06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
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Item Open Access Analyzing and characterizing spaceborne observation of water storage variation : past, present, future(2024) Saemian, Peyman; Sneeuw, Nico (Prof. Dr.-Ing.)Water storage is an indispensable constituent of the intricate water cycle, as it governs the availability and distribution of this precious resource. Any alteration in the water storage can trigger a cascade of consequences, affecting not only our agricultural practices but also the well-being of various ecosystems and the occurrence of natural hazards. Therefore, it is essential to monitor and manage the water storage levels prudently to ensure a sustainable future for our planet. Despite significant advancements in ground-based measurements and modeling techniques, accurately measuring water storage variation remained a major challenge for a long time. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) satellites have revolutionized our understanding of the Earth's water cycle. By detecting variations in the Earth's gravity field caused by changes in water distribution, these satellites can precisely measure changes in total water storage (TWS) across the entire globe, providing a truly comprehensive view of the world's water resources. This information has proved invaluable for understanding how water resources are changing over time, and for developing strategies to manage these resources sustainably. However, GRACE and GRACE-FO are subject to various challenges that must be addressed in order to enhance the efficacy of our exploitation of GRACE observations for scientific and practical purposes. This thesis aims to address some of the challenges faced by GRACE and GRACE-FO. Since the inception of the GRACE mission, scholars have commonly extracted mass changes from observations by approximating the Earth's gravity field utilizing mathematical functions termed spherical harmonics. Various institutions have already processed GRACE(-FO) data, known as level-2 data in the GRACE community, considering the constraints, approaches, and models that have been utilized. However, this processed data necessitates post-processing to be used for several applications, such as hydrology and climate research. In this thesis, we evaluate various methods of processing GRACE(-FO) level-2 data and assess the spatio-temporal effect of the post-processing steps. Furthermore, we aim to compare the consistency between GRACE and its successor mission, GRACE-FO, in terms of data quality and measurement accuracy. By analyzing and comparing the data from these two missions, we can identify any potential discrepancies or differences and establish the level of confidence in the accuracy and reliability of the GRACE-FO measurements. Finally, we will compare the processed level-3 products with the level-3 products that are presently accessible online. The relatively short record of the GRACE measurements, compared to other satellite missions and observational records, can limit some studies that require long-term data. This short record makes it challenging to separate long-term signals from short-term variability and validate the data with ground-based measurements or other satellite missions. To address this limitation, this thesis expands the temporal coverage of GRACE(-FO) observations using global hydrological, atmospheric, and reanalysis models. First, we assess these models in estimating the TWS variation at a global scale. We compare the performance of various methods including data-driven and machine learning approaches in incorporating models and reconstruct GRACE TWS change. The results are also validated against Satellite Laser Ranging (SLR) observations over the pre-GRACE period. This thesis develops a hindcasted GRACE, which provides a better understanding of the changes in the Earth's water storage on a longer time scale. The GRACE satellite mission detects changes in the overall water storage in a specific region but cannot distinguish between the different compartments of TWS, such as surface water, groundwater, and soil moisture. Understanding these individual components is crucial for managing water resources and addressing the effects of droughts and floods. This study aims to integrate various data sources to improve our understanding of water storage variations at the continental to basin scale, including water fluxes, lake water level, and lake storage change data. Additionally, the study demonstrates the importance of combining GRACE(-FO) observations with other measurements, such as piezometric wells and rain-gauges, to understand the water scarcity predicament in Iran and other regions facing similar challenges. The GRACE satellite mission provides valuable insights into the Earth's system. However, the GRACE product has a level of uncertainty due to several error sources. While the mission has taken measures to minimize these uncertainties, researchers need to account for them when analyzing the data and communicate them when reporting findings. This thesis proposes a probabilistic approach to incorporate the Total Water Storage Anomaly (TWSA) data from GRACE(-FO). By accounting for the uncertainty in the TWSA data, this approach can provide a more comprehensive understanding of drought conditions, which is essential for decision makers managing water resources and responding to drought events.Item Open Access Item Open Access Assessment of ICESat-2 laser altimetry in hydrological applications(2024) Wang, Bo; Sneeuw, Nico (Prof. Dr.-Ing.)Water bodies act as critical components of the hydrological cycle, serving as reservoirs, lakes, wetlands, and aquifers that store and release water over time. Monitoring changes in the extent and volume of these water bodies is crucial for understanding their role in regulating water flow, maintaining baseflow during dry periods, and supporting ecological habitats. Furthermore, the identification of trends and alterations in water body dynamics aids in detecting potential impacts of climate change and human activities on the hydrological cycle. Historically, gauge stations have been employed to monitor the water level of these bodies since the 19th century. However, their numbers have been dwindling since the 1970s due to maintenance challenges. With the development of satellite altimetry missions, more accurate and continuous monitoring of lakes and rivers has become possible. These satellites in recent years offer the capability to provide water level data with different along-track sampling distances. For instance, ICESat-2 offers a sampling distance of 70 cm with a footprint size of ~17 m, while Sentinel-3 provides a sampling distance of 300 m. The temporal resolution ranges from 10 days (Jason-3) to 369 days (Cryosat-2). These advances allow researchers to effectively observe and understand changes in water bodies. The invention of satellite-based laser altimetry has brought a revolutionary advancement in our ability to monitor and study Earth’s water bodies with unprecedented precision and extensive spatial coverage. This doctoral thesis aims to explore the diverse applications of ICESat-2 laser altimetry data over inland water bodies. Through these investigations, the aim is to advance our understanding of global hydrological processes and acquire valuable insights to improve water resource management strategies. It is important to understand the error budget of the altimetric observations, one component of which is radial orbit error. Apart from the altimetric ranging errors, radial orbit errors directly influence the accuracy of the measurement of Earth’s surface heights. These errors can be assessed by analyzing the difference of surface heights at ground track intersections, so-called crossover differences (XO differences). An effective approach is to model the orbit error by minimizing the residual XO difference by the least-squares (LS) method, which is commonly known as XO adjustment. This method was implemented in the Arctic region to examine the performance of the LS adjustment over spherical cap geometry and assess the level of radial orbit error across a large-scale area. This analysis will aid in understanding the accuracy and reliability of ICESat-2 satellite orbit over the Arctic region. The ICESat-2 satellite captures high-resolution observations of Earth’s surface, including land and water, thus enabling dense measurements of heights. The green laser used in ICESat-2 has the capability to penetrate water surfaces, allowing measurements of not only the lake water level but also the nearshore water bottom. This study proposes a novel algorithm that combines ICESat-2 measurements with Landsat imagery to extract lake water level, extent and volume. This algorithm was applied to Lake Mead, resulting in a long-term time series of water level, extent and volume dating back to 1984, only derived from remote sensing data. The ICESat-2 satellite is equipped with three pairs of laser transmitters, which concurrently generate three pairs of ground tracks. This unique characteristic enables us to derive river surface heights for each ground track, thereby calculating the river slope between two tracks, referred to as the across-track river slope. Moreover, when one ground track passes through the river surface, producing dense measurements, it allows us to obtain the small-scale slope for that specific track, termed the along-track based river slope. By using these methods, both types of slopes were estimated for the entire length of the Rhine River and subsequently generated the average slope for each reach along the river.Item Open Access Cross-over analysis of altimetry over ocean and investigating the orbital error’s effect on inter-mission/track bias in inland altimetry(2024) Kappich, OliverThe largest part of Earth’s surface (approximately 71%) is covered with water. Given the constantly changing environment, particularly amidst accelerated climate change, it is crucial to continuously measure the water levels of oceans and lakes. Therefore, satellite altimetry becomes essential. The orbits of the altimetry satellites are selected in a way that allows satellites to pass over the same locations after a specific interval. These orbits are termed as repeat orbits, facilitating the creation of time series measurements. Over the past 40 years, numerous altimetry satellite missions have been launched. When multi-mission monitoring of water bodies is targeted, each satellite altimeter possesses its own biases, which should be removed for comparability among different missions. This ensures the creation of long-term data records by combining data from various missions. Over open oceans, this is typically achieved through a cross-calibration method. However, these methods prove effective for ocean data but not for inland altimetry. In this thesis, I investigated the reasons for the bias among water values measured by different satellites. Additionally, I explored potential solutions to merge the data. The main