Search Results (118)

Real-Time Precise Orbit Determination (RTPOD) of Low Earth Orbit (LEO) satellites relies primarily on onboard GNSS observations and may suffer from degraded performance when observation geometry weakens or tracking conditions deteriorate within satellite formations. To enhance the robustness and accuracy of RTPOD under such conditions, a cooperative Extended Kalman Filter (EKF) framework that fuses onboard GNSS and inter-satellite link (ISL) range measurements is established, integrated with an iterative Detection, Identification, and Adaptation (DIA) quality control algorithm. By introducing high-precision ISL range measurements, the strategy increases observation redundancy, improves the effective observation geometry, and provides strong relative position constraints among LEO satellites. This constraint strengthens solution stability and convergence, while simultaneously enhancing the sensitivity of the DIA-based quality control to observation outliers. The proposed strategy is validated in a simulated real-time environment using Centre National d’Etudes Spatiales (CNES) real-time products and onboard observations of the GRACE-FO mission. The results demonstrate comprehensive performance enhancements for both satellites over the experimental period. For the GRACE-D satellite, which suffers from about 17% data loss and a cycle slip ratio several times higher than that of GRACE-C, the mean orbit accuracy improves by 39% (from 13.1 cm to 8.0 cm), and the average convergence time is shortened by 44.3%. In comparison, the GRACE-C satellite achieves a 4.2% mean accuracy refinement and a 1.3% reduction in convergence time. These findings reveal a cooperative stabilization mechanism, where the high-precision spatiotemporal reference is transferred from the robust node to the degraded node via inter-satellite range measurements. This study demonstrates the effectiveness of the proposed method in enhancing the robustness and stability of formation orbit determination and provides algorithmic validation for future RTPOD of LEO satellite formations or large-scale constellations. Full article

Determining long-term changes in global ice and water storage from satellite gravimetry remains challenging due to the limited temporal coverage of high-resolution missions. Here, we combine Satellite Laser Ranging (SLR) and Gravity Recovery and Climate Experiment (GRACE) data to reconstruct large-scale, non-linear mass variations from 1995 to 2024, extending gravity-based observations into the pre-GRACE era while preserving spatial detail through backward extrapolation. The combined model reveals widespread and statistically significant accelerations in global water and ice mass changes and enables the identification of key turning points in their temporal evolution. Results indicate that in Svalbard, a non-linear transition in ice mass balance occurred in late 2004, followed by a pronounced acceleration of mass loss due to climate warming. Glaciers in the Gulf of Alaska exhibit persistent mass loss with a marked intensification after 2012, while in the Antarctic Peninsula, ice mass loss substantially slowed and a potential trend reversal emerged around 2021. The reconstructed mass anomalies show strong consistency with independent satellite altimetry and climate indicators, including a clear response to the 1997/1998 El Niño event prior to the GRACE mission. These findings demonstrate that integrating SLR with GRACE enables robust detection of non-linear, climate-driven mass redistribution on a global scale and provides a physically consistent extension of satellite gravimetry records beyond the GRACE era. Full article

The ongoing global decline in groundwater levels poses significant challenges for sustainable water management. Satellite gravity missions, such as the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), provide valuable estimates of groundwater storage changes at regional scales. However, the relatively coarse spatial resolution of these satellite data limits their direct applicability to local groundwater management. In this study, we address this limitation for China by analyzing groundwater monitoring data from 108 cities with shallow groundwater use and 37 cities with deep groundwater use from the period 2019–2022, integrating in situ groundwater level records, official monitoring reports, monthly dynamic data, and GRACE-FO-derived groundwater storage estimates. Our findings reveal rapid groundwater depletion in northern China, especially in Xinjiang and Hebei Provinces. Fluctuations in shallow groundwater levels in Beijing and Jiangsu are closely related to precipitation variability. For deep aquifer regions, GRACE-FO-derived groundwater storage changes show a moderate Pearson correlation coefficient of 0.45 and groundwater level variations. Regional analysis for 2019–2021 in the Northeast Plain and the Huang–Huai–Hai Basin indicates better agreement between satellite-derived storage and groundwater levels, with a Pearson correlation coefficient of 0.58 in the Huang–Huai–Hai Basin. Groundwater level dynamics are strongly influenced by both precipitation and pumping, with an approximate three-month lag between precipitation events and groundwater storage responses. Overall, satellite gravity data are suitable for use in regional groundwater assessment and could serve as valuable indicators in areas with intensive deep groundwater exploitation. To enable fine-scale groundwater management, future work should focus on improving the spatial resolution through downscaling and other advanced techniques. Full article

