Journal of Geodesy最新文献

A new generation of space-borne LiDAR (Light Detection And Ranging) satellite ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) equipped with ATLAS (Advanced Topographic Laser Altimeter System) can perform earth observation. The main problem is to remove the noise photons from the data. The study proposes a main direction-based noise removal algorithm based on three sets of photon-counting LiDAR data. In order to extract the main direction, features in the spatial neighborhood (k) of photons are calculated, most of the initial noise is removed according to the angle between the main direction of photons and the along-track distance direction. Qualitative and quantitative evaluations are employed to validate the proposed algorithm. The obtained results and the performed analysis reveal that the proposed algorithm can process day and night data with different signal-to-noise ratios, while the accuracy of various surface types exceeds 96%. More specifically, the accuracy of the proposed algorithm for night data can reach 97.43%. Based on quantitative evaluations using SPL (Single photon LiDAR), MATLAS, and airborne LiDAR data, the average R, P, and F values are 0.951, 0.959, and 0.954, respectively. Meanwhile, the result of the proposed algorithm is compatible with the ATL03 photons with low, medium, and high confidence, and its accuracy is superior to ATL08 products. The proposed algorithm had fewer parameters and significantly outperformed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the improved local statistical distance algorithm. This algorithm is expected to provide a reference for subsequent photon-counting LiDAR data processing.

While the theory of random isotropic scalar fields on the sphere is well established, it has not been fully extended to the case of vector fields yet. In this contribution, several theoretical results are thus generalized to random isotropic vector fields on the sphere, including an equivalent of the Wiener–Khinchin theorem, which relates the distance-dependent covariance of the field’s components in a particular rotationally invariant basis to the covariance of the vector spherical harmonic coefficients of the field, i.e., its angular power spectrum. A parametric model, based on a stochastic partial differential equation, is proposed to represent the spatial covariance and angular power spectrum of such fields. Such a model is adjusted, with minor modifications, to empirical spatial correlations of the white noise and flicker noise components of 3D displacement time series of ground global navigation satellite system (GNSS) tracking stations. The obtained spatial correlation model may find several applications such as enhanced detection of offsets in GNSS station position time series, enhanced estimation of long-term ground deformation (i.e., station velocities), enhanced isolation of station-specific displacements (i.e., spatial filtering) and more realistic assessment of uncertainties in all GNSS network-based applications (e.g., estimation of crustal strain rates, of glacial isostatic adjustment models or of tectonic plate motion models).

Precise knowledge of geocenter motion, i.e., the relative motion between the Earth’s center of mass (CM) and the center of figure of the Earth’s surface (CF), is crucial to high-stakes geodetic applications such as sea-level rise monitoring with satellite altimetry or the establishment of regional and global mass budgets with satellite gravimetry. The computation of the latest release of the International Terrestrial Reference Frame, ITRF2020, involved the estimation of a field of seasonal motions for a global network of geodetic stations, expressed with respect to CM, as sensed by satellite laser ranging, from which the translational part represents seasonal geocenter motion. This paper presents two different methods to isolate seasonal geocenter motion from the field of ITRF2020 seasonal station motions, among which a new method based on a direct weighted average of seasonal station motions, with station-specific weights chosen so as to provide a better approximation of CF than the standard network shift approach. The ITRF2020 annual geocenter motion model thus obtained is then compared with other recent geodetic and geophysical estimates. Although different sub-groups of estimates with relatively good internal consistency may be identified, the overall scatter of recent geodetic estimates remains at the level of several mm, i.e., close to the amplitude of annual geocenter motion itself. Efforts toward reconciling seasonal geocenter motion estimates therefore still appear necessary. Meanwhile, it would seem safe to assume that seasonal geocenter motion models, in particular those currently used in satellite altimetry and satellite gravimetry, are still uncertain.

