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The frequent occurrence of extreme climate events has a significant impact on people's lives. Heavy rainfall can lead to an increase of regional Terrestrial Water Storage (TWS), which will cause land subsidence due to the influence of hydrological load. At present, regional TWS is mostly obtained from Gravity Recovery and Climate Experiment (GRACE) data, but the method has limitations for small areas. This paper used water level and flow data as hydrological signals to study the land subsidence caused by heavy rainfall in the Chaohu Lake area of East China (June 2016–August 2016). Pearson's correlation coefficient was used to study the interconnection between water resource changes and Global Navigation Satellites System (GNSS) vertical displacement. Meanwhile, to address the reliability of the research results, combined with the Coefficient of determination method, the research findings were validated by using different institutional models. The results showed that: (1) During heavy rainfall, the vertical displacement caused by atmospheric load was larger than non-tidal oceanic load, and the influence trends of the two were opposite. (2) The rapidly increasing hydrologic load in the Chaohu Lake area resulted in greater subsidence displacement at the closer CORS station (CHCH station) than the more distant CORS station (LALA station). The Pearson correlation coefficients between the vertical displacement and water level were as high as −0.80 and −0.64, respectively. The phenomenon confirmed the elastic deformation principle of disc load. (3) Although there was a systematic bias between the different environmental load deformation models, the deformation trends were generally consistent with the GNSS monitoring results. The average Coefficients of determination between the different models and the GNSS results were 0.63 and 0.77, respectively. The results demonstrated the effectiveness of GNSS in monitoring short-term hydrological load. This study reveals the spatial-temporal evolution of land deformation during heavy rainfall around Chaohu Lake, which is of reference significance for water resource management and infrastructure maintenance in this area. [ABSTRACT FROM AUTHOR]

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. [ABSTRACT FROM AUTHOR]

Underground coal-mining-induced ground subsidence deformation is a common geological disaster impacting buildings, transportation and water supplies. Models predicting ground subsidence dynamically with high precision are important for the prevention of damage derived from ground subsidence. In this paper, the Hook function is utilized to develop a model describing the velocity of ground subsidence due to underground coal mining. Based on the subsidence velocity model, a dynamic subsidence model is established by taking an integral of the velocity model. Coefficients of the model, which depend on maximum subsidence, maximum subsidence velocity and the time corresponding to the maximum subsidence velocity, are related to the geological and mining conditions of the coal seam being investigated. A Levenberg–Marquardt-algorithm-based method is also proposed to calculate the optimal model coefficients based on subsidence velocity observations. Four continuously operating Global Navigation Satellite System (GNSS) stations were constructed above a typical longwall coal mining working face in the Jining mining area, China. These GNSS stations collected subsidence observations over two years, which were used to validate the developed prediction model. The results show that the root-mean-square (RMS) of the model-predicted ground subsidence error is 56.1 mm, and the maximum relative error is 2.5% for all four GNSS stations, when the ground subsidence is less than 6000 mm. [ABSTRACT FROM AUTHOR]

As a major grain-producing region in China, the North China Plain (NCP) faces serious challenges such as water shortage and land subsidence. In late 2014, the Central Route of the South-to-North Water Diversion Project (SNWD-C) began to provide NCP with water resources. However, the effectiveness of this supply in mitigating land subsidence remains a pivotal and yet unassessed aspect. In this paper, we utilized various geodetic datasets, including the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO), Global Navigation Satellite System (GNSS) and leveling data, to conduct a spatial-temporal analysis of the equivalent water height (EWH) and vertical ground movement in the NCP. The results reveal a noteworthy decline in EWH from 2011 to 2015, followed by a slight increase with minor fluctuations from 2015 to 2020, demonstrating a strong correlation with the water resources supplied by the SNWD-C. The GRACE-derived surface deformation rate induced by hydrological loading is estimated to be <1 mm/yr. In comparison, GNSS-derived vertical ground movements exhibit considerable regional differences during the 2011–2020 period. Substantial surface subsidence is evident in the central and eastern NCP, contrasting with a gradual uplift in the front plain of the Taihang Mountains. Three-stage leveling results indicate that the rate of subsidence in the central and eastern plains is gradually increasing with the depression area expanding from 1960 to 2010. Based on these geodetic results, it can be inferred that the SNWD-C's operation since 2014 has effectively mitigated the reduction in terrestrial water storage in the NCP. However, land subsidence in the NCP persists, as the subsidence rate does not turn around in sync with the change in EWH following the operation of SNWD-C. Consequently, it's necessary to maintain and enforce existing policies, including controlling groundwater exploitation and water resources supply (e.g., SNWD-C) to curtail the exacerbation of land subsidence in the NCP. Additionally, continuous monitoring of land subsidence by GRACE, GNSS, leveling and other geodetic techniques is crucial to enable timely policy adjustments based on monitoring results. [ABSTRACT FROM AUTHOR]

This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model's performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models. [ABSTRACT FROM AUTHOR]

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model. [ABSTRACT FROM AUTHOR]