Low Earth orbit (LEO) satellites, characterized by low altitudes, high velocities, and strong ground signal reception, have become an essential and dynamic component of modern global navigation satellite systems (GNSS). However, orbit decay induced by atmospheric drag poses persistent challenges to maintaining stable trajectories. Frequent orbit maneuvers, though necessary to sustain nominal orbits, introduce significant difficulties for precise orbit determination (POD) and navigation augmentation, especially under complex operational conditions. Unlike most existing methods that rely on Two-Line Element (TLE) data—often affected by noise and limited accuracy—this study directly utilizes onboard GNSS observations in combination with real-time precise ephemerides. A novel time-series indicator is proposed, defined as the geometric root-mean-square (RMS) distance between reduced-dynamic and kinematic orbit solutions, which is highly responsive to orbit disturbances. To further enhance robustness, a sliding window-based adaptive thresholding mechanism is developed to dynamically adjust detection thresholds, maintaining sensitivity to maneuvers while suppressing false alarms. The proposed method was validated using eight representative maneuver events from the GRACE-FO satellites (May 2018–June 2022), successfully detecting seven of them. One extremely short-duration maneuver was missed due to the limited number of usable GNSS observations after quality-control filtering. To examine altitude-related applicability, two Sentinel-3A maneuvers were also analyzed, both successfully detected, confirming the method’s effectiveness at higher LEO altitudes. Since the thrust magnitudes and durations of the Sentinel-3A maneuvers are not publicly available, these cases primarily serve to verify applicability rather than to quantify sensitivity. Experimental results show that for GRACE-FO maneuvers, the proposed method achieves near-real-time responsiveness under long-duration, high-thrust conditions, with an average detection delay below 90 s. For Sentinel-3A, detections occurred approximately 7 s earlier than the reported maneuver epochs, a discrepancy attributed to the 30 s observation sampling interval rather than methodological bias. Comparative analysis with representative existing methods, presented in the discussion section, further demonstrates the advantages of the proposed approach in terms of sensitivity, timeliness, and adaptability. Overall, this study presents a practical, efficient, and scalable solution for real-time maneuver detection in LEO satellite missions, contributing to improved GNSS augmentation, space situational awareness, and autonomous orbit control. Full article

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article

The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow-on mission (GRACE-FO, collectively referred to as GRACE) to investigate the spatiotemporal dynamics of hydrological mass changes in the Amazon Basin from 2002 to 2021. Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. This distribution likely reflects the interplay between precipitation and topography. Vertical displacement measurements from the Global Navigation Satellite System (GNSS) show strong correlations with GRACE-derived hydrological load deformation (mean Pearson correlation coefficient = 0.72) and reduce its root mean square (RMS) by 35%. Furthermore, the study demonstrates that existing hydrological models, which neglect groundwater dynamics, underestimate hydrological load deformation. Principal component analysis (PCA) of the Amazon GNSS network demonstrates that the first principal component (PC) of GNSS vertical displacement aligns with abrupt interannual TWS fluctuations identified by GRACE during 2010–2011, 2011–2012, 2013–2014, 2015–2016, and 2020–2021. These fluctuations coincide with extreme precipitation events associated with the El Niño–Southern Oscillation (ENSO), confirming that ENSO modulates basin-scale interannual hydrological variability primarily through precipitation anomalies. This study provides new insights for predicting extreme hydrological events under climate warming and offers a methodological framework applicable to other critical global hydrological regions. Full article

Groundwater is a critical yet understudied resource in Peru, where surface water has traditionally dominated national assessments. This study provides the first country-scale analysis of groundwater storage (GWS) variability in Peru from 2003 to 2023 using satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions. We used the GRACE Data Assimilation-Data Mass Modeling (GRACE-DA-DM GLV3.0) dataset at 0.25° resolution to estimate annual GWS trends and evaluated the influence of El Niño–Southern Oscillation (ENSO) events and anthropogenic extraction, supported by in situ well data from six major aquifers. Results show a sustained GWS decline of 30–40% in coastal and Andean regions, especially in Lima, Ica, Arequipa, and Tacna, while the Amazon basin remained stable. Strong correlation (r = 0.95) between GRACE data and well records validate the findings. Annual precipitation analysis from 2003 to 2023, disaggregated by climatic zone, revealed nearly stable trends. Coastal El Niño events (2017 and 2023) triggered episodic recharge in the northern and central coastal regions, yet these were insufficient to reverse the sustained groundwater depletion. This research provides significant contributions to understanding the spatiotemporal dynamics of groundwater in Peru through the use of satellite gravimetry data with unprecedented spatial resolution. The findings reveal a sustained decline in GWS across key regions and underscore the urgent need to implement integrated water management strategies—such as artificial recharge, optimized irrigation, and satellite-based early warning systems—aimed at preserving the sustainability of the country’s groundwater resources. Full article