Spherical geodetic satellites tracked by satellite laser ranging (SLR) stations provide indispensable scientific products that cannot be replaced by other sources. For studying the time-variable gravity field, two low-degree coefficients C₂₀ and C₃₀ derived from GRACE and GRACE Follow-On missions are replaced by the values derived from SLR tracking of geodetic satellites, such as LAGEOS-1/2, LARES-1/2, Starlette, Stella, and Ajisai. The subset of these satellites is used to derive the geocenter motion which is fundamental in the realization of the origin of the terrestrial reference frames. LAGEOS satellites provide the most accurate standard gravitational product GM of the Earth. In this study, we use the Kaula theorem of gravitational perturbations to find the best possible satellite height, inclination, and eccentricity for a future geodetic satellite to maximize orbit sensitivity in terms of the recovery of low-degree gravity field coefficients, geocenter, and GM. We also derive the common station-satellite visibility-coverability coefficient as a function of the inclination angle and satellite height. We found that the best inclination for a future geodetic satellite is 35°–45° or 135°–145° with a height of about 1500–1700 km to support future GRACE/MAGIC missions with C₂₀ and C₃₀. For a better geocenter recovery and derivation of the standard gravitational product, the preferable height is 2300–3500 km. Unfortunately, none of the existing geodetic satellites has the optimum height and inclination angle for deriving GM, geocenter, and C₂₀ because there are no spherical geodetic satellites at the heights between 1500 (Ajisai and LARES-1) and 5800 km (LAGEOS-1/2, LARES-2).

In this paper, we analyze the general errors-in-variables (EIV) model, allowing both the uncertain coefficient matrix and the dispersion matrix to be rank-deficient. We derive the weighted total least-squares (WTLS) solution in the general case and find that with the model consistency condition: (1) If the coefficient matrix is of full column rank, the parameter vector and the residual vector can be uniquely determined independently of the singularity of the dispersion matrix, which naturally extends the Neitzel/Schaffrin rank condition (NSC) in previous work. (2) In the rank-deficient case, the estimable functions and the residual vector can be uniquely determined. As a result, a unified approach for WTLS is provided by using generalized inverse matrices (g-inverses) as a principal tool. This method is unified because it fully considers the generality of the model setup, such as singularity of the dispersion matrix and multicollinearity of the coefficient matrix. It is flexible because it does not require to distinguish different cases before the adjustment. We analyze two examples, including the adjustment of the translation elimination model, where the centralized coordinates for the symmetric transformation are applied, and the unified adjustment, where the higher-dimensional transformation model is explicitly compatible with the lower-dimensional transformation problem.

The resolution and above all the stability of the geodetic reference frames is crucially important when global change, such as the sea level rise is observed. In this context systematic errors are still presenting a significant challenge to the measurement techniques of space geodesy. In order to overcome this unfortunate situation for the satellite laser ranging technique, we have utilized the injection of a mode-locked laser to provide a stable low-noise link between the optical domain, where the measurements are carried out, and the microwave regime in which the station clock is defined. We obtained a considerably enhanced measurement delay stability by 10–20 ps over several days, albeit with some experimental challenges. The implementation of waveform scans required us to revisit the issue of target structure and intensity variation in satellite laser ranging.

To support real-time global navigation satellite systems (GNSS) precise applications, satellite clock corrections need to be precisely estimated at a high-rate update interval, which remains a challenge due to the rapid development of multi-GNSS constellations. In this study, we developed an undifferenced (UD) ambiguity resolution (AR) procedure to improve both the accuracy and computational efficiency for real-time multi-GNSS clock estimation realized by a square root information filter. In the proposed method, UD ambiguities are resolved after correcting the simultaneously estimated uncalibrated phase delays (UPD) and the fixed UD ambiguity parameters are eliminated immediately from the filter, so that the computational burden is significantly reduced. Moreover, based on the linear relationship between double-differenced (DD) and UD ambiguities, we investigated the difference between DD and UD AR in clock estimation. We found that the major reason why DD AR contributes little to the clock estimation while UD AR can speed up the convergence remarkably is that UD AR additionally provides a stable clock datum compared with DD AR. GNSS observations from about 100 globally distributed stations were processed with the proposed method to generate simulated real-time clocks and UPDs for GPS, Galileo, and BDS satellites over a one-month period. The results show that the percentage of wide-lane (WL) UPD residuals within ± 0.25 cycles and narrow-lane (NL) UPD residuals within ± 0.15 cycles are over 97.0% and 90.0%, respectively, which contributes to an ambiguity fixing rate of more than 90% for three systems. The mean daily standard deviation (STD) of the clocks of the UD-fixed solution with respect to Center for Orbit Determination in Europe 30 s final products is 0.021, 0.020, and 0.035 ns for GPS, Galileo, and BDS satellite, respectively, which is improved by 78.1%, 58.3%, and 79.8% compared to the float solution. Benefiting from the removal of fixed ambiguities, the average computation time per epoch was reduced from 3.88 to 1.05 s with a remarkable improvement of 72.9%. The quality of the satellite clock and UPD products was also evaluated by the performance of kinematic precise point positioning (PPP). The results show that fast and reliable multi-GNSS PPP-AR can be achieved with the derived UD-fixed clocks and UPDs, which outperforms that using DD-fixed clock and off-line UPD products with an average improvement of 7.9% and 19.9% in terms of convergence time and positioning accuracy, respectively. Furthermore, we demonstrated the effectiveness of the proposed UD AR method through a 7-day real-time clock estimation experiment.

