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The Heifangtai Loess terrace in northwest China is frequently affected by landslides due to hydrological factors, establishing it as a significant research area for loess landslides. Advanced time-series InSAR technology facilitates the retrieval of surface deformation information, thereby aiding in the monitoring of landslide deformation status. However, existing methods that analyze deformation patterns do not fully exploit the displacement time series derived from InSAR, which hampers the exploration of potentially coexisting deformation patterns within the area. This study integrates InSAR with time-series clustering methods to reveal the surface deformation patterns and their spatial distribution characteristics in Heifangtai. Initially, utilizing the Sentinel-1 ascending and descending SAR data stack from January 2020 to June 2023, we optimize the interferometric phase based on distributed scatterer characteristics to reduce noise levels and obtain higher spatial density of measurement points. Subsequently, by combining the differential interferometric datasets from both ascending and descending orbits, the multidimensional small baseline subsets technique is employed to calculate the two-dimensional deformation time series. Finally, time-series clustering methods are utilized to extract the deformation patterns present and their spatial distribution from all measurement point time series. The results indicate that the deformation of the Heifangtai is primarily distributed around the surrounding area of the platform, with subsidence deformation being more intense than horizontal deformation. The entire terrace exhibits five deformation patterns: eastward subsidence, westward subsidence, vertical subsidence, westward movement, and eastward movement. The spatial distribution of these patterns suggests that the areas beneath the platform, namely Yanguoxia Town and Dangchuan Village, may be more susceptible to landslide threats in the future. Furthermore, wavelet analysis reveals the response relationship between rainfall and various deformation patterns, further enhancing the interpretability of these patterns. These findings hold significant implications for subsequent landslide monitoring, early warning, and risk prevention.

For meter-wave multiple-input multiple-output (MIMO) radar, the multipath of target echoes may cause severe errors in height measurement, especially in the case of complex terrain where terrain fluctuation, ground inclination, and multiple reflection points exist. Inspired by a sparse array with greater degrees of freedom and low mutual coupling, a height measurement method based on a sparse array is proposed. First, a practical signal model of MIMO radar based on a sparse array is established. Then, the modified multiple signal classification (MUSIC) and maximum likelihood (ML) estimation algorithms based on two classical sparse arrays (coprime array and nested array) are proposed. To reduce the complexity of the algorithm, a real-valued processing algorithm for generalized MUSIC (GMUSIC) and maximum likelihood is proposed, and a reduced dimension matrix is introduced into the real-valued processing algorithm to further reduce computation complexity. Finally, sufficient simulation results are provided to illustrate the effectiveness and superiority of the proposed technique. The simulation results show that the height measurement accuracy can be efficiently improved by using our proposed technique for both simple and complex terrain.

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios.

For real-time (RT) precise point positioning (PPP), the state space representation (SSR) information is often delayed due to possible communication delays and specific broadcast intervals. In this case, the positioning results will diverge and re-converge due to the increase of SSR products extrapolation errors. In addition, RT orbit and clock offset accuracy, as well as their extrapolation errors, will vary in different systems and satellites. We propose a PPP with ambiguity resolution (PPP-AR) method that combines a time-differenced carrier phase (TDCP) model, in which the characteristics of the orbit and clock are considered. Under normal communication, the PPP-AR solution is obtained by fixing satellites with small SSR product errors. When the communication is abnormal, the TDCP model is utilized to extrapolate user coordinates by considering different extrapolation error characteristics of satellites. The experimental results show that GPS and Galileo SSR products have better accuracy than BDS, with signal-in-space user ranger errors (SISREs) of 2.7, 2.2, and 8.6 cm, respectively. Optimizing the PPP stochastic model based on SISREs can effectively reduce the convergence time. Under 5 min SSR delay, SISREs caused by clock and orbit extrapolation for GPS/Galileo/BDS are 3.5, 1.4, and 2.6 cm, respectively. After optimizing the TDCP stochastic model based on extrapolation errors, the horizontal and vertical positioning accuracies can be maintained at 0.7 cm and 5.0 cm. For multi-GNSS, the combination of the TDCP and PPP-AR can overcome the influence of short delay. After optimizing the stochastic model, the GPS/Galileo/BDS positioning accuracy can be maintained at about 2.4 cm under 3 min delay, showing an accuracy improvement rate of 59.3% compared with the traditional method using only PPP. Additionally, the rapid PPP convergence results can be obtained by inheriting previous filter state information when the communication recovers normally.

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation.

The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly limits the usage of the InSAR deformation map, especially for spatiotemporal characterizing and mechanism inversion. Some conventional methods (e.g., spatial interpolation) rely only on the deformation measurements without considering the influence factors, leading to the inaccuracy of the deformation prediction. So, we propose a multifactor-based machine learning model, namely the K-RFR model, that combines K-means clustering and random forest regression algorithm to reconstruct a continuous deformation map, where the influence factors on ground deformation are considered, such as land use, geological engineering, and under groundwater extraction. We take the city of Xi’an, China, as the study area where SBAS-InSAR was used to obtain the ground deformation maps from 2012 to 2015. Fourteen influence factors are employed, including confined water level, change of confined water, phreatic water level, change of phreatic water, rainfall, ground fissures, stratigraphic lithology, landform, hydrogeology, engineering geology, type of land use, soil type, GDP, and DEM, where the K-means clustering method is used to reduce the influence of spatial heterogeneity. The study area is divided into three homogeneous regions and modeled independently, where the mean squared errors of region I–III are 2.9 mm, 2.3 mm, and 3.9 mm, respectively, and the mean absolute errors are 2.5 mm, 1.0 mm, and 2.8 mm, respectively. Finally, the continuous ground deformation maps of Xi’an from 2012 to 2015 are reconstructed. We compared the new method with two interpolation methods. Results show that the correlation coefficient between prediction and InSAR measurements of the new model is 0.94, whereas the ordinary Kriging method is 0.69, and the IDW method is only 0.63. This study provides an effective means to predict the continuous surface deformation over a large area.