Terrestrial water storage (TWS) is a critical component of the hydrological cycle and plays a key role in regional water resource management. The launch of the Gravity Recovery and Climate Experiment (GRACE) satellite mission in 2002 has provided precise measurements of TWS, enabling systematic investigations into its spatial pattern and driving mechanisms. However, a comprehensive evaluation of the spatial drivers of TWS variations across China is still lacking. In this study, we employed a robust machine learning model to capture the spatial patterns of TWS in China and further applied the Shapley Additive Explanations (SHAP) method to disentangle the individualized effects of hydroclimatic variables. Our findings reveal that precipitation is the dominant driver in northern and southern China, while soil moisture and snow water equivalent are key contributors on the Tibetan Plateau. In northwestern China, air pressure and groundwater runoff are the main influencing factors, whereas temperature shows a pronounced negative effect. Importantly, most variables demonstrate non-monotonic influences: in particular, we found that the importance of precipitation diminishes beyond a certain threshold, and surface pressure shifts sharply toward a negative impact. The explainable machine learning framework demonstrated strong adaptability in identifying complex drivers of TWS, offering a powerful methodological advancement for exploring TWS dynamics and providing valuable insights for water resource management in China. Full article

To address the limited inversion accuracy caused by low-fidelity data in satellite gravimetry, this study proposes a data preprocessing framework based on iterative residual correction. Utilizing Level-1B observations from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) satellite (January 2020), outliers were systematically detected and removed, while data gaps were compensated through spline interpolation. Experimental results demonstrate that the proposed method effectively mitigates data discontinuities and anomalous perturbations, achieving a significant improvement in data quality. Furthermore, a 60-order Earth gravity field model derived via the energy balance approach was validated against contemporaneous models published by the University of Texas Center for Space Research (CSR), German Research Centre for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL). The results reveal a two-order-of-magnitude enhancement in inversion precision, with model accuracy improving from 10⁻⁶–10⁻⁷ to 10⁻⁸–10⁻⁹. This method provides a robust solution for enhancing the reliability of gravity field recovery in satellite-based geodetic missions. Full article

Frequent droughts significantly threaten economic development, necessitating effective long-term drought monitoring. The Gravity Recovery and Climate Experiment (GRACE) satellite and its follow-on mission along with Global Navigation Satellite System (GNSS) inversion technologies provide long-term terrestrial water storage signals. However, their limitations in temporal resolution and spatial continuity are inadequate for current requirements. To solve this problem, this study combines a daily terrestrial water storage anomaly (TWSA) reconstruction method with the GNSS inversion technique to explore daily, spatially continuous TWSA in China’s Yellow River Basin (YRB). Furthermore, the Daily Drought Severity Index (DDSI) is employed to analyze drought dynamics in the YRB. Finally, by reconstructing the climate-driven water storage anomalies model, this study explores the influence of climate and human factors on drought. The results indicate the following: (1) The reconstructed daily TWSA product demonstrates superior quality compared to other available products and exhibits a discernible correlation with GNSS-derived daily TWSA data, while REC_TWSA is closer to the GRACE-based TWSA dataset. (2) The DDSI demonstrates superior drought monitoring capabilities compared to conventional drought indices. During the observation period from 2004 to 2021, the DDSI detected the most severe drought event occurring between 30 October 2010 and 10 September 2011. (3) Human activities become the primary driver of drought in the YRB. The high correlation of 0.81 between human-driven water storage anomalies and groundwater storage anomalies suggests that the depletion of TWSA is due to excessive groundwater extraction by humans. This study aims to provide novel evidence and methodologies for understanding drought dynamics and quantifying human factors in the YRB. Full article

