Comparative analysis of the correction effect of different environmental loading products on global GNSS coordinate time series

He, Yuefan; Nie, Guigen; Wu, Shu‐Fang Vivienne; Li, Haiyang

doi:10.1016/j.asr.2022.08.009

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…From these, the displacement of the Earth's surface due to load changes is calculated [57]. The accuracy and reliability of this data product have been confirmed through quality analyses involving various load models and GNSS data [74,75]. Figure 9 presents the time series information of load deformation at each land subsidence monitoring sampling point.…”

Section: Surface Mass Loadingmentioning

confidence: 86%

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

He,

Yang,

et al. 2024

Remote Sensing

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In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction.

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Section: Surface Mass Loadingmentioning

confidence: 86%

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

He,

Yang,

et al. 2024

Remote Sensing

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“…The geographical location and information of 5 GNSS reference stations are present in Figure 1 and Table 1. The nonlinear motion of GNSS reference stations is affected by environmental loading [25][26][27], thermal expansion [28][29][30], and tidal motions [6,31,32], which make GNSS reference stations have small motion and are expressed in the GNSS position time series. Position time series among the nearby GNSS reference stations may have strong correlations due to the relevance of the geophysical effects they are subjected to [22], so this provides the basis for modeling and interpolation of the GNSS position time series.…”

Section: Gnss Position Time Seriesmentioning

confidence: 99%

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

Li,

Lu,

et al. 2023

Remote Sensing

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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.

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“…Moreover, the length of time series in EEMD application is an important factor to consider, which is essential to investigate (approximately 2015.11 years). To find a rational explanation, detailed analysis is conducted and utilized hydrological loading data from the EOST Loading Service (http://loading.u-strasbg.fr/listdata.php?dirn=dicf) (He et al, 2022), which possesses a spatial resolution of 0.5°× 0.5° and a time resolution of 3 hours. Additionally, the daily rainfall time series provided by the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/) is adopted (Pan et al, 2022).…”

Section: Data Processing and Analysismentioning

confidence: 99%

Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

Li,

Guo

2023

Preprint

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Abstract. Ensemble Empirical Mode Decomposition (EEMD) has been widely used in the data analysis. Adaptive EEMD further improves computational efficiency through the adaptability in the white noise amplitude and set average number. However, its effectiveness of the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding on the inevitable missing data and offsets issues have not been comprehensively validated. It is verified with 5- year time series through 300 simulations for each case. The results show that high accuracy could reach for the overall random missing rate below 15 % and avoiding consecutive missing epochs exceeding 30. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of vertical component of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network, demonstrates the advantage in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients, power spectral density (PSD) indices and signal noise ratio (SNR), the means for adaptive EEMD are 0.36, -0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, -0.50 and 0.23. Meanwhile, significance test of the residuals further substantiate the effectiveness in periodic signal extraction, which shows there is no annual signal remained. Also, the longer the series, the higher accuracy of extracted periodic signal is reasonable concluded via the significance test. Furthermore, the time-varying periodic characteristics is more conducive to analyze the driving factors. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on the proper dealing with missing data and offsets.

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Comparative analysis of the correction effect of different environmental loading products on global GNSS coordinate time series

Cited by 6 publications

References 55 publications

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

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