Groundwater storage (GWS) in confined aquifer systems is often influenced by climate variability and anthropogenic activities, and it is vital to quantify their contributions for the purpose of groundwater management and surface water allocation plans. In this study, we characterize the spatiotemporal evolution of the GWS in confined aquifer systems across Hengshui, North China Plain, and investigate its relationships with changing climate conditions and human activities through the integration of InSAR-derived surface displacements with hydraulic head observations and precipitation data, during 2004–2010 and 2016–2020. Our results indicate that the GWS in confined aquifer systems decreased markedly by 4.59 ± 0.35 km3 with an accelerating trend during the study period. The GWS variations show a strong correlation with precipitation during irrigation periods (March to July), and hence, the climate and anthropogenic-driven GWS variations can be separated from each other with a linear model. We find that the GWS depletion caused by climate variability and anthropogenic activities were −0.31 ± 0.10 km3 and −4.28 ± 0.40 km3, respectively, during the study period. The mean contribution of anthropogenic activities to the GWS variations was −71.9%, implying that the GWS variations in confined aquifer systems were primarily anthropogenic driven. It is also found that the well observations alone poorly characterize the spatiotemporal evolution of the GWS due to their limited spatial density, and the integrated InSAR/well approach appears to be promising for overcoming this challenge.

The orbit maneuver detection is crucial in Global Navigation Satellite System (GNSS) precise orbit determination, which is necessary for adjusting data processing strategies. The frequency of orbit maneuvers for the BeiDou Navigation System is significantly higher than that of other navigation systems, especially for geosynchronous orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. We propose a novel real-time and postprocessing method for detecting orbit maneuvers for BeiDou satellites based on the orbit differences between the epoch-updated orbit estimated using square root information (SRIF) and the predicted orbit according to the precise orbit estimated during non-maneuver period, as well as the orbital state difference during maneuver and non-maneuver periods. This method has significant advantages over using observation residuals and it is not affected by observation outliers, thus improving the accuracy and timeliness of orbit maneuver detection. We demonstrated that 32 orbit maneuver events of BeiDou satellites were successfully detected in 2022, of which 1 was for medium Earth orbit (MEO), 7 were for IGSO with an average detected maneuvering time of 7–8 min, and 24 were for GEO satellites with an average detected time of 4–5 min. Moreover, our method can be easily integrated into current real-time filter-based precise orbit determination (POD) processing without any extra task line, which simplifies the overall data processing. The data used in this method can be accessed easily, including GNSS observation data, broadcast ephemeris, and other open-source information files.

Today, synthetic aperture radar (SAR) satellites provide large amounts of SAR data at unprecedented temporal resolutions, which promotes hazard dynamic monitoring and disaster mitigation with interferometric SAR (InSAR) technology. This study focuses on big InSAR data dynamical processing in areas of serious decorrelation and large gradient deformation. A new stepwise temporal phase optimization method is proposed to alleviate the decorrelation, customized for deformation parameter dynamical estimation. Subsequently, the sequential estimation theory is introduced to the intermittent small baseline subset (ISBAS) approach to dynamically obtain deformation time series with dense coherent targets. Then, we analyze the reason for the unstable accuracy of deformation parameters using sequential distributed scatterers-ISBAS technology, and construct five indices to describe the quality of deformation parameters pixel-by-pixel. Finally, real data of the post-failure Baige landslide at the Jinsha River in China is used to demonstrate the validity of the proposed approach.

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country’s disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating “three-dimensional deformation and soil moisture content” combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters.

The Weihe Basin, located in central China, is a Cenozoic rift basin that is characterized by a complicated geological background, with intense tectonic movement and relatively significant seismic activity. The faulting behaviors, slip rates, geometrical structures, and possible surface traces of the active faults in the Weihe Basin are still not well known. The goal of this work is to provide a more complete picture of recent vertical ground deformation of the basin and to identify active faults. We derived two basin-wide average InSAR deformation maps during 2015–2019 using C-band Sentinel-1A/B and L-band ALOS PALSAR2 ScanSAR imagery. The basin-wide vertical and east–west deformation components decomposed from ascending and descending InSAR measurements show that vertical movement dominates the deformation of the Weihe Basin. Subsidence and uplift maxima both occurred over the Xi’an region at rates of about −146 and 20 mm/y, respectively. A subsidence of ~38 mm/y was found at Sanyuan, Fuping, Weinan, Lantian, Qianxian, and Xingping while an uplift of ~11 mm/y was found at Fufeng, Huxian, Jingyang, Fuping, Pucheng, and Huaxian. The significant vertical deformation in these regions is spatially linked to previously identified or unmapped faults. A slip rate of ~13.7 mm/y on faults F20, F6, F10, and F7 explained the observed uplift of up to 5 mm/y in the Fufeng and Wugong areas. Furthermore, surface fault traces were clearly identified for faults F7-1, F8-1, F20, F25, and F26 based on discontinuities in the InSAR-derived vertical deformation measurements. Our results provide an accurate and economical way to delineate the surface deformation and fault movement and the associated geohazards over the Weihe Basin, and offer independent unprecedented data for further geological and geophysical interpretation.

