A SWOT-Based Approach for High-Resolution Intertidal DEM Mapping in the Elbe Estuary

Mingzhi Sun1 2, Luciana Fenoglio-Marc1, Jiaming Chen1, Jürgen Kusche1, Wei Feng2

1. Institute of Geodesy and Geoinformation, University of Bonn, Bonn 53115, Germany

2. School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China.

Tidal flats in the Elbe Estuary, Germany, are highly dynamic, with morphology changing rapidly due to tidal cycles, storms, and sedimentation. These changes affect water depth and tidal propagation, making high-resolution digital elevation models (DEMs) essential for hydrodynamic modeling, ecological monitoring, habitat mapping, and sediment transport studies. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution elevation data, offering new potential for generating accurate DEMs of intertidal zones. This study proposes a novel approach to derive high-resolution tidal flat DEMs using only SWOT observations, by integrating backscatter intensity and water surface elevation from the wide-swath KaRIn instrument.

Exploiting the bimodal Gaussian distribution of the backscatter signal in the SWOT_L2_HR_PIXC (v2.0) product, a peak–trough thresholding algorithm is developed to segment the data and extract valid elevation observations over tidal flats during satellite overpasses. Using the backscatter coefficient as a reference, water bodies and tidal flats can be effectively distinguished. A 25-meter resolution DEM of the tidal flats in the Elbe Estuarywas generated. Observations at low tide allow to build an high-quality DEM, therefore the results depend on the repeat cycle of the satellite. The RMSE of the SWOT-derived DEM and ICESat-2 laser altimetry is 0.18 m.

By combining physical principles with data-driven characteristics, this study provides a new data source and a methodology for coastal topographic mapping.

Satellite navigation has become ubiquitous in our everyday life and plays a crucial role also in geodesy. In order for the users to obtain optimal performance, reference stations are key elements: they allow to monitor satellite signals, providing alerts for malfunctioning as well as constantly estimating propagation errors and possibly distributing this information (through augmentation systems) to the users.

Antennas drive the obtainable performance of reference stations: with the development of antennas with always better performance (for instance in terms of phase center and group delay variations), research has also focused in the last years on how to achieve highest accuracy by minimizing the intrinsic errors of the antennas.

Near-field effects are among the largest remaining influencing variables in GNSS positioning. All objects in the direct vicinity of the antenna (near field) influence the reception behaviour of the antenna, effectively turning the antenna “standalone” pattern into an “installed” pattern, different for each installation. The strength of the near-field influence depends, among other things, on the size, geometry, position and material of the closeby objects. This variability of the antenna properties has a detrimental effect for precise geodetic measurement, as it introduces a not-known error, limiting the achievable accuracy.

Efforts to precisely characterize the antenna intrinsic-errors, such as phase centre and group delay variation, do not bring in this framework the wished improvement, as antennas are typically characterized in “standalone” configuration, i.e. not considering the actual objects (mast, pillar) they will be installed on in the final configuration.

Near-field effects are among the largest remaining influencing variables in GNSS positioning. All objects in the direct vicinity of the antenna (near field) influence the reception behaviour of the antenna, effectively turning the antenna “standalone” pattern into an “installed” pattern, different for each installation. The strength of the near-field influence depends, among other things, on the size, geometry, position and material of an interfering object. This variability of the antenna properties has a detrimental effect for precise and geodetic measurement, as it introduces a not-known error, limiting the achievable accuracy.

This paper aims at shedding insight into the influence of mounting structures on GNSS antenna performance, mostly in terms of PCV and GDV, and proposing possible countermeasures. To do so, we will use a mixture of electromagnetic measurement and simulation, to be able to visualize the effect of mounting structures on the antenna fields and then validate our observations on real antennas.

