This paper aims to address the limitations of the distribution number and uniformity of Continuously Operating Reference Stations (CORS) and their impact on the reliability of inverting regional groundwater storage (GWS) based on Green’s function method and using global navigation satellite system (GNSS) data. A fusion method on the inversion of regional GWS changes from GNSS and the Gravity Recovery and Climate Experiment (GRACE) was proposed in this paper. Taking the Shaanxi–Gansu–Ningxia (SGN) region as an example, the in situ groundwater level data from ten CORS stations and eight wells were used for test analyses. In this paper, an atmospheric pressure model from the European Centre for Medium-Range Weather Forecasts (ECMWF), a global land data assimilation system (GLDAS), a WaterGAP global hydrology model (WGHM), and mean sea level anomaly (MSLA) data were used to quantitatively monitor the influence of vertical deformation caused by non-tidal environmental load. After deducing these loading deformations from the filtered time series of non-linear monthly geodetic height from the GNSS, the GWS changes in the SGN region from 2011 to 2014 were inverted. Meanwhile, the change in surface water storage from the GLDAS and WGHM models were removed from the terrestrial water storage (TWS) changes derived from GRACE. On this basis, the remove–restore theory in the Earth’s gravity field was introduced to both fuse the inversion results and obtain the regional GWS changes based on the fusion method. The results showed the following: (1) The local characteristics from the fusion results were more prominent than those of GRACE on the spatial scale, such as in the southwest and northeast in the study area. In addition, the fusion results were more uniform than those from GNSS, especially for the sparse and missing areas in which CORS stations were located, and the local effect was weakened. (2) On the time scale, compared with GRACE, the trends in GWS changes obtained from the fusion method and from GNSS inversion were roughly the same as the in situ groundwater level changes. (3) For the in situ groundwater wells “6105010031” and “6101260010”, the correlation coefficients of the fusion result were 0.53 and 0.56, respectively. The accuracy of the fusion method was slightly higher than that from GNSS, which indicates that the fusion method may be more effective for areas where CORS stations are missing or sparsely distributed. The methods in this paper can provide significant reference material for hydrodynamic research, sustainable management of water resources, and the dynamic maintenance of height data.

During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes.

The terrestrial water resources in Shaanxi–Gansu–Ningxia (SGN) region are relatively scarce, and its climate change is unstable. Research on the deformation driven by terrestrial water load is of great significance to the dynamic maintenance of reference station networks. In this paper, data derived from Gravity Recovery and Climate Experiment (GRACE) and Global Navigation Satellite System (GNSS) from 2010 to 2014 were combined to monitor the spatiotemporal characteristics of surface vertical deformation caused by terrestrial water load change. The single scale factor was calculated by comparing CPC, WGHM, and GLDAS hydrological model to restore filtering leakage signal. The singular spectrum analysis (SSA) method was used to extract the principal component of temporal vertical deformation, and its spatial distribution was analyzed. At the same time, in order to study the relationship between the terrestrial water load deformation from GRACE and that from GNSS, the first-order term correction, the Atmosphere and Ocean De-aliasing Level-1B product (GAC) correction, and the first-order load LOVE number correction for GRACE were adopted in this paper. In addition, a quantitative comparative analysis of both the monitoring results was carried out. The results show that the time-variable characteristics of surface vertical deformation characterized by the filtered three hydrological models were consistent with those of GRACE. The correlation coefficient and Nash–Sutcliffe efficiency coefficient (NSE) values were the highest in the GLDAS model and the GRACE model, respectively; the former index is 0.93, while the latter is 0.85. The crustal vertical deformation from terrestrial water load showed a declining rate from 2010 to 2014. Its spatial change rate showed an obvious ladder distribution, with the surface subsidence rate gradually decreasing from south to north. In addition, weighted root mean square (WRMS) contribution rate of the crustal vertical deformation resulting from GRACE with GAC correction between the different GNSS stations ranged from 18.52% to 54.82%. The correlation coefficient between them was close to 0.70. After deducting the mass load impact of GRACE only, the WRMS contribution rate of the corresponding stations decreased from −8.42% to 21.18%. The correlation coefficient between them reduced noticeably. Adding GAC back can increase the comparability with GRACE and GNSS in terms of monitoring the crustal vertical deformation. The annual amplitude and phase of surface vertical deformation resulting from GRACE with GAC correction were close to those of GNSS. The research results can help to explore the motion mechanism between water migration and surface deformation, which is of benefit in the protection of the water ecological environment in the region.

Monitoring regional terrestrial water load deformation is of great significance to the dynamic maintenance and hydrodynamic study of the regional benchmark framework. In view of the lack of a spatial interpolation method based on the GNSS (Global Navigation Satellite System) elevation time series for obtaining terrestrial water load deformation information, this paper proposes to employ a CORS (Continuously Operating Reference Stations) network combined with environmental loading data, such as ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric data, the GLDAS (Global Land Data Assimilation System) hydrological model, and MSLA (Mean Sea Level Anomaly) data. Based on the load deformation theory and spherical harmonic analysis method, we took 38 CORS stations in southeast Zhejiang province as an example and comprehensively determined the vertical deformation of the crust as caused by regional terrestrial water load changes from January 2015 to December 2017, and then compared these data with the GRACE (Gravity Recovery and Climate Experiment) satellite. The results show that the vertical deformation value of the terrestrial water load in southeast Zhejiang, as monitored by the CORS network, can reach a centimeter, and the amplitude changes from −1.8 cm to 2.4 cm. The seasonal change is obvious, and the spatial distribution takes a ladder form from inland to coastal regions. The surface vertical deformation caused by groundwater load changes in the east–west–south–north–central sub-regions show obvious fluctuations from 2015 to 2017, and the trends of the five sub-regions are consistent. The amplitude of surface vertical deformation caused by groundwater load change in the west is higher than that in the east. We tested the use of GRACE for the verification of CORS network monitoring results and found a relatively consistent temporal distribution between both data sets after phase delay correction on GRACE, except for in three months—November in 2015, and January and February in 2016. The results show that the comprehensive solution based on the CORS network can effectively improve the monitoring of crustal vertical deformation during regional terrestrial water load change.

Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature T_m was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations.