Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn. As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.

Integrated Water Vapor (IWV) is crucial in environmental research, offering insights into atmospheric dynamics. Direct IWV measurement is challenging, necessitating alternative estimation technologies. Existing methods including Global Navigation Satellite System (GNSS), radiosondes, water vapor radiometers (WVR), satellite remote sensing, and numerical weather models (NWM), have specific limitations. GNSS and WVR provide high precision and temporal resolution (e.g., 5 min) but are limited to specific locations. Radiosondes, while accurate, have sparse spatial distribution and low temporal resolution (e.g., twice daily). Satellite remote sensing offers broad spatial resolution but lower temporal resolution (hours to days) and reduced accuracy under cloudy conditions and due to satellite tracks. NWMs provide global hourly products but their accuracy depends on meteorological data and model precision. This study introduces a regional IWV predictive model using Machine Learning to address these challenges. Utilizing IWV data from GNSS stations, the study develops a predictive model based on least squares support vector machine, which autonomously determines optimal parameters to enhance performance. The model enables accurate IWV estimation at any location within a region, using inputs such as latitude, longitude, altitude, and temperature, achieving an average root mean square error of 0.95 mm. The model’s performance varies across seasons and terrains, showing adaptability to diverse conditions. The model’s reliability is validated by comparing its predictions with the conventional ERA5 IWV method, showing a 61% improvement rate. This refined IWV estimation model is applied for regional climate analysis, demonstrating its practical utility in environmental research, specifically for the Upper Rhine Graben Region.

In spaceborne microwave remote sensing and geodesy, tropospheric delay has emerged as a critical factor affecting the precision of measurements. While the Global Navigation Satellite System (GNSS) offers reliable station-wise Zenith Total Delay (ZTD) products, their spatial resolution is inherently constrained by the GNSS stations distribution. Conversely, empirical models combined with numerical weather models (NWM), such as the fifth generation of European Reanalysis (ERA5) and Vienna Mapping Functions 3 (VMF3), can generate global gridded ZTD estimates. Yet, they exhibit centimeter-level discrepancies when benchmarked against GNSS-derived ZTD. This paper proposes a deep learning method based on the Gaussian Mixture Long Short-Term Memory (GM-LSTM) network, which learns the mapping of probability density between ZTD derived from the empirical model to the ZTD derived from GNSS. Once this mapping is learned, it can be used to infer the ZTD probability distribution and its uncertainty at any location within the study area. Upon evaluation across eight different latitude regions in Europe, the ZTD inferred by the proposed GM-LSTM model reaches the state-of-the-art level with an average Root Mean Square Error (RMSE) of 4.6mm. Compared with ZTD estimated from Deep Neural Network (DNN), ERA5 ray tracing, VMF3, and the Generic Atmospheric Correction Online Service (GACOS), the proposed GM-LSTM model achieved average performance improvements of 41.78%, 68.20%, 49.56%, and 50.43%, respectively. Verified by the meteorological records, the proposed GM-LSTM model can effectively reflect the uncertainty caused by spatially heterogeneous rainfall events. With homogeneous training data, it shows good performance in heavy rainfall, which is not matched by other ZTD estimation methods.

In the framework of the activities of the IAG Sub-Commission on the gravity and geoid in Africa, a recent set of gravity databases has been established. They are namely: AFRGDB_V2.0 and AFRGDB_V2.2. The AFRGDB_V2.0 has been created using the window remove-restore technique employing EGM2008 as geopotential Earth model complete to degree and order 1800. The AFRGDB_V2.2 has been established using the Residual Terrain Model (RTM) reduction technique employing GOCE DIR_R5 complete to degree and order 280, using the best RTM reference surface. The available gravity data set for Africa, used to establish the above mentioned two independently derived databases, consists of shipborne, altimetry derived gravity anomalies and of land point gravity data. In particular, the data set of point gravity values shows clear deficits with regard to a homogeneous data coverage over the completely African continent. The establishment of the gravity databases has been carried-out using the weighted least-squares prediction technique, in which the point gravity data on land has got the highest precision, while the shipborne and altimetry gravity data got a moderate precision. In this paper a new gravity data set on land and on sea, which became recently available for the IAG Sub-Commission on the gravity and geoid in Africa, located partly in the gap areas of the data set used for generating the gravity databases, has been employed to evaluate the accuracy of the previously created gravity databases. The results show reasonable accuracy of the established gravity databases considering the large data gaps in Africa.

This paper explores the problem of symmetric transformation in the presence of outliers for photogrammetric and remote-sensing applications. We propose a point-wise robust objective for symmetric coordinate transformation by assuming the corresponding structure of the down-weighting matrix and choosing the squared Mahalanobis distance as the statistic to evaluate the down-weighting factors. Compared to the previous work, our method regards all the coordinates of one point as a whole. It is practical in principle since the displacement and correspondence relation are point-wise; it is statistically rigorous since the stochasticity of both frames is considered; it is flexible since the incorporated constraints enable all kinds of transformation. By utilizing the parameter partition technique, a more intelligent iteration manner is proposed for the implementation aspect, which specially suits globally distributed geoscience data. The developed method is vetted in the synthetic and real examples including geodetic datum conversion and point cloud registration. The algorithms have potential in many areas, including cross-source registration, image analysis, pattern recognition, surface modeling and remote-sensing image registration.