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With the introduction of the operating system Android 7 Nougat in the year 2016, it became possible to access Global Navigation Satellite System (GNSS) code and carrier phase observations. These observations can be processed with the state-of-the-art GNSS processing software packages, which allows an in-depth evaluation of the smartphone's GNSS performance. The availability of carrier phase observations enables sub-decimeter-level positioning. A few years ago, smartphones wearing dual-frequency GNSS chipsets hit the mass market. In this study, we investigate the capability of such a device for the estimation of tropospheric delays. Static measurements carried out over the period of two weeks are performed using a Google Pixel 4 XL smartphone. The measurements are processed using relative positioning methods with a baseline length of about 33 kilometers, where a continuously operating reference station (CORS) acts as a base. The estimated differential zenith tropospheric wet delay (dZWD), obtained for the smartphone are then combined with absolute values computed at the reference station, in order to obtain time series of Zenith Total Delay (ZTD). Using this method, we demonstrate that high-precision ZTDs can be successfully determined from smartphone GNSS observations. When comparing the estimated tropospheric delays with those determined at a nearby geodetic receiver to assess the accuracy of the acquired time series of ZTD, differences in the range of few millimeters to a centimeter are visible. We examine the impact of various error sources, such as antenna phase center variations and residual effects of the ionosphere. Given that the obtained accuracies are at the level of a centimeter and below, the suggested method shows the potential to resolve small-scale tropospheric structures in near real-time, and thus, could be an interesting data source for numerical weather prediction models or related GNSS crowdsourcing projects., Measurement Science and Technology, 34 (9), ISSN:0957-0233, ISSN:1361-6501

The goal of the project “Co-location of Space Geodetic Techniques on Ground and in Space”, in the DFG funded research unit on reference systems and founded by the Swiss National Foundation (SNF), is the improvement of existing and the establishment of new ties between the space geodetic techniques, together with the assessment and reduction of technique-specific biases. To achieve this, the wealth of co-located instruments at the Geodetic Observatory Wettzell (Germany) are used, where systematic errors in the space geodetic techniques can be detected, assessed and removed on very short, well-known baselines. Within this paper we summarise results for three intra-technique co-location experiments in Wettzell. Firstly, an assessment of the GNSS to GNSS baselines in relation to the surveyed local ties shows discrepancies of up to 9 mm, for solutions based on the ionosphere-free linear combination. Secondly, an analysis of the short VLBI baseline shows that the use of a clock tie achieves a sub-mm agreement with respect to the local tie. And finally, initial results on the usage of differencing approaches on the short SLR baseline show that double-difference residuals are within ±4 mm. The results of this work show the potential of intra-technique studies on short baselines for the understanding of technique-specific biases and errors and for the monitoring of local ties., International Association of Geodesy Symposia, 152, ISSN:0939-9585, ISSN:2197-9359, Beyond 100: The Next Century in Geodesy, ISBN:978-3-031-09856-7, ISBN:978-3-031-09857-4

Global Navigation Satellite System (GNSS) is a well-recognized observation technique in studies on the ionosphere due to its sensitivity to the total electron content (TEC). The era of modern smartphones, running on Android version 7.0 and higher, facilitates the acquisition of raw dual-frequency GNSS measurements, paving the way for the GNSS community data to be potentially exploited in geoscience applications. One can assume that the continuous progress in this domain may result in future in a performance of those smart devices reaching the level of GNSS receivers (and antennas) used for atmospheric monitoring. The prospective utilization of a very large number of GNSS-capable smartphones, as a dynamic crowdsourcing receiver network, could form thus an attractive source of complementary GNSS data, allowing to significantly increase the spatial resolution of observations available for the analysis and cover areas of the globe where GNSS receivers are not yet present. The enormous volume of prospective GNSS community data brings, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest, also in near-real time. The same applies to the analysis of such huge and heterogeneous data sets, requiring a dedicated approach in order to exploit the data in a thorough manner and fully benefit from such a concept. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) is an ongoing ESA NAVISP project with activities covering acquisition of GNSS observations from modern smartphones and development of the dedicated infrastructure regarding GNSS processing and machine learning at scale. An Android application, developed within that project, is utilized to retrieve code and phase observations from the modern generation of smartphones. The acquired user-specific data is available to the user in the form of RINEX3-compliant files and can be uploaded by the user to the central server for subsequent processing. This contribution highlights the CAMALIOT project in relation to the ionosphere and provides information on the developed Android application, data ingestion and processing, complemented with methodology and initial results related to the TEC retrieval based on smartphone data collected in the vicinity of geodetic GNSS receivers, with the latter used for deriving reference time series. Concerning the smartphone data, the amount and quality of observations are much lower compared to the high-grade GNSS equipment and a dedicated pre-processing stage is needed in order to discard bad observations in a proper manner. An apparent correlation between the data quality, utilized frequency bands and satellite constellation involved is visible too. This area of GNSS still suffers from the limitations related mainly to the components comprising the smartphone, resulting in the lower quality of the acquired GNSS observations, compared to those obtained with the use of high-grade GNSS receivers and antennas. This translates to a greater susceptibility to multipath as well as a much more frequent occurrence of observation gaps and cycle slips, affecting the data availability and continuity of the carrier-phase measurements., EGUsphere

Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites propagate through the atmosphere before being received on Earth. Thereby, the signal is delayed and tropospheric parameters can be estimated. The good global coverage of GNSS receivers, combined with the high temporal resolution and the high accuracy, make GNSS a suitable tool for studies on the atmosphere.Atmospheric delays are differentiated into a zenith hydrostatic (ZHD) and a non-hydrostatic, or zenith wet delay (ZWD). The hydrostatic part has a larger contribution (causing a delay of roughly 2.4 meters in the zenith direction) but can be modeled with sufficient accuracy using analytical methods. The ZWD has a smaller contribution (causing a delay between 0 to 40 centimeters) and depends mainly on the water vapour content in the atmosphere. However, due to the variable nature of water vapour, the ZWD is difficult to model and is therefore typically estimated. Its quantification is essential since it drives weather systems and climate change to a great extent. For many applications, such as weather forecasting or positioning using low-cost GNSS receivers such as smartphones, global real-time monitoring or even predictions of ZWD would be required and be beneficial.In the last decade, machine learning (ML) algorithms have gained a lot of interest and are successfully utilized in many different fields. Thereby, ML algorithms have proven to be able to efficiently process and combine large amounts of data and solve problems of various kinds.This motivated us to investigate the feasibility of ML algorithms for the prediction of tropospheric parameters, in particular ZWD, with the help of meteorological data such as the water vapour content. The work aims to develop a global model capable of predicting ZWD in space and time. Therefore, different ML algorithms are used to train a model based on meteorological features. The performance of the utilized algorithms is evaluated based on commonly used performance metrics, such as Root Mean Squared Error (RMSE) and R².Preliminary investigations are carried out utilizing 3000 GNSS stations distributed over Europe. The performance of various ML methods, such as Linear Regression methods, Random Forest, (Extreme) Gradient Boosting, and Multilayer Perceptron is compared. Furthermore, different feature combinations, as well as training sample sizes are investigated. It is revealed that linear methods are not able to properly reflect the observations. Instead, our Random Forest approach provides, so far, the highest model accuracy with an RMSE of 1.7 centimeters and an R² value of 0.88.

Precise orbit determination is vital for the increasingly vast number of space objects around the Earth. Moreover, accurate orbit prediction of GNSS satellites is essential for many real-time geodetic applications, including real-time navigation. The typical way to obtain accurate orbit predictions is using physics-based orbit propagators. However, the prediction errors accumulate with time because of insufficient modeling of the changing perturbing forces. Motivated by the rapid expansion of computing power and the considerable data volume of satellite orbits available in recent years, we can apply machine learning (ML) and deep learning (DL) algorithms to assess if they can be used to further reduce orbit errors. In this study, we focus on the orbit prediction of GNSS constellations. We investigate the potential of using different ML and DL algorithms for improving the accuracy of the ultra-rapid products from IGS. As ground truth we consider the IGS final products, and the differences between the ultra-rapid and final products are computed and serve as targets for the ML/DL methods. In this context, we combine the advantages of physics-based and data-driven ML/DL methods. Since the major errors of GNSS orbits are expected to be caused by the deficiency of solar radiation pressure models, we consider different related parameters as additional features to implicitly model the solar impact, such as the C0,0 terms of global ionosphere maps. In order to accurately model the effect of solar radiation pressure on the radial, along-track and cross-track components of the satellite orbit system, the geometric relation between the Sun, the satellite and the Earth are also considered. Furthermore, the performances of different ML/DL algorithms are compared and discussed. Due to the temporal characteristics of the problem, certain sequential modeling algorithms, such as Long Short-Term Memory and Gated Recurrent Unit, show superiority. Our approach shows promising results with average improvements of over 40% in 3D RMS within the 24-hours prediction interval of the ultra-rapid products., EGUsphere

