差矩阵和相干矩阵,使得现有的DS相位估计方法误差较大.为此,提出了一种基于Fisher信息量的DS相位优化估计算法,利用Fisher信息量调节各干涉对的权重,抑制低相干干涉对的影响.通过模拟数据和真实数据验证了算法的可靠性和可行性.另外,构建了联合PS和DS的小基线(SBAS)干涉处理框架,在增加观测方程的同时保证干涉对的相干质量,从而实现形变信息的稳健估计.利用2020年10月至2021年4月间的Sentinel-1 SAR数据获取了日兰高铁巨野煤田段地表沉降,并结合已有的监测资料分析了地表沉降的成因及时空演化信息.研究结果表明:采用上述方法,能够根据10月至次年4月初的少量SAR数据监测高铁沿线沉降情况;日兰高铁巨野煤田段沿线仍在持续沉降,3 km内的平均形变速率集中在-3.5～-0.5 cm/a,与2015-2019年的观测结果一致,未出现加剧现象;巨野煤田段存在可能由断层活化、深层地下水流失等因素间接造成的更大范围地表沉降,并且沉降靠近高铁侧,这一点需引起注意.

Wuhan (China) is facing severe consolidation subsidence of soft soil and karst collapse hazards. To quantitatively explore the extent and causes of land subsidence in Wuhan, we performed multitemporal interferometry (MTI) analysis using synthetic aperture radar (SAR) data from the TerraSAR-X satellite from 2013 to 2017 and the Sentinel-1A satellite from 2015 to 2017. MTI results reveal four major subsidence zones in Wuhan, namely, Hankou (exceeding-6 cm/yr), Xudong-Qingshan (-3 cm/yr), Baishazhou-Jiangdi (-3 cm/yr), and JiansheYangluo (-2 cm/yr). Accuracy assessment using 106 levelling benchmarks and cross-validation between the two InSAR-based results indicate an overall root-mean-square error (RMSE) of 2.5 and 3.1 mm/yr, respectively. Geophysical and geological analyses suggest that among the four major subsiding zones, Hankou, Xudong-Qingshan, and Jianshe-Yangluo are located in non-karstic soft soil areas, where shallow groundwater (< 30 m) declines driven by engineering dewatering and industrial water depletion contribute directly to soft soil compaction. Subsidence in the Baishazhou-Jiangdi zone develops in the karst terrain with abundant underground caves and fissures, which are major natural factors for gradual subsidence and karst collapse. Spatial variation analysis of the geological conditions indicates that the stage of karst development plays the most important role in influencing kart subsidence, followed by municipal construction, proximity to major rivers, and overlying soil structure. Moreover, land subsidence in this zone is affected more via coupling effects from multiple factors. Risk zoning analysis integrating subsidence horizontal gradient, InSAR deformation rates, and municipal construction density show that the high-risk areas in Wuhan are mainly distributed in the Tianxingzhou and BaishazhouJiangdi zone, and generally spread along the metro lines.