In 2020, the BeiDou-3 global navigation satellite system (BDS-3) was officially completed and put into service. Currently, network real-time kinematic (RTK) technology is considered the main means through which to improve the positioning accuracy of the BeiDou navigation satellite system (BDS). This paper proposes a long-range undifferenced network RTK (URTK) algorithm, based on multi-frequency observation data of the BDS. First, the multi-frequency phase integer ambiguity resolution (AR) model considering atmospheric error parameters is designed, and the multi-frequency phase integer ambiguity of the long-range BDS reference station is determined. Then, the undifferenced integer ambiguity of each reference station is obtained, using linear variation based on the accurately determined phase integer ambiguity between reference stations, and the undifferenced observation error of each reference station is calculated. Considering the weakening spatial correlation of the observation errors between long-range stations, undifferenced classification error corrections of a reference station network are separated, according to different error characteristics. Finally, the inverse distance weighting method is employed to calculate the classification undifferenced error correction of the rover station. The rover station corrects the observation error through applying the undifferenced error correction to achieve high-precision positioning. The measured data of a long-range continuous operation reference station (CORS) network are selected for an experiment. The results show that the proposed algorithm can quickly and accurately realize the resolution of the BDS integer ambiguity of a reference station network and establish an undifferenced area error correction model in order to achieve accurate classification of undifferenced error correction values for a rover station. In China, the BDS-3 is superior to the global positioning system (GPS) in terms of the satellite number, position dilution of precision (PDOP) value, AR success rate, stability, and convergence time. The results show that the AR success rate, stability, and convergence time increase with the operational frequency, and the BDS-3 can achieve centimeter-level positioning of single-system rover stations without relying on the GPS. [ABSTRACT FROM AUTHOR]

High precision ionospheric Total Electron Content(TEC) prediction is of great significance for improving the accuracy of Global Navigation Satellite System(GNSS), preventing natural disasters, and ensuring wireless communication. Given the varying frequencies of TEC signals, a hybrid CEEMDAN-BiLSTM-PSO-LSSVM-FE model for predicting ionospheric TEC content is proposed in this paper. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the extracted hourly TEC sequence and calculate the fuzzy entropy (FE) of the subsequences. Then, the signal is divided into high-frequency and low-frequency parts based on the fuzzy entropy value, for the high-frequency component, Bidirectional Long Short-Term Memory network (BiLSTM) is used for prediction; for the low-frequency component, Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) is used for prediction. The hourly TEC values collected from six evenly distributed GPS stations in China are used as the main input variable for the proposed model, with solar and geomagnetic activity data used as auxiliary data, using the TEC data from the previous 48 h to forecast the TEC content for the next hour. The performance of the model is evaluated by comparing it with other models under different geographical locations, seasons, solar and geomagnetic activity conditions. Experimental results show that the proposed hybrid model outperforms other models, with a correlation coefficient R2 and root mean square error (RMSE) of 0.99 and 0.20 TECU, respectively. The model effectively overcomes the problem of low TEC prediction accuracy and can provide more precise ionospheric delay correction services. [ABSTRACT FROM AUTHOR]

Block Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints. [ABSTRACT FROM AUTHOR]

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models. [ABSTRACT FROM AUTHOR]

Atmospheric pressure loading (APL) deformation is one component of nontectonic deformation for Global Navigation Satellite System (GNSS) time series and is a kind of deformation response caused by a redistribution of atmospheric pressure. In this paper, we present an atmospheric data processing strategy to compute the APL based on a spherical harmonic expansion of the global atmosphere pressure changes. We also provide a sample model to describe the relativity between the global atmosphere pressure changes and APL vertical deformation. The results show that the variation of air mass has a major impact on the north-eastern area of East China, the eastern area of North China, and Northeast China, and the vertical crustal displacement caused by the atmosphere changes in these regions can reach about 20 mm. The correction of APL for vertical time series of GNSS reference stations in different regions indicates that the arid area of the Northwest China, Northeast China, Central China, and North China are greatly affected by APL. While for the station located in Sichuan-Yunnan region, the amplitude and period change are small after correction of APL for vertical time series of GNSS reference stations, which reveals that the area is seriously affected by tectonic movement and water migration loading. The correlation between atmospheric pressure changes and crustal deformation is analyzed, which shows that APL has a serious impact on the north-eastern area of North China, the Northeast China, and the eastern area of Central China when the variations in atmospheric pressure in mainland China are the same. The research results of this paper will provide some reference value for the study of crustal structural deformation and the establishment of geodetic datum. [ABSTRACT FROM AUTHOR]

Many studies have investigated the spatial variations of the ionosphere, but the quantitative characteristics of the ionosphere are rarely reported. In this paper, we utilize the total electron content (TEC) data to evaluate the latitudinal gradient of the ionosphere within 10°-50° N over the China sector. It is found that the magnitudes of latitudinal gradient are significantly higher within 10°-40° N and 45°-50° N, respectively. The database of TEC from 1 November 2018 to 31 October 2022 is processed to figure out the local time, seasonal, and solar activity dependency of the latitudinal gradient. The results suggest that the gradient within 10°-40° N is higher in the daytime and during high solar activity period. They are more noticeable in the spring and autumn, and least visible in the summer. Conversely, the gradient within 45°-50° N strengthens in the nighttime and under lower solar activity, and has larger values in the summer months. Furthermore, the International Reference Ionosphere (IRI-2020) model is assessed in terms of the reproducibility of latitudinal gradient. The IRI-2020 basically represents the latitudinal gradient within 10°-40° N, whereas it overestimates the gradient in the low solar activity period and misses the gradient features near 45°–50° N. [ABSTRACT FROM AUTHOR]