With test satellites already in space, the Swiss company Astrocast is currently in the process of establishing a constellation of about 80 nanosatellites for commercial purposes that are operating in a low Earth orbit (LEO). As a result of the collaboration with ETH Zürich, such satellites will be equipped with both low-cost multi-GNSS dual-frequency receivers and a small array of laser retroreflectors for satellite laser ranging (SLR). In the future, this set of geodetic instruments could be also extended with a simple, compact and low-power transmitter compliant with the next-generation very long baseline interferometry (VLBI) system, known as the VLBI Global Observing System (VGOS). Therefore, apart from scientific studies based on such state-of-the-art multi-GNSS receivers in space, the Astrocast nanosatellite network could also be examined in terms of satellite co-locations. In this case, the new geometrical connections in space could be realized together with all ground-based instruments that can observe the co-location satellites. Assuming sufficient precision of such observations and good knowledge of the spacecraft environment, this approach could result in an enhanced quantity of tie measurements at a high spatio-temporal resolution, potentially leading also to an enhanced quality of common geodetic parameters. However, accurate orbit determination is of high importance, whenever considering potential co-location in space or, in general, estimating various global parameters of geophysical interest. In this contribution, we focus on precise orbit determination (POD) of LEO Astrocast-type nanosatellites based on global SLR-only, VGOS-only as well as combined SLR-VGOS observations. The impact of this concept on various geodetic parameters and the derived orbits is studied on the basis of Monte-Carlo simulations carried out with the c5++ analysis software. All simulated data are combined on the observation level and used to derive satellite orbits and to estimate both, station-based and global geodetic parameters. Our study is based on VGOS-type schedules created in VieSched++ and consisting of both quasar and satellite observations. In addition to the simulated laser measurements to Astrocast satellites, the SLR-related solutions include also global observations to LAGEOS-1/2 satellites. Our considerations involve solutions with different time intervals, satellite observation precision levels and quantity of the considered cube satellites, providing thus initial insights concerning prospective utilization of LEO cube satellite technology for space geodesy in the era of the Global Geodetic Observing System., EGUsphere

High-precision GNSS (Global Navigation Satellite System) positioning can reach an accuracy at the millimeter level, and by collecting these data over years, very small ground movements can be resolved. These data can be used to study geodynamics, like continental drifts, tidal- and non-tidal loading effects, or earthquakes, for example. Here we focus on the sensitivity of GNSS for resolving these different types of movements. We derive minimal detectable displacements for linear drift rates, annual- and semiannual periodic motions, and offsets – these are main parameters of the so-called standard linear trajectory model for GNSS station motions. For our analysis, our data comes from several hundreds of permanent GNSS stations across Europe– the GNSS stations’ coordinates are obtained at a daily sampling rate, with almost 25 years of data available for some stations. Based on cleaned residual GNSS time series we calibrate a “power-law plus white noise” stochastic model for each station. Together with the functional trajectory model we compute the formal errors of the movement parameters based on a least-squares adjustment. Based on these errors we then introduce the statistics to derive minimum detectable displacements for the movement parameters. Our analysis shows that the minimum detectable trends can be as low as few tenth of millimeters per year. The minimum detectable amplitudes at the annual and semiannual periods are at the millimeter level or lower, and the detectable offset is few millimeters on average, too. As expected, the minimum detectable displacements depend strongly on the length of the datasets and on the noise characteristics. Another important parameter is the number of discontinuities and offsets for each station – it impacts the minimum detectable trend. We conclude that such an analysis can be very useful for sensitivity studies in climate change monitoring. Furthermore, the methodology cannot only be applied in the field of GNSS time series analysis, but also to any other time series data in geosciences., EGUsphere