Global Navigation Satellite System (GNSS) positioning technology has become the most effective method for real-time three-dimensional landslide monitoring. However, the GNSS observation signal is easily affected by the presence of a complex landslide environment with high occlusion and strong interference, in which case its accuracy and reliability cannot meet the requirements of landslide deformation monitoring. Although the accelerometers have strong autonomous working capacities and can complement the GNSS landslide monitoring technology, regular GNSS/accelerometer coupled deformation monitoring technology relies on high-quality GNSS measurement information in order to obtain high-precision accelerometer-reckoned results, derived by accurately estimating the baseline shift error (BSE). When the GNSS signal suffers severe interference, the GNSS monitoring error will be partially absorbed by the BSE component after Kalman filtering, resulting in the divergence of the deformation solution. In this study, an abnormal observation variance inflation model was used to process the simulated landslide monitoring data (GNSS and accelerometer raw observation) for three typical scenes—GNSS signal normally locked, signal partially lost, and short-term interruption. The results were as follows: (1) When the GNSS signal was normally locked, the accuracy was comparable to that of the coupled solution employing an accelerometer (the Root Mean Square (RMS) values in the East (E), North (N) and Upward (U) directions were 0.11 cm, 0.33 cm, and 0.30 cm, respectively). (2) When the GNSS signal was partially lost, the accelerometer could effectively suppress the low-precision float solution of the GNSS, but here, the accuracy of the coupled solution would also decrease with the duration of the floats (the RMS values were E—1.21 cm, N—0.31 cm, and U: 0.58—cm, respectively, when the floats lasted for 10 s, and increased to E—3.09 cm, N—0.39 cm, and U—1.14 cm when they lasted for 20 s, wherein E was the main simulated sliding direction). (3) When the GNSS signal was interrupted for a short time, the accuracy of the coupled solution gradually decreased during continuous interruption, and decreased more quickly during the sliding period of the landslide (when the interruption persisted for 10 s, the RMS values in the simulated landslide stability period were E—0.61 cm, N—0.24 cm, and U—0.25 cm, respectively, while in the simulated landslide sliding period they reached E—4.10 cm, N—6.84 cm, and U—2.30 cm). However, raw observations of the accelerometer could still effectively be used to assist in identifying the real state of the landslide, thereby providing auxiliary information pertinent to early landslide disaster warning.

Due to the adverse influence of landslide disasters on human lives, property, and infrastructures, slope reliability analysis has attracted worldwide attention. However, many problems such as the neglect of the uncertainty in the water table level and the balance between the performance and efficiency in conventional models are still unresolved. This study investigates the influence of the uncertainty in the water table level on the benefit of considering such uncertainty in slope reliability analysis. For this purpose, a new method, i.e., a dynamic whale optimization algorithm (WOA)–Gaussian process regression (GPR) agent model using uniform design with the consideration of uncertainty in the groundwater level, is proposed for slope probabilistic analysis in this paper. Then the developed technique is integrated with Monte Carlo Simulation (MCS) to obtain the slope failure probability. The benefit of the proposed method is illustrated through two practical landslides. The results demonstrate that the developed technique has better performance, as compared to MCS, the v-support vector machine (v-SVR), and the generalized regression neural network (GRNN). This may be attributed to the dynamic updating of the training samples provided by the uniform design, the optimal hyper-parameters optimized by WOA, or the GPR model that has strong generalization ability with limited samples. Furthermore, a small failure probability is obtained without considering the groundwater level uncertainty, which offers an optimistic estimate of landslide stability. Therefore, it is necessary to consider the probabilistic features of the groundwater level, especially for complicated landslides in high mountainous areas where the location of the water table level is not accurately available due to their inaccessibility to people and instruments.

Evapotranspiration (ET) plays an important role in the hydrological cycle of river basins. Studying ET in the Yellow River Basin (YRB) is greatly significant for the scientific management of water resources. Here, we made full use of the advantages of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) gravity satellites for monitoring large-scale hydrological changes to calculate the terrestrial water storage anomaly (TWSA) and terrestrial water flux in the YRB from May 2002 to June 2020. Furthermore, combined with terrestrial water flux, precipitation, and runoff data, ET in the YRB was calculated based on the water budget equation and then compared with other traditional ET products. The mutation of annual mean ET was identified by the Mann–Kendall trend test method, and the seasonal and interannual variations of ET were explored. ET was closely related to precipitation. Annual mean ET exhibited a sudden change in 2011, with an insignificant downward trend from 2003 to 2010, followed by an increasing trend from 2011 to 2019, particularly after 2016. Compared with the traditional ET monitoring methods and products, the ET estimated by GRACE/GRACE-FO observations provides a new way to effectively obtain continuous and reliable ET data in a wide range of river basins.

Serious land subsidence and ground fissure (GF) disasters have brought huge economic losses to the Su-Xi-Chang area (China) and threatened the safety of its residents. To better understand the development of these disasters, it is urgent to carry out long-term and large-scale deformation monitoring in this region. In this study, based on time-series interferometric synthetic aperture radar (InSAR) technology, ground deformation characteristics were obtained at different periods. Meanwhile, Fast Lagrangian Analysis of Continua in Three Dimensions (FLAC3D) version 5.00 was used to study the stress, seepage field, and displacement changes in the soil layers caused by pumping activities at the bedrock bulge. The results showed that three subsidence centers were located in Suzhou, Wuxi, and Changzhou from 2007 to 2010. The ground fissures in Guangming village had obvious differential settlements and intense activities. The land subsidence in the Su-Xi-Chang area was under control from 2018 to 2021, while there was a relative rebound in most areas. Combined with numerical simulation and geological data, we demonstrated that pumping activities would accelerate and intensify the land subsidence process, and differential subsidence was prone to occur at the buried hill, which in turn led to the formation of ground fissures. By comparing the characteristics of ground deformation in different periods, it was proven that banning groundwater exploitation is an effective measure for preventing and controlling such disasters.