This paper investigates the ionospheric responses to the February and April 2023 geomagnetic storms using Total Electron Content (TEC) derived from the 10 Global Navigation Satellite System (GNSS) stations at the transition of low to mid-latitudes (Approx. 20 ∼ 30°N) across different longitudes in the American and Asian sectors. Significant variations in both sectors were recorded during the recovery phase and were mainly attributed to the Prompt Penetration Electric Field (PPEF), Disturbance Dynamo Electric Field (DDEF) and the thermospheric neutral composition changes as O/N 2 depletion. Similarly, the American and Asian sectors showed TEC enhancements, primarily attributed to the PPEF, during the main phase of the April storm. The negative TEC variations during the recovery phase were observed due to the DDEF and changes in O/N 2. In the Asian sector, positive and negative variations were recorded over Pakistan and China, respectively, during the main phase of the April storm. These variations resulted in high and low concentrations of the O/N 2 ratio due to Magnetosphere-Ionosphere-Thermosphere coupling. [ABSTRACT FROM AUTHOR]

In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m. [ABSTRACT FROM AUTHOR]

The Daliang Shan is a tectonic unit that connects the active southeastern Tibetan Plateau with the stable South China block. As a newly generated (formed later than the Xianshuihe–Xiaojiang active fault system) seismotectonic zone induced by the Tibetan tectonics, the detailed constraints of the crustal deformation are central to the understanding of the kinematics and dynamics of the Tibetan expansion. This paper establishes and analyses a high-spatial resolution global positioning system (GPS) velocity field from a dense GPS network in this region. Our modelling results indicate that, in contrast to the equivalent sinistral strike-slip rate of approximately 5 mm yr–1 on the Anninghe–Zemuhe and Daliangshan faults, their inferred interseismic locking depth varies within a large range. The southern segment of the Anninghe Fault and the middle segment of the Daliangshan Fault have deep locking depths of ∼13 km, indicating that the seismic risk is high in these areas. In addition, the detectable counter-clockwise rotation rate of 0.35 ± 0.12° Myr–1 of the Mabian block makes a significant contribution of ∼50 per cent to the strike-slip motion on its boundary faults. This counter-clockwise rotation may be induced by a left-lateral shear gradient with southeastward motion relative to the South China-fixed reference frame, indicating the significance of a simple-shear pattern in exploring the kinematics of the encroachment of the Tibetan tectonics upon a stable block (craton). [ABSTRACT FROM AUTHOR]

As the Global Navigation Satellite System (GNSS) is widely used in all walks of life, the signal structure of satellite navigation is open, and the vulnerability to spoofing attacks is also becoming increasingly prominent, which will seriously affect the credibility of navigation, positioning, and timing (PNT) services. Satellite navigation signal authentication technology is an emerging technical means of improving civil signal anti-spoofing on the satellite navigation system side, and it is also an important development direction and research focus of the GNSS. China plans to carry out the design and development of the next-generation Beidou navigation satellite system (BDS), and one of its core goals is to provide more secure and credible PNT services. This paper first expounds on the principles and technical architecture of satellite navigation signal authentication, then clarifies the development history of satellite navigation signal authentication, and finally proposes the BDS authentication service system architecture. It will provide technical support for the construction and development of the follow-up Beidou authentication service. [ABSTRACT FROM AUTHOR]

Precise Point Positioning (PPP) is an official service of the BeiDou Global Navigation Satellite System (BDS-3) through the PPP-B2b signal. In this paper, we mainly focus on the long-term performance evaluation of BDS-3 PPP-B2b products and their application in time service. Since the PPP-B2b product is only available in and around China area, the arcs of PPP-B2b products are about several hours. We propose to evaluate the time datum stability by using all available satellites. Then, 557 day PPP-B2b products are collected for this experiment. The results show that there are large jumps in the GPS satellite clock time datum series. However, the BDS-3 satellite clock datum stability is almost at the same level with current Space State Representation (SSR) corrections from the International Global navigation satellite system Service (IGS). The difference between PPP-B2b GPS and BDS-3 satellite clock time datum will be absorbed into the Inter System Bias (ISB) parameter. Thus, it should be specially noted that the ISB parameter cannot be estimated as constant values if users use PPP-B2b products. In addition, the accuracy of the BDS-3 satellite clock is significantly better than that of the GPS for both the Root Mean Square Error (RMSE) and standard deviation (STD). The average Signal in Space Range Errors (SISREs) is 0.22 ns and 0.13 ns for GPS and BDS-3, respectively. The one-way timing experiment shows BDS-3 timing stability is 2.9 × 10−14@104 s. In addition, 10 baselines from 13 km to 4494 km are formed for time synchronization evaluation by using PPP-B2b products. The average RMSEs of time synchronization is from 0.46 ns to 1.58 ns and from 0.66 ns to 1.19 ns for GPS and BDS-3, respectively. As for STD, the average values are from 0.27 ns to 0.74 ns and from 0.27 ns to 0.47 ns for GPS and BDS-3, respectively. Overall, the results show that the time datum stability, accuracy, and service performance of BDS-3 PPP-B2b products has been stable over the past two years. [ABSTRACT FROM AUTHOR]