Global Navigation Satellite Systems (GNSS) have been used as a key technology for satellite orbit determination for about 30 years. With the increasing popularity of miniaturized satellites (e.g., CubeSats that are nanosatellites based on standardized 10 cm-sized units) the need for an adapted payload for orbit determination arises. We developed a small-size versatile GNSS payload board using commercial off-the-shelf single-frequency GNSS receivers with extremely small weight (1.6 g), size (12.2 x 16.0 x 2.4 mm3 ) and power consumption (100 mW). The board features two separate antenna connectors and four GNSS receivers – two connected to each antenna. This redundancy lowers the risk of a total payload failure in case one receiver is malfunctioning. Two prototypes of the GNSS positioning board have been successfully launched onboard the Astrocast-01 and -02 3-unit cube satellites with altitudes of 575 and 505 km, respectively. The multiGNSS receivers are capable of tracking the GNSS satellites of the four major systems, i.e., GPS, GLONASS, BeiDou and Galileo. In addition, both satellites are equipped with a small array of three laser retroreflectors enabling orbit validation with Satellite Laser Ranging (SLR). After the two precursor missions, a constellation of 80 satellites is planned, allowing the formation and computation of a highly uniform polyhedron in space with cm-accuracy, relevant for geocenter, reference frame, and GNSS orbit determination. At present, we have continuous receiver PVT solutions available. The real-time onboard orbit determination results indicate that the receivers perform very well on both satellites. The RMS of a daily orbit fitting is, after removing one or the other outlier, at the level of 2-5 meters despite errors caused by the ionosphere and the orbit model. For a few satellite arcs, the recording of GNSS raw phase and code data was enabled, allowing orbit determination in a post-processing mode. This allows a better assessment of the achievable orbit quality and an overall performance estimation. The tests performed so far include the improvement of the orbit quality by eliminating the ionospheric refraction based on a linear combination of phase and code observations, the comparison of various single-system solutions and advances in combining the different tracking systems for orbit determination. In collaboration with the Zimmerwald Observatory in Switzerland a first SLR campaign was conducted that successfully tracked both nanosatellites. The SLR measurements with their high accuracy were then analyzed to validate the orbits of the Astrocast satellites derived from GNSS measurements. We will present details on the payload board, on the results of the orbit determination in real-time and in post-processing mode based on the low-cost single-frequency multi-GNSS receivers onboard the satellites and on the SLR orbit validation., EGUsphere

In this study, a multiple algorithm approach to the analysis of GNSS coordinate time series for detecting geohazards and anomalies is proposed. This multiple algorithm approach includes the novel use of spatial and temporal analyses. In the spatial analysis algorithm, the spatial autoregressive model was used, assuming that the GNSS coordinate time series from a network of stations are spatially dependent. Whereas in the temporal analysis algorithm, it is assumed that the GNSS coordinate time series of a single station is temporally dependent and an artificial neural network is used to extract this dependency as a nonparametric model. This multiple algorithm approach was examined using (i) the BIGF network of GNSS stations in the British Isles and (ii) the GNSS stations of the GEONET network in Japan for the Tohoku‐Oki 2011 Mw9.0 earthquake. It was demonstrated in these case studies that this multiple algorithm approach can be used to detect the effect of a geohazard such as an earthquake on the GNSS network coordinate time series and to detect regional anomalies in the GNSS coordinate time series of a network. The spatial analysis algorithm seemed to be more suitable to detect coordinate offsets in the low‐frequency component and/or variations in the long‐term trends of the GNSS coordinate time series, while it is less reliable in detecting sudden large magnitude coordinate offsets due to earthquakes, as the effects at one station propagate to nearby stations. In contrast, the temporal analysis algorithm detects coordinate offsets in the high‐frequency component which makes it effective in detecting sudden large coordinate offsets in the GNSS coordinate time series such as those due to earthquakes. Thus, it was shown the complementary of the temporal and spatial analysis algorithms and their successful application for the magnitude and frequency content of the anomalies in the two case studies., Journal of Geophysical Research: Solid Earth, ISSN:2169-9313, ISSN:0148-0227, ISSN:2169-9356

Battelier, B., et al., We present the scientific motivation for future space tests of the equivalence principle, and in particular the universality of free fall, at the 10 level or better. Two possible mission scenarios, one based on quantum technologies, the other on electrostatic accelerometers, that could reach that goal are briefly discussed. This publication is a White Paper written in the context of the Voyage 2050 ESA Call for White Papers., Open Access funding enabled and organized by Projekt DEAL.