Real-time cycle slip detection and repair is one of the key issues in global positioning system (GPS) high precision data processing and application. In particular, when GPS stations are in special environments, such as strong ionospheric disturbance, sea, and high-voltage transmission line interference, cycle slip detection and repair in low elevation GPS observation data are more complicated than those in normal environments. For low elevation GPS undifferenced carrier phase data in different environments, a combined cycle slip detection algorithm is proposed. This method uses the first-order Gauss–Markov stochastic process to model the pseudorange multipath in the wide-lane phase minus narrow-lane pseudorange observation equation, and establishes the state equation of the wide-lane ambiguity with the pseudorange multipath as a parameter, and it uses the Kalman filter for real-time estimation and detects cycle slips based on statistical hypothesis testing with a predicted residual sequence. Meanwhile, considering there are certain correlations among low elevation, observation epoch interval, and ionospheric delay error, a second-order difference geometry-free combination cycle slip test is constructed that takes into account the elevation. By combining the two methods, real-time cycle slip detection for GPS low elevation satellite undifferenced data is achieved. A cycle slip repair method based on spatial search and objective function minimization criterion is further proposed to determine the correct solution of the cycle slips after they are detected. The whole algorithm is experimentally verified using the static and kinematic measured data of low elevation satellites under four different environments: normal condition, high-voltage transmission lines, dynamic condition in the sea, and ionospheric disturbances. The experimental results show that the algorithm can detect and repair cycle slips accurately for low elevation GPS undifferenced data, the difference between the float solution and the true value for the cycle slip does not exceed 0.5 cycle, and the differences obey the normal distribution overall. At the same time, the wide-lane ambiguity and second-order difference GF combination sequence calculated by the algorithm is smoother, which give further evidence that the algorithm for cycle slip detection and repair is feasible and effective, and has the advantage of being immune to the special observation environments.

This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement.

The positioning, navigation, and timing (PNT) service of the Global Navigation Satellite System (GNSS) is developing in the direction of real time and high precision. However, there are some problems that restrict the development of real-time and high-precision PNT technology. Satellite orbit maneuvering is one of the factors that reduce the reliability of real-time navigation products, especially the high-frequency orbit maneuvering of geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. The BeiDou Navigation Satellite System (BDS) constellation is designed to contain GEO, IGSO, and medium earth orbit (MEO). These orbit maneuvers bring certain difficulties for data processing, especially for BeiDou satellites, such as decreased real-time service performance, which results in real-time navigation products including unusable maneuvered satellites. Additionally, the performance of real-time navigation products will decrease because the orbit maneuvers could not be known in advance, which diminishes the real-time PNT service performance of BDS for users. Common users cannot obtain maneuvering times and strategies owing to confidentiality, which can lead to a decline in the BDS real-time service performance. Thus, we propose a method to predict orbit maneuvers. BDS data from the broadcast ephemeris were analyzed to verify the availability of the proposed method. In addition, the results of real-time positioning were analyzed by using ultra-rapid orbit products, demonstrating that their reliability is improved by removing maneuvered satellites in advance. This is vital to improve the reliability of real-time navigation products and BDS service performance.

Many debris-covered glaciers are broadly distributed across High Mountain Asia and have made a number of contributions to water circulation for Qinghai-Tibet Plateau (QTP). The formation of large supraglacial lakes poses risks for glacier lake outburst floods (GLOFs). Therefore, it is important to monitor the movement of glaciers and to analyze their spatiotemporal characteristics. In this study we take Cuolangma glaciers in the central Himalayas as study targets, where glacier No.1 is a lake-terminating debris-covered glacier and glacier No.2 is a land-terminating debris-covered glacier. The 3D deformation time series is firstly estimated by using the Pixel Offset-Small Baseline Subsets (PO-SBAS) based on the ascending and descending Sentinel-1 datasets spanning from January to December 2018. Then the horizontal and vertical time series displacements are obtained to show their spatiotemporal features. The velocities of glacier No.1 in horizontal and vertical direction were up to 16.0 ± 0.04 m/year and 3.4 ± 0.42 m/year, respectively, and the ones of the glacier No.2 were 12.0 ± 0.07 m/year and 2.0 ± 0.27 m/year, respectively. Next, the correlation between the precipitation and the surface velocity suggests that the glacier velocity does not show a clear association with daily precipitation alone. Finally, the debris-covered glaciers evolution is evaluated which shows that the tongue of the glacier No.1 is wasting away and the transition of glacier No.2 from land-terminating to lake-terminating is a probable scenario in the later period of glacier wastage. This research can significantly serve for glacier multidimensional monitoring and the mitigation of hazardous disaster caused by debris-covered glaciers in the central Himalayas.

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) satellites are important for studying regional gravitational field changes caused by strong earthquakes. In this study, we chose Chile, one of Earth’s most active seismic zones to explore the co-seismic and post-seismic gravitational field changes of the 2010 Mw 8.8 Maule earthquake based on longer-term GRACE and the newest GRACE-FO data. We calculated the first-order co-seismic gravity gradient changes (GGCs) and probed the geodynamic characteristics of the earthquake. The earthquake caused significant positive gravity change on the footwall and negative gravity changes on the hanging wall of the seismogenic fault. The time series of gravity changes at typical points all clearly revealed an abrupt change caused by the earthquake. The first-order northern co-seismic GGCs had a strong suppressive effect on the north-south strip error. GRACE-FO results showed that the latest post-seismic gravity changes had obvious inherited development characteristics, and that the west coast of Chile maybe still affected by the post-seismic effect. The cumulative gravity changes simulated based on viscoelastic dislocation model is approximately consistent with the longer-term GRACE and the newest GRACE-FO observations. Our results provide important reference for understanding temporal and spatial gravity variations associated with the co-seismic and post-seismic processes of the 2010 Maule earthquake.

