Water vapor, the most influential greenhouse gas, is central to Earth’s climate system, affecting the hydrological cycle, energy balance, and atmospheric dynamics. Integrated Water Vapor (IWV) is a key variable for understanding these processes. However, conventional IWV retrieval methods—such as ground-based sensors, satellite observations, and numerical weather models (NWM)—are often limited by spatial resolution, temporal continuity, and retrieval accuracy. To address these challenges, this study introduces a novel deep learning method GMLSTM-HIM, a High-resolution IWV estimation Model (HIM) based on a Gaussian Mixture Long Short-Term Memory (GMLSTM) framework. By integrating Global Navigation Satellite System (GNSS) and NWM inputs, including weighted mean temperature, GMLSTM-HIM utilizes a bidirectional LSTM structure and probabilistic output sequences to improve IWV estimation accuracy while quantifying uncertainty arising from spatial heterogeneity. Compared to ERA5 and VMF3, the model achieves average RMSE reductions of 68.44% and 36.15%, respectively. The model’s performance is further evaluated through inter-comparisons with MODIS and Fengyun satellite-derived IWV products, highlighting both the accuracy of GMLSTM-HIM and the complementary strengths of satellite observations. The results suggest that, of the satellite datasets examined in this case study, the MODIS 5 km product exhibits the highest consistency with the GMLSTM-HIM model estimates, outperforming the higher-resolution MODIS 1 km and FY-3D 1 km products in terms of product reliability (measured by root mean square error and correlation). A data fusion strategy is also proposed, combining model and satellite estimates to preserve fine-scale details and enhance robustness. Overall, GMLSTM-HIM provides a robust framework for high-resolution IWV retrieval, with significant potential to advance atmospheric studies, climate surveillance, and operational weather forecasting within the remote sensing community.

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 station distribution. Conversely, empirical models combined with numerical weather models (NWMs), 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 article 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 4.6 mm. 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.

This article explores the problem of symmetric transformation in the presence of outliers for photogrammetric and remote-sensing applications. We propose a pointwise robust objective for symmetric coordinate transformation by assuming the corresponding structure of the downweighting matrix and choosing the squared Mahalanobis distance (MD) as the statistic to evaluate the downweighting factors. Compared with 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 pointwise; 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.

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the latest release of Bodenbewegungsdienst Deutschland (BBD) [1] provided September 2022 by Federal Institute for Geosciences and Natural Resources (BGR) and the first release of the European Ground Motion Service (EGMS) [2] as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel-1 data that were processed by GAF AG with software developed by Earth Observation Center (EOC), which is part of German Aerospace Center (DLR). It suggests itself to ask, if there is some added value of BBD over EGMS and how well do the two new releases perform compared to other geodetic techniques. For a study commissioned by the surveying authorities of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg (LGL)), we investigated the performance of BBD and EGMS and validated them against levelling and GNSS data.

The modern GNSS technique is one of the most effective geoscience and remote-sensing tool to observe crustal motions and quantify plate tectonics dynamics. Given multiple installed continuously operating GNSS observing stations, the multi-frame transformation is implemented to connect the timevarying GNSS coordinates by the traditional step-wise method. Compared with the step-wise treatment of each pair of frames, the proposed structured total least-squares method considers the combined estimation for all frames, guaranteeing unique and consistent results for the multi-frame symmetric transformation. Furthermore, we introduce the variance component as the nutshell and flexible indicator for land movement. The variance components can quantify the movement coordinate-wise, regional-wise, or frame-wise if the variance components are estimable as we analyze. The simulated experiment shows that the multi-frame symmetric transformation is statistically superior to the traditional stepwise treatment. For the application, the deformation caused by Tohoku earthquake that happened in 2011 in northeast Japan is analyzed.