Monitoring surface deformation in real-time help at planning and protecting infrastructures and populations, manage sensitive production (i.e. SEVESO-type) and mitigate long-term consequences of modifications implemented. We present RT-SHAKE, an algorithm developed to detect ground motions associated with landslides, sub-surface collapses, subsidences, earthquakes or rock falls. RT-SHAKE detects first transient changes in individual GPS time series before investigating for spatial correlation(s) of observations made at neighbouring GPS sites and eventually issue a motion warning. In order to assess our algorithm on fast (seconds to minute), large (from 1cm to meters) and spatially consistent surface motions, we use the 1Hz GEONET GNSS network data of the Tohoku-Oki MW9.0 2011 as a test scenario. We show the delay of detection of seismic wave arrival by GPS records is of ~10 seconds with respect to an identical analysis based on strong-motion data and this time delay depends on the level of the time-variable noise. Nevertheless, based on the analysis of the GPS network noise level and ground motion stochastic model, we show that RT-SHAKE can narrow the range of earthquake magnitude, by setting a lower threshold of detected earthquakes to MW6.5-7, if associated with a real-time automatic earthquake location system., Geophysical Journal International, 214 (2), ISSN:0956-540X, ISSN:1365-246X

Journal of Geodesy, 89 (7), ISSN:0949-7714, ISSN:1432-1394

Geophysical Journal International, 199, ISSN:0956-540X, ISSN:1365-246X

Journal of Geodesy, 87 (5), ISSN:0949-7714, ISSN:1432-1394

Journal of Geodesy, 87 (6), ISSN:0949-7714, ISSN:1432-1394

The tools of geodesy have the potential to transform the Ocean Observing System. Geodetic observations are unique in the way that these methods produce accurate, quantitative, and integrated observations of gravity, ocean circulation, sea surface height, ocean bottom pressure, and mass exchanges among the ocean, cryosphere, and land. These observations have made fundamental contributions to the monitoring and understanding of physical ocean processes. In particular, geodesy is the fundamental science to enable determination of an accurate geoid model, allowing estimate of absolute surface geostrophic currents, which are necessary to quantify ocean’s heat transport. The present geodetic satellites can measure sea level, its mass component and their changes, both of which are vital for understanding global climate change. Continuation of current satellite missions and the development of new geodetic technologies can be expected to further support accurate monitoring of the ocean. The Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) provides the means for integrating the geodetic techniques that monitor the Earth's time-variable surface geometry (including ocean, hydrologic, land, and ice surfaces), gravity field, and Earth rotation/orientation into a consistent system for measuring ocean surface topography, ocean currents, ocean mass and volume changes. This system depends on both globally coordinated ground-based networks of tracking stations as well as an uninterrupted series of satellite missions. GGOS works with the Group on Earth Observations (GEO), the Committee on Earth Observation Satellites (CEOS) and space agencies to ensure the availability of the necessary expertise and infrastructure. In this white paper, we summarize the community consensus of critical oceanographic observables currently enabled by geodetic systems, and the requirements to continue such measurements. Achieving this potential will depend on merging the remote sensing techniques with in situ measurements of key variables as an integral part of the Ocean Observing System.

The complexity of the Earth system has been discussed in many fundamental documents in recent years. Trying to understand the Earth system and improve our forecast capability step by step are the great challanges of the Earth system science. It is evident that only a joint effort of many scientific disciplines will make it possible to shed more light into the complexity of the Earth system. Despite enormous progress in recent years the uncertainties of predictions are still rather high. A recent analysis of the deeper reason for the limited quality of climate forecasts led to the unanimous cocnlusion that "data are still poor". Data from space will and must play a central role in Earth system research.