06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
Browse
4 results
Search Results
Item Open Access Item Open Access Understanding the hydrological signature in gravity data(2023) Schollmeier, PhilippOver the past two decades, the subsequent advancements in Superconducting Gravimeters (SGs) have ushered in a level of precision that enables the measurement of the impact of ground water and soil water on gravity. Because of the challenging nature of monitoring the total water volume and the relatively subtle amplitude of the hydrological signal, a comprehensive understanding of the precise hydrological signature in continuous gravity data remains elusive. In this study, I use SG data in conjunction with hydrological measurements from a geoscientific observatory in Germany to find the signature of hydrological signals in gravity data. I scrutinize the various steps involved in extracting this signal, presenting new methodologies, including a technique to eliminate oscillations in gravity residuals that are likely attributed to remaining tidal signals due to an imperfect tidal model. A major contribution of this work involves constructing a data-driven model that incorporates precipitation and soil moisture measurements to elucidate gravity variations. I address critical questions such as the impact of utilizing soil moisture data on the model’s performance, determining the optimal model for achieving the closest fit with gravity measurements, and assessing the applicability of computed model parameters to new epochs. Furthermore, I provide recommendations for refining the model-building process in future investigations. Results show that a convolution of the different hydrological timeseries with one half of a Gaussian bell curve leads to a strong agreement with the gravity measurements. The use of soil moisture data significantly improves the fit, especially when the measurement stations are spatially well distributed. This fit becomes less strong when the computed parameters are applied to new events, but the approach showed promise for some of the events. Enhancing our comprehension of the hydrological influence on gravity measurements holds promising implications, potentially positioning SGs as instruments for monitoring soil and ground water in the future. Moreover, this improved understanding could elevate the pre cision of analyzing other subtle signals, such as the effects of Polar Motion.Item Open Access Validating Sentinel 3 altimetry over the Neckar River using GNSS Interferometric Reflectometry(2023) Yu, ZiqingGNSS-IR is a technique that enables the constant observation of water surface height using reflected GNSS signals from water surface. It offers a simple monitoring approach compared to other techniques, requiring only a GNSS receiver near the water. The principle of the technique involves analyzing signal-to-noise data by converting signals from the time domain to the frequency domain. Satellite altimetry is another powerful technique for long-term water monitoring, providing extensive spatial coverage. The retrieval of water level from altimetry waveforms, known as retracking, is susceptible to errors due to various factors, despite the development of multiple retracking algorithms for different waveform types. The Sentinel-3 satellite mission, operated by ESA and EUMETSAT, is designed to monitor Earth’s surface topography and climate while providing altimetry data. In this study, the altimetry results for the Neckar river from Sentinel-3 mission will be validated using the GNSS-IR technique. Due to the absence of a permanent GNSS receiver at the ideal measurement point, the measurement campaigns have a limited duration of a few hours each time. To better receive the reflected signals from water, GNSS antennas are rotated in the last 2 campaigns. To maximize the utilization of GNSS signal-to-noise ratio (SNR) data and capture the dynamic water level changes during observations, a novel technique is developed. This technique involves splitting the data according to time and multiplying the Lomb-Scargle Periodograms(LSP) from different satellites within specific time ranges. By extracting the peaks of the multiplied periodograms, a time series can be generated. The altimetry results from the Sentinel-3 mission will then be validated using this time series. To enhance the quality of GNSS-IR results, various methods have been implemented, including selecting different campaign locations, rotating GNSS receivers, and applying data filters such as elevation angle. GNSS-IR is proved to be a able to monitor the inland small water body and rotating the GNSS antenna can improve the result quality. All seven retrackers from the Sentinel-3 mission are validated using water level data obtained from GNSS-IR. The altimetry water level is higher as the result from GNSS-IR and this offset varies for different retrackers from about 0.1 to 0.4 meters. In the challenging Neckar area with a narrow river width and complex environment, OCOG has demonstrated the best performance in terms of both availability and accuracy, the difference is from 0.03 to 0.17 meters in all experiments.Item Open Access Surface water extent monitoring using the Global WaterPack product: automated extraction, refinement, and analysis(2025) Jalali Jirandehi, MasoudMonitoring and analyzing surface water dynamics is critical for understanding hydrological variations, climate change impacts, and water resource management. Traditional methods of surface water storage monitoring rely on in-situ measurements, which are often spatially and temporally limited. Remote sensing has revolutionized this field by enabling large-scale, consistent, and continuous observations of area and height water storages. This study presents a methodology for generating accurate water area time series using the Global WaterPack (GWP), a monthly satellite-derived dataset. Developed by the German Aerospace Center (DLR), the GWP dataset is specifically designed for monitoring surface water dynamics on a global scale. A Python-based processing tool is developed to systematically extract and analyze lake and river water extents, mitigating key challenges such as cloud contamination, defining proper threshold, and classification inaccuracies. By integrating high-frequency surface water observations from GWP with the Prior Lake Database PLD (a static dataset for extracting the initial search area), and the SWOT (The Surface Water and Ocean Topography) prior River Database (SWORD), which provides a standardized framework of high-resolution river nodes and reaches, this tool enhances the reliability of time-series analysis. This framework improves surface water change detection, reduces computational complexity, and refines water occurrence assessments under diverse hydroclimatic conditions. The workflow automates the entire process, allowing users to select lakes interactively via a geospatial interface or upload coordinate lists for batch processing. Key steps include (1) downloading images, (2) defining the search area, (3) normalization, (4) generating a water occurrence map, (5) defining constant water and land masks, (6) residual analysis, (7) deriving and applying thresholds, (8) generating time series plots, and (9) correlation analysis. Advanced filtering methods, such as threshold-based classification and residual analysis, refine water occurrence measurements, while an adaptive thresholding approach using the Cumulative Distribution Function (CDF) enhances water body delineation accuracy. To evaluate the reliability of the extracted data, the resulting surface area time series are compared against altimetry-derived water height records using correlation analysis. The analysis revealed clear seasonal and interannual variations in lake water areas, aligning well with natural hydrological patterns. Many lakes showed strong positive correlations between satellite-derived surface area and altimetry-based water levels, validating the method’s effectiveness. However, weaker correlations in some cases were attributed to issues like cloud cover, sensor limitations, and complex hydrodynamics. The study emphasized that a fixed threshold is insufficient for all systems, whereas the corrected method provided more reliable results across diverse conditions. Although river analysis showed varied hydraulic responses, the tool proved useful for monitoring floods, seasonal changes, and long-term water trends, especially with proper calibration. By providing an automated, scalable, and accurate tool for water body monitoring, this thesis contributes to advancing hydrological analysis using remote sensing and geospatial processing techniques. The developed tool can aid in climate studies, water resource management, and flood risk assessment, offering a valuable framework for long-term surface water monitoring.