As the largest groundwater drainage region in China, the per capita water resources in the North China Plain (NCP) account for only one-seventh of the country’s available water resources. Currently, the NCP is experiencing a serious water shortage due to the overexploitation of groundwater resources and a subsequent series of natural disasters. Thus, accurate regional assessments and effective water resource management policies are of critical importance. To accomplish this phenomenon, the daily terrestrial water storage anomaly (TWSA) over the NCP is calculated from the combination of the GNSS vertical deformation sequences (seasonal items) and GRACE (trend items). The groundwater storage anomaly (GWSA) in the NCP is obtained by subtracting the canopy water, soil water, and snow water equivalent components from the TWSA. The inversion results of this study are verified by comparisons with the Global Land Data Assimilation System (GLDAS) data products. The elevated annual amplitude areas are located in Beijing and Tianjin, and the Pearson correlation coefficient (PCC), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) between the two GWSA results are 0.67, 4.01 cm, and 0.61, respectively. This indicates that the methods proposed in this study are reliable. Finally, the groundwater drought index was calculated for the period from 2011 to 2021, and the results showed that 2019 was the driest year, with a drought severity index value of −0.12, indicative of slightly moderate drought conditions. By calculating and analyzing the annual GWSA, this work shows that the South–North Water Transfer Project does provide some regional drought mitigation.

Gravity matching navigation is one of key technologies for underwater navigation. Terrain contour matching (TERCOM), as a sequence matching method, is insensitive to the error of the initial position and has high robustness. However, it suffers from the matching low-efficiency and out-of-domain mismatches due to the grid traversal search and the domain boundary limitation, which affects TERCOM underwater navigation ability. To address these problems, a novel domain-center adaptive-transfer matching method (DAMM) is proposed. It first uses the optimal positioning of horizontal-and-vertical pre-matching lines to realize the adaptive transfer of the domain center from the INS-indicated ending point to the on-line optimal matching position. Then, a new re-matching grid domain based on this on-line transferred domain-center index value is adaptively generated, and the repositioning and update for the optimal matching position are performed by following the optimization principle of the matching indexes, which contributes to improving the matching efficiency and out-of-domain positioning reliability of underwater gravity matching navigation. Experimental results verify the outstanding underwater navigation performance of DAMM on the matching efficiency and out-of-domain positioning reliability. For three different gravity tracks, the DAMM matching efficiency, compared with TERCOM, is improved by 71%, while the number of out-of-domain mismatches and positioning accuracy are improved by more than 92% and 84%, respectively, on average.

Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R²) are equal to 0.76 and 0.65, respectively; the values of PCC and R² between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R² by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO.

Atmospheric pressure and zenith wet delay (ZWD) are essential for global navigation satellite system (GNSS) tropospheric correction and precipitable water vapor (PWV) retrieval. As the development progresses of real-time GNSS kinematic technology, moving platforms, such as airborne and shipborne, require high-quality tropospheric delay information to pre-correct errors. Most existing tropospheric models are only applicable to the Earth's surface and exhibit poor accuracies in high-altitude areas due to simple vertical fitting functions and limited temporal resolution of the underlying parameters. Hence, an improved global empirical pressure and ZWD model is developed using 5-year ERA5 hourly reanalysis data, called IGPZWD, which takes seasonal and intraday variations into consideration. The vertical accuracy and applicability of IGPZWD model are further optimized by introducing the annual and semi-annual harmonics for pressure and ZWD height-scale factors of exponential function with three orders. Taking the ERA5 and radiosonde profile data in 2020 as reference, the pressure and ZWD of IGPZWD model show superior performance compared to those of three state-of-the-art models, i.e., GPT3, IGPT, and GTrop. Furthermore, IGPZWD-predicted zenith tropospheric delay (ZTD) yields improvements of up to 65.7 %, 2.4 %, and 7.8 % over that of GPT3, RGPT3, and GTrop models on a global scale, respectively. The proposed vertical correction algorithm effectively weakens the impact of accumulation error caused by excessive height difference, achieving optimal accuracy and feasibility in the high-altitude area. The IGPZWD model can be extensively applied in GNSS kinematic precision positioning, as well as atmospheric water vapor sounding.

The Global Ecosystem Dynamics Investigation (GEDI) contains a full waveform, multi-beam laser altimeter, which provides vertical vegetation structure information. However, few studies have assessed the accuracy of using GEDI LiDAR data in retrieving ground topography from dense forest environments. There has been no in-depth study assessing the accuracy of GEDI data with different kinds of elevation index in footprint samples from densely forested areas (canopy cover >70%). To address this limitation, this study aims to assess the accuracy of ground topography estimates from dense forested terrain through the combination of airborne LiDAR data. The results show that under dense forest conditions, the mean elevation in the GEDI footprint performed more accurate than the max elevation, the min elevation and the median elevation in the GEDI footprint. GEDI provides reasonable estimates of mean terrain height, with R ² of 0.99, root mean squared error (RMSE) of 6.08 m and mean absolute error (MAE) of 3.92 m. In addition, dense forest ecosystems also reduce the accuracy of the terrain height estimates. Overall, GEDI with its full waveform technology represents promising spaceborne LiDAR for terrain height retrieval in dense forest environments.

With the completion of the BeiDou Global Navigation Satellite System (BDS-3), the multi-system precise point positioning ambiguity resolution (PPP-AR) has been realized. The satellite phase fractional cycle bias (FCB) is a key to the PPP-AR. Compared to the combined ionosphere-free (IF) model, the undifferenced and uncombined (UDUC) model retains all the information from the observations and can be easily extended to arbitrary frequencies. However, the FCB is difficult to apply directly to the UDUC model. An observable-specific signal bias (OSB) can interact directly with the original observations, providing complete flexibility for PPP-AR for multi-frequency multi-GNSS. In this study, the OSB product generation for the GPS (G), Galileo (E), and BDS-3 (C) systems is performed using 117 globally distributed multi-GNSS experiment (MGEX) stations, and their performances are evaluated. Then, the PPP-AR comparison and analysis of the two positioning models of the UDUC and IF are conducted. The results show that the stability of OSB products of the three systems is better than 0.05 ns. For the precise point positioning (PPP) ambiguity fixed solution, with comparable positioning accuracy and convergence time to the products of both the Wuhan University (WUM) and the Centre National d’Etudes Spatials (CNES) institutions, an average fixed-ambiguity rate is over 90%. Compared to the PPP float solution, the PPP-AR has the most significant improvement in positioning accuracy in the E-direction. The average improvements in the positioning accuracy under the IF and UDUC models in the static and kinematic modes are higher than 45% and 40%, respectively. The convergence times of the IF and UDUC models are improved on average by 48% and 60% in the static mode and by 40% and 55% in the kinematic mode, respectively. Among the IF and UDUC positioning models, the former has slightly better positioning accuracy and convergence time than the latter for the PPP float solution. However, both models have comparable positioning accuracy and convergence time after the PPP-AR. The GCE multi-system combination is superior to other system combinations. The average convergence time for the static PPP fixed solution is 8.5 min, and the average convergence time for the kinematic PPP fixed solution is 16.4 min.