Landslides along the Three Gorges Reservoir in China pose a threat to coastal residents and waterway safety. To reduce false positive misjudgments caused by a sudden local change in the landslide deformation curve, in this paper, we propose an effective method for predicting the deformation trend of reservoir bank landslides. We take reservoir bank landslides in the Wanzhou District of the Three Gorges Reservoir area as the research object. The Time-Series Interferometric Synthetic Aperture Radar (InSAR) method and 62 Sentinel-1A images from 2018 to 2022 were selected for landslide deformation monitoring, and the Hurst index was calculated to characterize the deformation trend. Furthermore, we propose a method for predicting the deformation trend based on the statistical distribution of deformation rates and the physical significance of the Hurst index. After the field survey and Global Positioning System (GPS) verification, the Time-Series InSAR results are shown to be reliable. We take the Sifangbei landslide as a representative case to analyze the validation results. It is found that the determined Sifangbei landslide deformation trend is consistent with the conclusions for the region. In addition, the deformation trend of a reservoir bank slope has obvious spatial and temporal differences. Changes in the reservoir water level and concentrated rainfall play roles similar to those of catalysts. The proposed method, involving the combination of Time-Series InSAR and the Hurst index, can effectively monitor deformation and predict the stability trend of reservoir bank landslides. The presented research results provide new ideas and solutions for landslide prevention and risk mitigation in reservoir areas. [ABSTRACT FROM AUTHOR]

China developed its Digital Television (DTV) standard in 2006, known as Digital Television Terrestrial Multimedia Broadcasting (DTMB), which employs time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) as the modulation method. In contrast to Global Navigation Satellite Systems (GNSSs), DTV signals have higher transmitting power, wider coverage, larger bandwidth, and fixed transmitter location. This paper explores the positioning performance of DTMB signals, and the potential to improve GNSS positioning accuracy in urban environments. Specifically, a solution is proposed, and a software-defined radio receiver is developed for wireless localization. Without changing the current signal structure, the pseudorandom noise (PN) sequences in the signal are used for signal acquisition and carrier phase ranging. The carrier phase of the first arrived path is extracted by the least squares matching pursuit method. Both static and dynamic field tests were conducted to verify the proposed ranging and positioning method. Centimeter-level ranging accuracy was achieved in the static scenario, while meter-level ranging accuracy was achieved in the dynamic scenario. As one possible application, the proposed ranging method was combined with GPS pseudorange measurements to achieve higher accuracy position results in an urban pedestrian scenario, especially when there is only a limited number of visible satellites. [ABSTRACT FROM AUTHOR]

Ionospheric irregularities have been studied since ~ 70 years ago. With the development of Global Navigation Satellite system (GNSS), networks of GNSS receivers have been used to obtain the characteristics of the irregularities, including the drift velocity, the structure, and the evolution. In this paper, keograms based on the Crustal Movement Observation Network of China (CMONOC) were used to characterize the irregularities over the area from longitude 85 to 125 °E and latitude 11 to 35 °N in 2014. Keograms were obtained for the rate of TEC index (ROTI) for every 0.5 degree longitude and 30 min universal time pixel. The results showed that the occurrence rate of irregularities in 2014 was high in the equinox months and December, and lowest in June. In equinox months the irregularities often appeared after sunset. In March the irregularities usually had long lifetime of ~ 5–7 h and ~ 5 degrees apparent longitudinal width. The long lifetime usually was accompanied by obvious eastward drift of ~ 100 m/s and large vertical ROTI (vROTI). In September the irregularities had weaker ROTI and shorter lifetime than those in March. The irregularities in the 2 equinox months should be related to the equatorial plasma bubbles (EPBs). In June, they appeared ~ 2–3 h later than those in equinoxes and drifted westward. The summer irregularities had weakest ROTI and their latitude was ~ 30 °N, much higher than those in equinoxes. In December, the irregularities were discrete patches with a longitudinal width of ~ 2 degrees and short lifetime of ~ 2 h. Unlike the equatorial irregularities in equinox months which are part of equatorial plasma bubbles, the solstice irregularities mainly appear to be a local phenomenon. [ABSTRACT FROM AUTHOR]

On 21 May 2021 (UT), Yangbi Ms6.4 and Maduo Ms7.4 earthquakes occurred in mainland China. This paper analyzed the ionospheric perturbations possibly related to the earthquake, based on global positioning system (GPS) total electron content (TEC) and global ionosphere map (GIM) TEC data. We identified GPS TEC anomalies by the sliding quartile, based on statistical analysis. After eliminating the days with high solar activity levels and strong geomagnetic disturbances, the time series analysis of GPS TEC data showed that there were significant TEC anomalies from 5 to 10 May. TEC anomalies were mainly positive anomalies. We obtained the spatial and temporal distributions of TEC anomalies using natural neighbor interpolation (NNI). The results showed that the TEC anomalies were distributed in the seismogenic zone and surrounded the epicenters of the Maduo and Yangbi earthquakes, indicating that they may be related to the earthquakes. From the GIM TEC difference map, we found the TEC enhancement in the seismogenic zone and its magnetic conjugate area of the Maduo and Yangbi earthquakes at 10:00–12:00 (UT) on the 5 and 6 May. We discussed our results according to the lithosphere-atmosphere-ionosphere coupling mechanism. Finally, based our results, we suggested that the Yangbi and Maduo earthquakes may affect the ionosphere through seismogenic electric field and thermal anomalies generated during the process of lithosphere-atmosphere-ionosphere coupling. [ABSTRACT FROM AUTHOR]