Accurate orientation of geodetic instruments is fundamental for understanding deformation processes within the Earth's interior. Misalignment can lead to significant errors in data interpretation, affecting various geophysical applications. However, accurate alignment of standalone instruments like seismometers, strainmeters and tiltmeters remains a challenge in field geodesy. While numerous seismic-wave-based orientation methods have been successfully applied to seismometers, they are often inapplicable to tiltmeters due to their high-frequency filtering behavior and the requirement for a neighboring, pre-oriented instrument. In response to these challenges, we propose a novel orientation calibration method for borehole tiltmeters based on maximizing the correlation between recorded tilt data and theoretical tides by adjusting azimuthal angles. Our study encompasses two kinds of borehole tiltmeters and four datasets from three different field sites. Using solid and ocean tides modeling together with local topography and cavity disturbances, we obtain coefficient correlations ranging between 0.831 and 0.963, and 95% confidence intervals of azimuthal angles below 3.3°. The correlation-based method demonstrates robustness across various tidal-signal extraction techniques, including different averaging window sizes and band-pass filters. Moreover, it yields azimuthal results in agreement with direct compass measurements for known orientations, while exhibiting a moderate sensitivity to factors such as ocean tides and site-specific topography for the studied cases. This method appears to be advantageous when direct measurements are either unavailable or challenging, and emerges as an accurate tool for determining borehole tiltmeter orientation. Its potential applicability may extend beyond tiltmeters to other instruments that can also record tidal phenomena, such as strainmeters and broadband seismometers. Additionally, its utility could be extended to environments like the seafloor, in order to refine the precision of azimuthal angle estimation and simplify methods for azimuthal angle determination.

The combination of satellite laser ranging (SLR) observations to various low earth orbit (LEO) satellites can enhance the accuracy and robustness of SLR-derived geodetic parameters, benefiting the realization of the International terrestrial reference frames. Observation stochastic models play a critical role in the integrated processing of SLR observations to multiple LEO satellites. The consideration of precision in heterogeneous SLR observations from various satellites is essential. In this study, we aim to improve the combination of multi-LEO SLR observations for geodetic parameters determination by optimizing the stochastic model using variance component estimation (VCE). We perform weekly estimates of the geodetic parameters, including station coordinates, Earth rotation parameters, and geocenter coordinates (GCC), using three years of SLR observations to seven LEO satellites at different orbits. The satellite-dependent, station-dependent, and satellite–station-dependent variance components are separately estimated through VCE processing to refine the stochastic models. Given the fact that the precision of SLR observations significantly differs in satellites and stations, the multiple LEO combination can be significantly improved with the implementation of VCE. Satellite–station-pair-dependent variance components are more suitable to the SLR VCE and the accuracy of station coordinates, pole coordinates, and length of day can be averagely improved by 8.4, 22.6, and 21.9%, respectively, compared to the equal-weight solution. Our result also indicates that the observation insufficiency for some stations may result in an unreliable VCE estimation, and eventually leads to an accuracy degradation for station coordinates. To overcome this deficiency, we adopt the variance components derived from the monthly solutions to build the stochastic model in the weekly solutions. The application of monthly weights can effectively mitigate the accuracy deterioration of station coordinates, improving the repeatability of the station coordinates by 15.9, 14.6, and 9.2% with respect to the equal-weight solution in E, N, and U components. The global geodetic parameters also benefit from this processing. The import of monthly weight decreases the outliers in the GCC series, especially in the X and Y components.