The Earth’s time-variable gravity field holds significant research and application value. However, satellite gravimetry missions such as GRACE and GRACE-FO face limitations in spatial resolution when detecting monthly gravity fields, while traditional radar altimeters lack the observational efficiency needed for monthly gravity anomaly inversion. These limitations hinder further exploration and application of the Earth’s time-variable gravity field. Leveraging its advantages, such as rapid global coverage, high revisit frequency, and low cost for constellation formation, spaceborne GNSS-R technology holds the potential to address the observational efficiency gaps of traditional radar altimeters. This study presents the first assessment of the capability of spaceborne GNSS-R interferometric altimetry for high spatial resolution monthly marine gravity anomaly inversion through simulations. The results indicate that under the PARIS Operational scenario of a single GNSS-R satellite (a spaceborne GNSS-R interferometric altimetry scenario proposed by Martin-Neira), a 30′ grid resolution marine gravity anomaly can be inverted with an accuracy of 4.93 mGal using one month of simulated data. For a dual-satellite constellation, the grid resolution improves to 20′, achieving an accuracy of 4.82 mGal. These findings underscore the promise of spaceborne GNSS-R interferometric altimetry technology for high spatial resolution monthly marine gravity anomaly inversion. Full article

Iraq and other semi-arid regions are facing severe climate change impacts, including increased temperatures and decreased rainfall. Changes to climate variables have posed a significant challenge to groundwater storage dynamics. In this regard, the Gravity Recovery and Climate Experiment (GRACE) mission permits novel originate groundwater storage variations. This study used the monthly GRACE satellite data for 2002–2023 to determine variations in groundwater storage (GWS). Changes in GWS were implied by extracting soil moisture, acquired from the Global Land Data Assimilation System (GLDAS), from the extracted Territorial Water Storage (TWS). The results demonstrated that an annual average ΔGWS trend ranged for the Goddard Space Flight Center (GSFC) mascon and Jet Propulsion Laboratory (JPL) mascon was from 0.94 to −1.14 cm/yr and 1.64 to −1.36 cm/yr, respectively. Also, the GSFC illustrated superior performance in estimating ΔGWS compared with the JPL in Iraq, achieving the lowest root mean square error at 0.28 mm and 0.60 mm and the highest coefficient of determination (R²) at 0.92 and 0.89, respectively. These data are critical for identifying areas of depletion, especially in areas where in situ data are lacking. These data allows us to fill the knowledge gaps; provide critical scientific information for monitoring and managing dynamic variations. Full article

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R²) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made. Full article

Growing human demands are placing significant pressure on groundwater resources, causing declines in many regions. Identifying areas where groundwater levels are declining due to human activities is essential for effective resource management. This study investigates the influence of land use and land cover, crop types, and precipitation patterns on groundwater level trends across the Mississippi River Watershed (MRW), USA. Groundwater storage changes from 2003 to 2015 were estimated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. A spatiotemporal analysis was conducted at four scales: the entire MRW, groundwater regimes based on groundwater level change rates, 31 states within the MRW, and six USGS hydrologic unit code (HUC)-2 watersheds. The results indicate that the Lower Mississippi region experienced the fastest groundwater decline, with a Sen’s slope of −0.07 cm/year for the mean equivalent water thickness, which was attributed to intensive groundwater-based soybean farming. By comparing groundwater levels with changes in land use, crop types, and precipitation, trends driven by human activities were identified. This work underscores the ongoing relevance of GRACE data and the GRACE Follow-On mission, launched in 2018, which continues to provide vital data for monitoring groundwater storage. These insights are critical for managing groundwater resources and mitigating human impacts on the environment. Full article

Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by hydrological and meteorological data is proposed. Considering these driving data usually cannot fully capture the trend changes of the TWSA time series, we propose first to use the BEAST algorithm to perform piecewise linear detrending for the TWSA series and then fill the gap of the detrended series using the GMDH algorithm. The complete gap-filling TWSAs can be readily obtained after adding back the previously removed piecewise trend. By comparing the simulated gap filled by BEAST + GMDH using Multiple Linear Regression and Singular Spectrum Analysis with reference values, the results show that the BEAST + GMDH scheme is superior to the latter two in terms of the correlation coefficient, Nash-efficiency coefficient, and root-mean-square error. The real GRACE/GFO gap filled by BEAST + GMDH is consistent with those from hydrological models, Swarm TWSAs, and other literature regarding spatial distribution patterns. The correlation coefficients there between are, respectively, above 0.90, 0.80, and 0.90 in most of the global river basins. Full article