In this study, an uncombined precise point positioning (PPP) model was established and was used for estimating fractional cycle bias (FCB) products and for achieving ambiguity resolution (AR), using GPS, BDS-2, and Galileo raw observations. The uncombined PPP model is flexible and efficient for positioning services and generating FCB. The FCBs for GPS, BDS-2, and Galileo were estimated using the uncombined PPP model with observations from the Multi-GNSS Experiment (MGEX) stations. The root mean squares (RMSs) of the float ambiguity a posteriori residuals associated with all of the three GNSS constellations, i.e., GPS, BDS-2, and Galileo, are less than 0.1 cycles for both narrow-lane (NL) and wide-lane (WL) combinations. The standard deviation (STD) of the WL combination FCB series is 0.015, 0.013, and 0.006 cycles for GPS, BDS-2, and Galileo, respectively, and the counterpart for the NL combination FCB series is 0.030 and 0.0184 cycles for GPS and Galileo, respectively. For the BDS-2 NL combination FCB series, the STD of the inclined geosynchronous orbit (IGSO) satellites is 0.0156 cycles, while the value for the medium Earth orbit (MEO) satellites is 0.073 cycles. The AR solutions produced by the uncombined multi-GNSS PPP model were evaluated from the positioning biases and the success fixing rate of ambiguity. The experimental results demonstrate that the growth of the amount of available satellites significantly improves the PPP performance. The three-dimensional (3D) positioning accuracies associated with the PPP ambiguity-fixed solutions for the respective only-GPS, GPS/BDS-2, GPS/Galileo, and GPS/BDS-2/Galileo models are 1.34, 1.19, 1.21, and 1.14 cm, respectively, and more than a 30% improvement is achieved when compared to the results related to the ambiguity-float solutions. Additionally, the convergence time based on the GPS/BDS-2/Galileo observations is only 7.5 min for the ambiguity-fixed solutions, and the results exhibit a 53% improvement in comparison to the ambiguity-float solutions. The values of convergence time based on the only-GPS observations are estimated as 22 and 10.5 min for the ambiguity-float and ambiguity-fixed solutions, respectively. Lastly, the success fixing rate of ambiguity is also dramatically raised for the multi-GNSS PPP AR. For example, the percentage is approximately 99% for the GPS/BDS-2/Galileo solution over a 10 min processing period. In addition, the inter-system bias (ISB) between GPS, BDS-2, and Galileo, which is carefully considered in the uncombined multi-GNSS PPP method, is modeled as a white noise process. The differences of the ISB series between BDS-2 and Galileo indicate that the clock datum bias of the satellite clock offset estimation accounts for the variation of the ISB series.

Ambiguity resolution (AR) is critical for achieving a fast, high-precision solution in precise point positioning (PPP). In the standard uncombined PPP (S-UPPP) method, ionosphere-free code biases are superimposed by ambiguity and receiver clock offsets to be estimated. However, besides the time-constant part of the receiver code bias, the complex and time-varying term in receivers destroy the stability of ambiguities and degrade the performance of the UPPP AR. The variation of receiver code bias can be confirmed by the analysis in terms of ionospheric observables, code multipath (MP) of the Melbourne−Wübbena (MW) combination and the ionosphere-free combination. Therefore, the effect of receiver code biases should be rigorously mitigated. We introduce a modified UPPP (M-UPPP) method to reduce the effects of receiver code biases in ambiguities and to decouple the correlation between receiver clock parameters, code biases, and ambiguities parameters. An extra receiver code bias is set to isolate the code biases from ambiguities. The more stable ambiguities without code biases are expected to achieve a higher success rate of ambiguity resolution and a shortened convergence time. The variations of the receiver code biases, which are the unmodeled errors in measurement residuals of the S-UPPP method, can be estimated in the M-UPPP method. The maximum variation of the code biases is up to 16 ns within two-hour data. In the M-UPPP method, the averaged epoch residuals for code and phase measurements recover their zero-mean features. For the ambiguity-fixed solutions in the M-UPPP method, the convergence times are 14 and 43 min with 17.7% and 69.2% improvements compared to that in the S-UPPP method which are 17 and 90 min under the 68% and 95% confidence levels.

Okmok, a ~10-km wide caldera that occupies most of the northeastern end of Umnak Island, is one of the most active volcanoes in the Aleutian arc. The most recent eruption at Okmok during July–August 2008 was by far its largest and most explosive since at least the early 19th century. We investigate post-eruptive magma supply and storage at the volcano during 2008–2014 by analyzing all available synthetic aperture radar (SAR) images of Okmok acquired during that time period using the multi-temporal InSAR technique. Data from the C-band Envisat and X-band TerraSAR-X satellites indicate that Okmok started inflating very soon after the end of 2008 eruption at a time-variable rate of 48–130 mm/y, consistent with GPS measurements. The “model-assisted” phase unwrapping method is applied to improve the phase unwrapping operation for long temporal baseline pairs. The InSAR time-series is used as input for deformation source modeling, which suggests magma accumulating at variable rates in a shallow storage zone at ~3.9 km below sea level beneath the summit caldera, consistent with previous studies. The modeled volume accumulation in the six years following the 2008 eruption is ~75% of the 1997 eruption volume and ~25% of the 2008 eruption volume.

The determination of slant total electron content (STEC) between satellites and receivers is the first step for establishing an ionospheric model. However, the leveling errors, caused by the smoothed ambiguity solutions in the carrier-to-code leveling (CCL) method, degrade the performance of ionosphere modeling and differential code bias (DCB) estimation. To reduce the leveling errors, an uncombined and undifferenced precise point positioning (PPP) method with ambiguity resolution (AR) was used to directly extract the STEC. Firstly, the ionospheric observables were estimated with CCL, PPP float-ambiguity solutions, and PPP fixed-ambiguity solutions, respectively, to analyze the short-term temporal variation of receiver DCB in zero or short baselines. Then, the global ionospheric map (GIM) was modeled using three types of ionospheric observables based on the single-layer model (SLM) assumption. Compared with the CCL method, the slight variations of receiver DCBs can be obviously distinguished using high precise ionospheric observables, with a 58.4% and 71.2% improvement of the standard deviation (STD) for PPP float-ambiguity and fixed-ambiguity solutions, respectively. For ionosphere modeling, the 24.7% and 27.9% improvements for posteriori residuals were achieved for PPP float-ambiguity and fixed-ambiguity solutions, compared to the CCL method. The corresponding improvement for residuals of the vertical total electron contents (VTECs) compared with the Center for Orbit Determination in Europe (CODE) final GIM products in global accuracy was 9.2% and 13.7% for PPP float-ambiguity and fixed-ambiguity solutions, respectively. The results show that the PPP fixed-ambiguity solution is the best one for the GIM product modeling and satellite DCBs estimation.