With the rapid development of interferometric synthetic aperture radar (InSAR) measurement technology, its measurement accuracy requirements are increasing. Atmospheric delay errors must be corrected, especially in the case of crustal deformation monitoring, the 20% variation of tropospheric water vapor among InSAR pairs generally produces range from 10 cm to 14 cm deformation errors. Such errors can be of the same magnitude as the annual changes in crustal deformation, or even greater, masking crustal deformation information and seriously affecting the results of crustal deformation monitoring. Therefore, in order to obtain a more accurate InSAR atmospheric delay correction model, this paper calculated and integrated atmospheric delays that were estimated by different sources, including the 37 pressure levels of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF)) numerical weather prediction model, ECMWF Reanalysis v5 (ERA5), and Global Navigation Satellite System (GNSS) measurement data from the crustal movement observation network of China, based on the variance component estimation (VCE) weighting method. The results showed that the integrated model, based on the VCE method, is better than the generic atmospheric correction online service (GACOS) model for InSAR measuring of crustal deformation. The precision in monitoring crustal deformations was improved by approximately 5 mm, the correlation coefficient of atmospheric delay errors and crustal deformations improved from 0.287 to 0.347, and accuracy improved by approximately 25%. However, the improvement in accuracy was limited because of system error decoherence that was induced by atmospheric noise caused by abundant vegetation or snow cover. Therefore, in order to achieve more accurate results, we recommend the adoption of the multi-source integrated atmospheric delay correction model, based on the VCE method, for InSAR high-precision measuring of crustal deformation and seismic activities. [ABSTRACT FROM AUTHOR]

Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China. [ABSTRACT FROM AUTHOR]

Continuous Global Positioning Systems (GPS) coordinate time series with a high spatiotemporal resolution, and provide a great opportunity to study their noise models and common mode errors (CMEs), thus making it possible to detect and analyse spatiotemporal characteristics of tectonic and non-tectonic signals in time series, and further to estimate a reliable and accurate velocity field of crustal movement in a region by removing CMEs and using the optimal noise model. In this paper, we used GPS coordinate time series from the Crustal Movement Observation Network of China (CMONOC) with an approximate decadal period from 2010 to 2020, to construct optimal noise models by fitting them with several noise combinations according to the Akaike information criterion (AIC). We further adopted independent component analysis (ICA) to extract CMEs and analysed their spatiotemporal characteristics, and then evaluated their effects on noise models and velocity uncertainties, and finally estimated a decennial velocity field of crustal movement with a higher signal-to-noise ratio (SNR) by applying the CME filtering and considering the optimal noise model in Mainland China. Our results show that optimal noise models are dominated by white noise (WN) plus flicker noise (FN) for both east and north components, and WN plus power law noise (PN) with spectral index close to −1 for up component, respectively. ICA filtering can remove the highly spatially correlated CMEs and decrease the mean RMSEs of the residual time series by about 40–60%, providing a more accurate velocity field with a higher SNR in Mainland China, accordingly. [ABSTRACT FROM AUTHOR]

Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations. [ABSTRACT FROM AUTHOR]

The information unavailability of a global navigation satellite system (GNSS) for geo‐localization in many military and tourism applications implies the need to employ other applicable types of information, such as automatic image‐based location recognition. Unlike most existing localization methods that use GNSS information to locate an initial position, this paper proposes an automatic geo‐localization framework that does not require GNSS data. The proposed framework is a two‐stage pipeline that uses a query image and digital elevation model (DEM) data as input. The authors frame automatic geo‐localization without GNSS recordings as a skyline matching problem. By extracting the skyline from the DEM data and query images, the query image can be localized by matching the query skyline feature to the DEM skyline database. It has been demonstrated that this low‐cost approach can perform efficiently in mountainous or hilly areas to produce reliable localization results. The system was tested on 50 testing site points within a large‐scale area (China, 202.6 km2), and an average position error of 43.13 m was detected within 4.5 s. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies. [ABSTRACT FROM AUTHOR]

In terms of cataloged debris produced, the anti-satellite test carried out by Russia, in November 2021, at an altitude of about 480 km, leading to the destruction of the old satellite Cosmos 1408, was the second worst to date and represented the third worst fragmentation in orbit. It generated more than 1/4 of the cataloged debris produced over 55 years by all such tests and almost twice as many as were produced by all previous Soviet tests. After placing this event in its historical context, this paper analyzes in detail how the evolution of the Cosmos 1408 debris cloud affected the environment below 600 km in the first seven months, focusing on the two operational space stations and the Starlink large constellation of satellites. During the first six months following the test, the Cosmos 1408 cloud of fragments nearly doubled the average flux of cataloged objects on the International Space Station and increased by about 3/4 that on China's Tiangong. In the same period, the Starlink large constellation saw an average increase in the flux of cataloged objects of about 20%. Some orbital planes, the "counter-rotating" ones with respect to the Cosmos 1408 debris cloud, were more affected than others, and the affected planes gradually changed over time, due to the differential precession of cloud and constellation nodes. However, being the Starlink constellation 70 km higher up, the flux of Cosmos 1408 cataloged debris steadily decreased over the period analyzed, due to the cloud orbital decay, reducing to just over a quarter of its extrapolated initial value after seven months. • A review of ASAT tests as a source of orbital debris was presented. • The impact of the Cosmos 1408 destruction was put into context. • The cataloged debris flux increase on space stations was computed and discussed. • The debris flux increase and evolution on Starlink orbit planes was analyzed. • A moratorium on this type of ASAT tests was recommended, including low LEO. [ABSTRACT FROM AUTHOR]

Modern geodetic technologies, including high-precision ground-based gravity measurements, satellite gravimetry, satellite altimetry, Global Navigation Satellite Systems (GNSS), and Interferometric Synthetic Aperture Radar(InSAR), offer a wealth of observations for monitoring global hydrological processes with exceptional accuracy and spatio-temporal resolutions. Mass redistribution and Earth's surface deformation over land related to global and regional water cycling can be inferred from modern gravimetry, altimetry, GNSS, and InSAR techniques. Hydrogeodesy becomes an emerging field of geodesy aiming to analyze the changes of water in the Earth system. The paper introduces the China's advances in hydrogeodesy in recent years. It brings together multiple geodetic teams' work from China, showcasing the application of modern geodetic technologies in the field of hydrology, including research on terrestrial water storage, groundwater storage, glaciers/ice sheets, and reservoir water storage. [ABSTRACT FROM AUTHOR]

Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas. [ABSTRACT FROM AUTHOR]

Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time. Nevertheless, these potential measurements only illustrate the maximum possible accessibility a person can have, which may not accurately measure real-world transit accessibility in urban areas. This paper introduces a novel methodology to measure positive public transit accessibility based on multi-source big public transit data such as Smart Card Data (SCD) and Global Navigation Satellite System trajectory data, which embed rich travel information and real-world spatio-temporal constraints. First, we use multi-source transit data to reconstruct trip chains, which are used to extract popular destinations. A novel transit accessibility measure is defined to account for latent trip information such as mode/route preference, opportunity attraction, and travel impedance that are difficult to capture explicitly via traditional normative measures. Finally, we produce accessibility maps to visualize time-varying and heterogeneous accessibility patterns distributed over the study region. We performed an empirical evaluation on real-world transit data collected in Shenzhen City, China, demonstrating the applicability and effectiveness of the proposed method in mapping positive transit accessibility over large metropolitan areas. The results and findings of the empirical study demonstrate that the proposed positive accessibility measure can better capture travel behavior characteristics and constraints than traditional normative measures. The measurement method can be used as a practical high-resolution mapping tool for transit decision makers in evaluating public transit systems, supporting strategic transit planning, and improving daily transit management. [ABSTRACT FROM AUTHOR]

To develop refined agriculture and improve Agricultural productivity, a new monitoring system has been proposed in this paper. Based on the actual situation of early agriculture and the actual national conditions of China, Geographic Information System (GIS) technology and Global Positioning System (GPS) technology have been combined. Based on the combination of GIS technology and GPS technology, the results show that the position of field vehicles can be displayed in the electronic MAP in real time within 5% error. On this basis, Agricultural production and cultivation can be realized, and the monitoring system can realize the real-time display of vehicle location in the field on electronic MAP to guide production and cultivation. The static test shows that the positioning accuracy of the four GPS receivers is the worst, and the positioning accuracy of MAP330 receiver and GPS25 receiver is better. However, the positioning accuracy of AGl32 receiver is the highest with the 0.37m error when compared with the error of 1.2m of other machines. Using GPS to measure the area, the error of farmland area and farmland side length is less than 5%, and the precision AGl32 receiver for precision Agricultural measurement is also improved with the proposed model. [ABSTRACT FROM AUTHOR]

Using GPS technology to retrieve atmospheric water vapor is a new water vapor detection method, which can effectively compensate for the shortcomings of conventional water vapor detection methods, to provide high-precision, large-capacity, near real-time water vapor information. In-depth study of ground-based GPS detection of atmospheric water vapor technology aims to further improve the accuracy and practicability of GPS inversion of water vapor and to explore its ability to detect atmospheric water vapor information to better serve the meteorological services. In this paper, the influence of the setting parameters of initial station coordinates, satellite ephemeris and solution observation on the total delay accuracy of the tropospheric zenith is discussed based on the observed data. In this paper, the observations obtained from the observation network consisting of 8 IGS stations in China in June 2013 are used to inverse the water vapor data of the 8 stations. The data of Wuhan station is further selected and compared with the data of Nanhu Sounding Station in Wuhan The error between the two data was between -6mm-6mm, and the trend of the two was almost the same, the correlation reached 95.8%. The experimental results also verify the reliability of ground-based GPS inversion of water vapor technology. [ABSTRACT FROM AUTHOR]

In this paper, we tried to reveal the characteristics of the crustal deformation in both the horizontal and vertical directions in Beijing using InSAR and GNSS observations. Regarding the serious land subsidence in Beijing plain, we also analysed the mechanism of the occurrence and development of the subsidence in combination with the tectonic settings. The GNSS results reveal that the crust in Beijing shows a significant left-lateral trend movement in the horizontal direction, while the vertical direction shows a gentle rise in the mountainous region and a significant subsidence in the plain area. The INSAR results shows a detailed subsidence area and the deformation characteristics were analyzed considering the fault activity. The foundation of geological structure dominates the subsiding in the Beijing Plain. The exploitation of groundwater exacerbates the level of subsidence and has new development. The active faults controlled the development of the subsiding in present days. [ABSTRACT FROM AUTHOR]

There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect. [ABSTRACT FROM AUTHOR]

High-intensity coal mining activities in China's western mining area are damaging fragile ecological environments. The key to coordinating ecological protection and coal mining is to understand the law of overlying strata migration in high-intensity mining. However, it is difficult for conventional monitoring methods to efficiently monitor overburden movement in high-intensity mining areas in real time. In this paper, a space-sky-surface (3S) integrated system is proposed to conquer this challenge. This monitoring system consists of three parts: a space monitoring component (a combination of a global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR)), a sky observation component (an unmanned aerial vehicle (UAV) equipped with a measuring camera (MC)), and a surface exploration component (ground-penetrating radar (GPR) and high-density electricity method (HDEM)). This system was successfully applied to the 12,401 longwall face in the Shangwan coal mine (the maximum mining height is 8.8 m). The deformation cloud map of the entire mining area was obtained through InSAR monitoring technology accompanying the 3D laser scanning to improve the accuracy in the local region of 12,401 longwall face. The distribution characteristics and dynamic evolution of surface fractures were obtained using UAV and MC (visible light camera + infrared camera). Using the HDEM and GPR technologies, the fracture development was well detected. Based on the strata movement theory, combined with the monitoring results, a three-dimensional model of overburden rock fracture after 12,401 longwall face mining was constructed. Monitoring results and the fracture model provide a basis for controlling air leakage in the goaf and remediation of surface fracture. [ABSTRACT FROM AUTHOR]