Authors: Stefano Caizzone, Stefan Ionita, Wahid Elmarissi

At German ports and waterways, there are approximately 3,000 km of shoreline walls and about 2,500 km of traffic structures. As these structures have mostly reached an advanced age, it is essential to know whether they are still load-bearing. Therefore, these structures must be examined for their reliability.
The common method to carry out this examination has been to deploy divers to inspect the walls underwater. This is very time-consuming, especially given that underwater light conditions in German ports are often poor. To overcome these disadvantages, sheet pile walls can also be surveyed using a multi-sensor system with a multi-beam echo sounder (MBES). This system measures the walls under water using sound waves and provides a point cloud of the structure.
To better assess and predict the reliability of the measurement data, it is essential to understand how various influencing factors – such as measurement distance, incidence angle, and surface roughness – affect the MBES measurements. Therefore, test walls with different geometric shapes, surface materials, and holes of varying size and shape were constructed. Based on the known geometry, hole positions, and dimensions, various characteristics were analyzed, with particular focus on the noise level and resolution capabilities of the MBES.

Monitoring geometric deformations of dam structures has traditionally relied on discrete, pointwise measurements acquired using total stations and predefined signalized targets. While this approach provides high precision at selected locations, it remains spatially sparse and limited in its ability to capture surface-wide deformations. In contrast, dense point clouds obtained from terrestrial laser scanning (TLS) or unmanned aerial vehicle (UAV)-based laser scanners provide high-resolution surface coverage, enabling comprehensive deformation analysis. However, they lack the ability to consistently observe identical surface points across different epochs, which complicates temporal comparisons.

To address this limitation, we propose a feature-based registration workflow tailored for multi-epoch point cloud analysis. Distinct and temporally stable 3D surface features are extracted using established handcrafted keypoint detectors and local geometric descriptors. These features are matched across epochs to establish repeatable point correspondences. Based on the resulting correspondences, a global affine transformation is estimated via a least-squares adjustment to quantify spatially distributed deformations on the dam surface. This approach enables geometrically interpretable deformation models while avoiding the limitations of dense surface matching techniques.

The method is evaluated using real-world data from two different dams, acquired through point clouds from both TLS and UAV platforms. The resulting deformation estimates are compared against those obtained using the Iterative Closest Point (ICP) algorithm, which serves as a widely used reference method.

Authors:
Annika Tobies, Eike Koller, Ansgar Dreier, André Cornelißen, Lasse Klingbeil and Heiner Kuhlmann

Urban environments with various obstructions by buildings, trees or other traffic participants are extremely challenging for reliable GNSS based positioning and navigations, needed for highly automated applications such as autonomous driving or autonomous shuttle services. In this contribution, we will summarize the challenges in urban environments. We will show recent research progress at IfE Hannover to overcome them and to provide reliable positioning. 

The application of mobile mapping systems (MMS) has increased continuously in the last decades in fields like infrastructure or ecosystem monitoring. Equipped with multiple laser scanners and cameras, these systems can generate high-resolution 3D point clouds of the environment in a short time. In this process, the accuracy of the trajectory of the system is of central importance as it directly affects the accuracy of the resulting point cloud.

However, since the trajectory estimation depends on sensor observations that are often affected by unknown systematic errors, the actual accuracy of the trajectory remains mainly unknown.

To uncover the gap in the trajectory accuracy assessment, I want to present my work on a method to create ground truth trajectories for mobile mapping systems by integrating millimeter-accurate total station measurements. We mount two 360-degree prisms on a mobile platform, track them with two Robotic Total Stations (RTS) during motion, and fuse these prism measurements with the readings from an Inertial Measurement Unit (IMU) using a factor graph-based trajectory estimation approach.

To evaluate the quality of this ground truth trajectory, we record repeated measurements on a closed-loop rail track close to Bonn, Germany. The results show that the generated ground truth trajectory estimated with RTS and IMU data achieves a precision of around 1 mm in position and 0.05° in orientation. To show the potential of the method, we detect systematic deviations of an example MSS that uses Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) and IMU data for trajectory estimation. The results show that even under good GNSS conditions, the ground truth trajectory has significantly better precision and less systematic errors than the trajectory based on RTK-GNSS and IMU data.

Author: Manuel Mittelstedt, Felix Esser, Gereon Tombrink, Lasse Klingbeil, Heiner Kuhlmann