Although the Zhaotong−Ludian fault is a seismically active zone located in the boundary between the Sichuan−Yunnan block and the South China block, it has not experienced a large earthquake greater than Mw 7 since at least 1700. On 3 August, 2014, an Mw 6.1 earthquake (the Ludian earthquake) ruptured the Zhaotong active belt in Ludian County, Yunnan province, China. This earthquake was the largest earthquake recorded in the region since 2000, and it provides us with a unique opportunity to study the active tectonics in the region. The analysis of the aftershocks showed that two conjugate faults could have been involved in the event. We first used Global Positioning System (GPS) data and C-band RADARSAT-2 imagery to map the coseismic surface deformation. We then inverted the derived coseismic deformation for the slip distribution based on the constructed conjugate fault model. Finally, the coulomb failure stress due to the Ludian earthquake was estimated to investigate the potential seismic hazards in this region. Our investigations showed that the Ludian earthquake was mainly a bilateral rupture event. The major slip of the main shock was located at depths of 0−5 km, which is close but does not superpose with the aftershocks that are mostly located at depths of 5−20 km. Interestingly, the seismic moment released by the aftershocks (6.9 × 1018 N∙m) was greater than that of the main shock (2.6 × 1018 N∙m). This evidence suggests that the accumulated elastic strain at depths of 0−20 km could have been fully released by the Ludian earthquake and its subsequent aftershocks. Furthermore, our analysis of the coulomb failure stress changes due to the main shock showed that the aftershocks could be the result of dynamic triggering rather than static triggering.

High-precision, high-reliability, and high-density GPS crustal velocity are extremely important requirements for geodynamic analysis. The least-squares collocation algorithm (LSC) has unique advantages over crustal movement models to overcome observation errors in GPS data and the sparseness and poor geometric distribution in GPS observations. However, traditional LSC algorithms often encounter negative covariance statistics, and thus, calculating statistical Gaussian covariance function based on the selected distance interval leads to inaccurate estimation of the correlation between the random signals. An unreliable Gaussian statistical covariance function also leads to inconsistency in observation noise and signal variance. In this study, we present an improved LSC algorithm that takes into account the combination of distance scale factor and adaptive adjustment to overcome these problems. The rationality and practicability of the new algorithm was verified by using GPS observations. Results show that the new algorithm introduces the distance scale factor, which effectively weakens the influence of systematic errors by improving the function model. The new algorithm can better reflect the characteristics of GPS crustal movement, which can provide valuable basic data for use in the analysis of regional tectonic dynamics using GPS observations.

In order to provide better service for the Asia-Pacific region, the BeiDou navigation satellite system (BDS) is designed as a constellation containing medium earth orbit (MEO), geostationary earth orbit (GEO), and inclined geosynchronous orbit (IGSO). However, the multi-orbit configuration brings great challenges for orbit determination. When orbit maneuvering, the orbital elements of the maneuvered satellites from broadcast ephemeris are unusable for several hours, which makes it difficult to estimate the initial orbit in the process of precise orbit determination. In addition, the maneuvered force information is unknown, which brings systematic orbit integral errors. In order to avoid these errors, observation data are removed from the iterative adjustment. For the above reasons, the precise orbit products of maneuvered satellites are missing from IGS (international GNSS (Global Navigation Satellite System) service) and iGMAS (international GNSS monitoring and assessment system). This study proposes a method to determine the precise orbits of maneuvered satellites for BeiDou GEO and IGSO. The initial orbits of maneuvered satellites could be backward forecasted according to the precise orbit products. The systematic errors caused by unmodeled maneuvered force are absorbed by estimated pseudo-stochastic pulses. The proposed method for determining the precise orbits of maneuvered satellites is validated by analyzing data of stations from the Multi-GNSS Experiment (MGEX). The results show that the precise orbits of maneuvered satellites can be estimated correctly when orbit maneuvering, which could supplement the precise products from the analysis centers of IGS and iGMAS. It can significantly improve the integrality and continuity of the precise products and subsequently provide better precise products for users.

High-precision monitoring of landslides is essential for understanding their kinematics and reducing landslide induced damage. The spatiotemporal deformation variations of the Three Bears landslide in northern California have not been systematically monitored and interpreted. In this study, we applied advanced time-series InSAR analysis methods to characterize the kinematics of the landslide covering two periods (2007−2011 and 2015−2017) with multi-track synthetic aperture radar (SAR) images acquired from L-band ALOS PALSAR-1/2 satellites. Our results show that the Three Bears landslide has been moving consistently, with the Line of Sight (LOS) deformation rate exceeding 300 mm/yr from 2007 to 2011 and around 250 mm/yr from 2015 to 2017. The east−west and vertical deformation components were inverted by integrating ascending and descending ALOS PALSAR-2 interferograms during the 2015−2017 period, indicating that the landslide was dominated by eastward movement and in a continuous deformation stage. Down-slope landslide motions observed from adjacent satellite tracks with slightly different radar look vectors were used to verify the accuracy of InSAR-derived results. Comparison between linearly detrended InSAR displacements and precipitation records indicate that the landslide tends to accelerate during the wet seasons. The results could allow us to better understand the kinematics of the landslide and provide significant evidence for evaluating the potential for catastrophic failure and the threat posed by such failure to human life and property. Combined with a proper geotechnical/geomechanical model, the results would also facilitate the design and implementation of mitigation measures.

This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % .

The Xinmo landslide occurred on 24 June 2017 and caused huge casualties and property losses. As characteristics of spatiotemporal pre-collapse deformation are a prerequisite for further understanding the collapse mechanism, in this study we applied the interferometric synthetic aperture radar (InSAR) technique to recover the pre-collapse deformation, which was further modeled to reveal the mechanism of the Xinmo landslide. Archived SAR data, including 44 Sentinel-1 A/B data and 20 Envisat/ASAR data, were used to acquire the pre-collapse deformation of the Xinmo landslide. Our results indicated that the deformation of the source area occurred as early as 10 years before the landslide collapsed. The deformation rate of source area accelerated about a month before the collapse, and the deformation rate in the week before the collapse reached 40 times the average before the acceleration. Furthermore, the pre-collapse deformation was modeled with a distributed set of rectangular dislocation sources. The characteristics of the pre-collapse movement of the slip surface were acquired, which further confirmed that a locked section formed at the bottom of the slope. In addition, the spatial-temporal characteristics of the deformation was found to have changed significantly with the development of the landslide. We suggested that this phenomenon indicated the expansion of the slip surface and cracks of the landslide. Due to the expansion of the slip surface, the locked section became a key area that held the stability of the slope. The locked section sheared at the last stage of the development, which triggered the final run-out. Our study has provided new insights into the mechanism of the Xinmo landslide.

Due to the cuboid satellite body of BeiDou-3 satellites, the accuracy of their orbit showed a trend of systematic variation with the sun-satellite-earth angle (ε) using the Extend CODE Orbit Model (ECOM1). Therefore, an a priori cuboid box-wing model (named the cuboid model) is necessary to compensate ECOM1. Considering that the body-dimensions and optical properties of the BeiDou-3 satellites used to construct the box-wing model have not yet been fully released, the adjustable box-wing model (ABW) was used for precise orbit determination (POD). The a priori cuboid box-wing model was directly estimated by the precision radiation accelerations, obtained from ABW POD. When using ECOM1 model, for 14 < β < 40°, a linear systematic variation of D0 related to the elevation of the sun above the orbital plane (β-angle) with a slope of 0.048 nm/s2/°, was found for C30. After adding the cuboid model to assist ECOM1 (named Cuboid + ECOM1), the slope was reduced to 0.005 nm/s2/°, and for C20 satellite, the standard deviation (STD) of D0 was improved, from 1.28 to 0.85 nm/s2 (34%). For satellite laser ranging (SLR) validation, when using the ECOM1 model, the systematic variation with the ε angle was about 14 cm for C20 and C30. After using the Cuboid + ECOM1 model, the variation was significantly reduced to about 5 cm. For C20 and C21, compared with the ECOM1 model, the root mean square (RMS) of the ECOM2 and Cuboid + ECOM1 model was improved by about 0.54 (10.3%) and 0.43 cm (8.7%). For C29 and C30, the RMS of ECOM2 and Cuboid + ECOM1 model was improved for about 0.7 (10.9%) and 1.6 cm (25.6%). Finally, the RMS of the SLR residuals of 4.37 to 4.88 cm was achieved for BeiDou-3 POD.

Artificial lands or islands reclaimed from the sea due to their vast land spaces and air are suitable for the construction of airports, harbors, and industrial parks, which are convenient for human and cargo transportation. However, the settlement process of reclamation foundation is a problem of public concern, including soil consolidation and water recharge. Xiamen New Airport, one of the largest international airports in China, has been under construction on marine reclamation land for three years. At present, the airport has reached the second phase of construction, occupying 15.33 km2. The project will last about twenty years. To investigate the temporal and spatial evolution of ground settlement associated with land reclamation, Sentinel-1 synthetic aperture radar (SAR) data, including intensity images and phase measurements, were considered. A total of 82 SAR images acquired by C-band Sentinel-1 satellite covering the time period from August 2015 to October 2018 were collected. First, the spatial evolution process of land reclamation was analyzed by exploring the time series of SAR image intensity maps. Then, the small baseline subset InSAR (SBAS–InSAR) technique was used to retrieve ground deformation information over the past three years for the first time since land reclamation. Results suggest that the reclaimed land experienced remarkable subsidence, especially after the second phase of land reclamation. Furthermore, 26 ground settlement areas (i.e., 0.015% of the whole area) associated with land reclamation were uncovered over an area of more than 1200 km2 of the Xiamen coastal area from January 2017 to October 2018. This study offers important guidance for the next phase of land reclamation and the future construction of Xiamen New Airport.

The ancient city of Xi’an, China, has been suffering severe land subsidence and ground fissure hazards since the 1960s, mainly due to the over-withdrawal of groundwater and large-scale urban construction. This has threatened and will continue to threaten the stability of urban infrastructure, such as the construction and operation of high buildings and subway lines. It is necessary to map the spatiotemporal variations of land subsidence over Xi’an, and to analyze their causes and the correlation with underground water level changes and ground fissure deformation. Time series of land subsidence were observed with the interferometric synthetic aperture radar (InSAR) technique, using multi-sensor SAR datasets from 2012 to 2018. Four land subsidence rate maps over Xi’an city were retrieved from TerraSAR-X, ALOS/PALSAR2, and Sentinel-1 data, each with different tracks. The InSAR derived results were then cross-validated with three independent SAR data stacks, and calibrated with GPS and leveling observations. Next, the spatiotemporal evolutions of three main regional land subsidence zones were quantitatively analyzed in detail, and the surface deformation of the Xi’an subway network was spatially analyzed. Third, the correlations between land subsidence and ground water withdrawal, ground fissure deformation, landforms, and faults were intensively analyzed. Finally, a flat lying sill model with distributed contractions was implemented to model the InSAR deformation over one typical subsidence zone, which further suggested that the ground deformation was mainly caused by groundwater withdrawal. This systematic research can provide sound evidence to serve decision-making for land subsidence mitigation in Xi’an, and may also guide land subsidence research in other cities.