Geological disasters such as landslides and debris flows pose a serious threat to human life and property. To mitigate this risk, monitoring and early warning systems are essential. However, monitoring high-angle landslide areas can be challenging due to the steep and complex terrain, making it difficult to carry out large-scale and refined deformation measurements using existing methods. This paper proposes a method to measure the large-scale deformation of landslide bodies based on nap-of-the-object photogrammetry. The method uses UAVs to acquire high-resolution 3D models of steep and high landslide areas at multiple time periods. Then, 3D model matching is employed to obtain accurate variation information for terrain deformation measurement. To obtain fine 3D models, a terrain-adaptive nap-of-the-object photogrammetric flight planning is applied to design the optimal photographic positions and directions for capturing ultra-high-resolution images. The images are processed using photogrammetric principles and technologies to produce fine 3D models. For terrain deformation measurement, an algorithm is proposed to obtain 3D correspondences by fusing DEM differential and 3D model texture matching. The 3D points variation vectors are then calculated, and the large-scale deformation measurement results of the landslide body can be derived after the vectors are aggregated. Experiments were conducted on the Lijiebeishan landslide in Gansu Province, western China. The results showed that the proposed deformation measurement method was highly effective in accurately detecting areas with displacement greater than 5 cm, and the large-scale deformation trend is consistent with GNSS predictions. In conclusion, the proposed method is an effective way to measure the large-scale deformation of landslide bodies in high-angle landslide areas, providing a valuable tool for monitoring and early warning systems. [ABSTRACT FROM AUTHOR]

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods. [ABSTRACT FROM AUTHOR]

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Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirectional Sliding Window (BSW) method, for PCA boundary recognition. We first collected the latitude and longitude coordinates of the four vertices of straight quadrilaterals using the Global Positioning System—Real Time Kinematic (GPS-RTK) measurement principle for precise single-point positioning, analyzed single-point positioning accuracy, and determined the measurement error distribution models. Next, we took points at equal intervals along one straight line segment and two curved line segments with curvature radii of 70 m to 300 m, for simulation experiments. BSW was adopted to recognize the Possible Irrelevant Points (PIP) and Relevant Points (RP), which were used to identify PCA boundaries. Experiments show that when the proposed BSW algorithm is used and the single-point positioning accuracy is at the centimeter level, PCA boundary recognition for straight polygons reaches single-point positioning accuracy, and that for curved polygons reaches decimeter-level accuracy. [ABSTRACT FROM AUTHOR]

A large number of studies have shown that equatorial plasma bubbles (EPBs) occur mainly after sunset, and they usually drift eastward. However, in this paper, an unusual EPB event was simultaneously observed by an all-sky imager and the global navigation satellite system (GNSS) network in southern China, during the recovery phase of a geomagnetic storm that happened on 6–8 November 2015. Observations from both techniques show that the EPBs appeared near dawn. Interestingly, the observational results show that the EPBs continued to develop after sunrise, and they disappeared about 1 h after sunrise. The development stage of EPBs lasted for at least about 3 h. To our knowledge, this is the first time that the evolution of EPBs developing around sunrise was observed by an all-sky imager and the GNSS network. Our observation showed that the EPBs drifted westward, which was different from the usual eastward drifts of post-sunset EPBs. The simulation from the Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIE-GCM) suggest that the westward drift of EPBs should be related to the enhanced westward winds at storm time. Besides this, bifurcation and merging processes of EPBs were observed by the all-sky imager in the event. Associated with the development of EPBs, an increase in the peak height of the ionospheric F region was also observed near sunrise, and we suggest the enhanced upward vertical plasma drift during the geomagnetic storm plays a major role in triggering the EPBs near sunrise. [ABSTRACT FROM AUTHOR]

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations' data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city's gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China. [ABSTRACT FROM AUTHOR]

Water impoundment of the Three Gorges Reservoir has induced more than 5,000 landslides or potential landslides. These are huge threats to the safety of the dam and the people living in the reservoir area. The deformation characteristics and failure mode of the landslides are important factors in landslide stability evaluation and reinforcement design. The work reported in this paper used the Zhujiadian landslide as an example to study the deformation characteristics and failure mode of the landslides in the Three Gorges Reservoir region. Field investigations were conducted to analyze the structure of the landslide. Then the deformations of the landslide were observed using an integrated monitoring system, including extensometer monitoring for crack development, GPS monitoring for surface displacement, and inclinometer monitoring for internal displacement. The results obtained suggest that the Zhujiadian landslide is a multi-rotational landslide containing three main rotational sub-landslides at present. The deformation and failure of this landslide have developed from front to back. The failure of the front part opens up space for the back part to slide forward into. The coupled actions of the reservoir water and rainfall have led to the failure of the Zhujiadian landslide. Finally, a risk evaluation of the water surge potentially generated by the landslide was conducted by calculating the maximum wave height at the opposite side of the river. The findings of this paper provide some useful information for the stability evaluation and reinforcement design of landslides in similar geological settings in the Three Gorges Reservoir area and in other parts of the world. [ABSTRACT FROM AUTHOR]