On 9 July 2009, an Ms6.0 earthquake occurred in mountainous area of Yao’an in Yunnan province of Southern China. Although the magnitude of the earthquake was moderate, it attracted the attention of many Earth scientists because of its threat to the safety of the population and its harm to the local economy. However, the source parameters remain poorly understood due to the sparse distribution of seismic and GNSS (Global Navigation Satellite System) stations in this mountainous region. Therefore, in this study, the two L-band ALOS (Advanced Land Observing Satellite-1) PALSAR (Phased Array type L-band Synthetic Aperture Radar) images from an ascending track is used to investigate the coseismic deformation field, and further determine the location, fault geometry and slip distribution of the earthquake. The results show that the Yao’an earthquake was a strike-slip event with a down-dip slip component. The slip mainly occurred at depths of 3⁻8 km, with a maximum slip of approximately 70 cm at a depth of 6 km, which is shallower than the reported focal depth of ~10 km. An analysis of the seismic activity and tectonics of the Yao’an area reveals that the 9 July 2009 Yao’an earthquake was the result of regional stress accumulation, which eventually led to the rupture of the northwestern most part of the Maweijing fault.

The emergence of multiple global navigation satellite systems (multi-GNSS), including global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), and Galileo, brings not only great opportunities for real-time precise point positioning (PPP), but also challenges in quality control because of inevitable data anomalies. This research aims at achieving the real-time quality control of the multi-GNSS combined PPP using additional observations with opposite weight. A robust multiple-system combined PPP estimation is developed to simultaneously process observations from all the four GNSS systems as well as single, dual, or triple systems. The experiment indicates that the proposed quality control can effectively eliminate the influence of outliers on the single GPS and the multiple-system combined PPP. The analysis on the positioning accuracy and the convergence time of the proposed robust PPP is conducted based on one week’s data from 32 globally distributed stations. The positioning root mean square (RMS) error of the quad-system combined PPP is 1.2 cm, 1.0 cm, and 3.0 cm in the east, north, and upward components, respectively, with the improvements of 62.5%, 63.0%, and 55.2% compared to those of single GPS. The average convergence time of the quad-system combined PPP in the horizontal and vertical components is 12.8 min and 12.2 min, respectively, while it is 26.5 min and 23.7 min when only using single-GPS PPP. The positioning performance of the GPS, GLONASS, and BDS (GRC) combination and the GPS, GLONASS, and Galileo (GRE) combination is comparable to the GPS, GLONASS, BDS and Galileo (GRCE) combination and it is better than that of the GPS, BDS, and Galileo (GCE) combination. Compared to GPS, the improvements of the positioning accuracy of the GPS and GLONASS (GR) combination, the GPS and Galileo (GE) combination, the GPS and BDS (GC) combination in the east component are 53.1%, 43.8%, and 40.6%, respectively, while they are 55.6%, 48.1%, and 40.7% in the north component, and 47.8%, 40.3%, and 34.3% in the upward component.

The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

As a result of rapid societal development and urbanization, the pumping of groundwater has gradually increased. Land subsidence has thus become a common geological disaster, which can result in huge economic losses. Interferometric synthetic aperture radar (InSAR), with its large-scale and high-accuracy monitoring characteristics, can attain information on Earth surface deformation using the interferometric phase between couples of SAR images acquired at different times. Time-series results for the ground surface are the key information required to understand the deformation pattern and further study the reason for the subsidence. However, in recent research, most methods for resolving time-series deformation—like the Berardino method—that use residuals in functional model solving and distinguish high-pass displacement and the atmospheric component by filtering do not generally work well and functional models focusing on prior information in the time-series solution process are not always available. In this paper, to solve the above problems, 34 Sentinel-1A descending mode scenes of Mexico City captured between 2015/04/13 and 2016/09/10 are used as experimental data. Firstly, a new functional model is provided to obtain the deformation time-series. The nonlinear deformation and atmospheric phase are combined as an unknown parameter and the method of singular value decomposition (SVD) is used to solve this variable. The nonlinear displacement and atmospheric phase are then separated by the singular spectrum analysis (SSA) method. Finally, the total land subsidence time-series is obtained by adding together the linear displacement and nonlinear displacement. Two typical methods and the proposed method were compared using both unit weights and adaptive weights. The experimental results show that the proposed method can obtain a more accurate time-series deformation result. Moreover, the different weights do not result in significant differences and the solved atmospheric and nonlinear phases have good consistency with the interferogram phase.

Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

On the afternoon of 28 June 2010, an enormous landslide occurred in the Gangwu region of Guanling County, Guizhou Province. In order to better understand the mechanism of the Guanling landslide, archived ALOS/PALSAR data was used to acquire the deformation prior to the landslide occurrence through stacking and time-series InSAR techniques. First, the deformation structure from InSAR was compared to the potential creep bodies identified using the optical remote sensing data. A strong consistency between the InSAR detected deformed regions and the creep bodies detected from optical remote sensing images was achieved. Around 10 creep bodies were suffering from deformation. In the source area, the maximum pre-slide mean deformation rate along the slope direction reached 160 mm/year, and the uncertainty of the deformation rates ranged from 15 to 34 mm/year. Then, the pre-slide deformation at the source area was analyzed in terms of the topography, geological structure, and historical rainfall records. Through observation and analysis, the deformation pattern of one creep body located within the source area can be segmented into three sections: a creeping section in the front, a locking section in the middle, and a cracking section in the rear. These sections constitute one of the common landslide modes seen in the south-west of China. This study concluded that a sudden shear failure in the locking segment of one creeping body located within the source area was caused by a strong rainstorm, which triggered the Guanling landslide.

The Linfen–Yuncheng basin is an area prone to geological disasters, such as surface subsidence, ground fissuring, fault activity, and earthquakes. For the purpose of disaster prevention and mitigation, Interferometric Synthetic Aperture Radar (InSAR) was used to map ground deformation in this area. After the ground deformation characteristics over the Linfen–Yuncheng basin were obtained, the cross-correlations among regional ground subsidence, fault activity, and underground water level were analyzed in detail. Additionally, an area of abnormal deformation was found and examined. Through time series deformation monitoring and mechanism inversion, we found that the abnormal deformation was related mainly to excessive groundwater exploitation.