Tropospheric delay has always been an important issue in Global Navigation Satellite System (GNSS) processing. Empirical tropospheric delay models are difficult to simulate complex and volatile atmospheric environments, resulting in poor accuracy of the empirical model and difficulty in meeting precise positioning demand. In recent years, some scholars proposed to establish real-time tropospheric product by using real-time or near-real-time GNSS observations in a small region, and achieved some good results. This paper uses real-time observing data of 210 Chinese national GNSS reference stations to estimate the tropospheric delay, and establishes ZWD grid model in the country wide. In order to analyze the influence of tropospheric grid product on wide-area real-time PPP, this paper compares the method of taking ZWD grid product as a constraint with the model correction method. The results show that the ZWD grid product estimated based on the national reference stations can improve PPP accuracy and convergence speed. The accuracy in the north(N), east(E) and up(U) direction increase by 31.8%,15.6% and 38.3%, respectively. As with the convergence speed, the accuracy of U direction experiences the most improvement. [ABSTRACT FROM AUTHOR]

The solid Earth deforms elastically in response to variations of surface atmosphere, hydrology, and ice/glacier mass loads. Continuous geodetic observations by Global Positioning System (CGPS) stations and Gravity Recovery and Climate Experiment (GRACE) record such deformations to estimate seasonal and secular mass changes. In this paper, we present the seasonal variation of the surface mass changes and the crustal vertical deformation in the South China Block (SCB) identified by GPS and GRACE observations with records spanning from 1999 to 2016. We used 33 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs) in SCB. The average weighted root-mean-square (WRMS) reduction is 38% when we subtract GRACE-modeled vertical displacements from GPS time series. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution in and around the South China Block. The correlation between GRACE and GPS time series is analyzed which provides a reference for further improvement of the seasonal variation of CGPS time series. The results of the GRACE observations inversion are the surface deformations caused by the surface mass change load at a rate of about -0.4 to -0.8 mm/year, which is used to improve the long-term trend of non-tectonic loads of the GPS vertical velocity field to further explain the crustal tectonic movement in the SCB and surroundings. [ABSTRACT FROM AUTHOR]

Ionospheric disturbances constitute the main restriction factor for precise positioning techniques based on global positioning system (GPS) measurements. Simultaneously, GPS observations are widely used to determine ionospheric disturbances with total electron content (TEC). In this paper, we present an analysis of ionospheric disturbances over China mid- and low-latitude area before and during the magnetic storm on 17 March 2015. The work analyses the variation of magnetic indices, the amplitude of ionospheric irregularities observed with four arrays of GPS stations and the influence of geomagnetic storm on GPS positioning. The results show that significant ionospheric TEC disturbances occurred between 10:30 and 12:00 UT during the main phase of the large storm, and the static position reliability for this period are little affected by these disturbances. It is observed that the positive and negative disturbances propagate southward along the meridian from mid-latitude to low-latitude regions. The propagation velocity is from about 200 to 700ms-1 and the amplitude of ionospheric disturbances is from about 0.2 to 0.9 TECU min-1. Moreover, the position dilution of precession (PDOP) with static precise point positioning (PPP) on storm and quiet days is 1.8 and 0.9 cm, respectively. This study is based on the analysis of ionospheric variability with differential rate of vertical TEC (DROVT) and impact of ionospheric storm on positioning with technique of GPS PPP. [ABSTRACT FROM AUTHOR]

The Chinese meteorological satellites Fengyun (FY) have recently developed rapidly, resulting in a substantial wealth of precipitable water vapor (PWV) products. However, these latest operational PWV products still need to be systematically validated. In this paper, we focus on the PWV products derived from the FY-3D/Medium Resolution Spectral Imager II (MERSI-II), FY-2H/Visible and Infrared Spin Scan Radiometer (VISSR), and FY-4A/Advanced Geostationary Radiation Imager (AGRI), and test their performance by using the global navigation satellite system (GNSS) data. We find that FY-4A/AGRI performs best in various regions of China in 2021. Compared to GNSS-PWV, FY-4A/AGRI PWV has a correlation coefficient 0.95, a root mean square error (RMSE) of 4.67 mm, and a mean deviation (MB) of −0.73 mm, respectively. Significant discrepancies between FY-PWV and GNSS-PWV are observed in the Qinghai-Tibetan Plateau (MB: −4.86 mm for FY-2H/VISSR and − 5.12 mm for FY-4A/AGRI) and the Sichuan Basin (MB: 5.52 mm for FY-2H/VISSR, and 3.82 mm FY-4A/AGRI). All FY-PWVs exhibit season-dependent variations, and their discrepancies with GNSS-PWV during winter are smaller than in summer. Finally, we analyze the distribution and changes of water vapor in China from 2019 to 2022 using the preferred FY-4A/AGRI PWV. The results reveal a gradual decrease in the annual mean water vapor content, diminishing by approximately 0.33 mm during this period. Notably, this decrease occurs in southern China, and the water vapor content spikes in summer, culminating at 23.59 mm in 2022. Our findings strongly correlate with the precipitation changes documented in the China Climate Bulletin (2022). • This study is an application of 3S (remote sensing, GNSS, and GIS) technology in meteorology. • A harmonized standard method has been used for the evaluation of FY-PWVs. • FY-4A/AGRI PWV exhibits the best consistency with GNSS PWV in mainland China. • The distribution and changes of water vapor in China in recent years have been investigated. [ABSTRACT FROM AUTHOR]