focus lays on the tandem phases of Jason 1 and 2, as well as Jason 2 and 3. The study area focused on Lake Erie, situated in the Great Lakes region in the northwest of the US. To reduce the bias, I employed a cross-calibration method to estimate and reduce radial error components. As this approach does not resolve the entire bias problem, I investigated the retracking algorithms by comparing their results. Differences between height measurements of Jason 2 and Jason 3, both using MLE4, were identified. It could be determined that MLE4 in Jason 3 finds systematically lower values compared to Jason 2. Over the whole tandem phase, Jason 3 finds the retracking point approximately 20%, in respect to the leading edge, lower than Jason 2. The influence of this systematic difference on the SSH/LLH remains unclear, as no further investigations are done. To get a better understanding if the bias can be reduced when the mid-height point is used, two simple threshold retracking algorithms are employed. The outcome is, that the difference between Jason 2 and Jason 3 increased to 18.3 cm on average. Lastly, I examined the corrections that need to be added to the range measurement of the satellite. This includes the geoid undulation, tidal height variations, the ocean surface response caused by atmospheric pressure and propagation delay due to the atmosphere. I found differences of 5 to 8 cm over Lake Erie in the atmosphere corrections. Employing the same corrections for two satellites yielded the most effective bias reduction. However, employing this technique, necessitates satellites passing the same location within a few minutes of each other. Consequently, the Jason satellites were selected during their tandem phases. On average the bias could be reduced from 7 cm to 2.4 cm. The study delved into understanding and reducing biases in satellite altimetry measurements, particularly focusing on the tandem phases of Jason satellites, revealing challenges and promising methods to significantly reduce biases.Item Open Access The B-spline mapping function (BMF) : representing anisotropic troposphere delays by a single self-consistent functional model(2024) He, Shengping; Hobiger, Thomas; Becker, DorisTroposphere’s asymmetry can introduce errors ranging from centimeters to decimeters at low elevation angles, which cannot be ignored in high-precision positioning technology and meteorological research. The traditional two-axis gradient model, which strongly relies on an open-sky environment of the receiver, exhibits misfits at low elevation angles due to their simplistic nature. In response, we propose a directional mapping function based on cyclic B-splines named B-spline mapping function (BMF). This model replaces the conventional approach, which is based on estimating Zenith Wet Delay and gradient parameters, by estimating only four parameters which enable a continuous characterization of the troposphere delay across any directions. A simulation test, based on a numerical weather model, was conducted to validate the superiority of cyclic B-spline functions in representing tropospheric asymmetry. Based on an extensive analysis, the performance of BMF was assessed within precise point positioning using data from 45 International GNSS Service stations across Europe and Africa. It is revealed that BMF improves the coordinate repeatability by approximately 10%horizontally and about 5% vertically. Such improvements are particularly pronounced under heavy rainfall conditions, where the improvement of 3-dimensional root mean square error reaches up to 13%.Item Open Access High-dimensional experiments for the downward continuation using the LRFMP algorithm(2024) Schneider, Naomi; Michel, Volker; Sneeuw, NicoTime-dependent gravity data from satellite missions like GRACE-FO reveal mass redistribution in the system Earth at various time scales: long-term climate change signals, inter-annual phenomena like El Niño, seasonal mass transports and transients, e. g. due to earthquakes. For this contemporary issue, a classical inverse problem has to be considered: the gravitational potential has to be modelled on the Earth’s surface from measurements in space. This is also known as the downward continuation problem. Thus, it is important to further develop current mathematical methods for such inverse problems. For this, the (Learning) Inverse Problem Matching Pursuits ((L)IPMPs) have been developed within the last decade. Their unique feature is the combination of local as well as global trial functions in the approximative solution of an inverse problem such as the downward continuation of the gravitational potential. In this way, they harmonize the ideas of a traditional spherical harmonic ansatz and the radial basis function approach. Previous publications on these methods showed proofs of concept. In this paper, we report on the progress of our developments towards more realistic scenarios. In particular, we consider the methods for high-dimensional experiment settings with more than 500 000 grid points which yields a resolution of 20 km at best on a realistic satellite geometry. We also explain the changes in the methods that had to be done to work with such a large amount of data.Item Open Access Building a fully-automatized active learning framework for the semantic segmentation of geospatial 3D point clouds(2024) Kölle, Michael; Walter, Volker; Sörgel, UweIn recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically ≪1%of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become human processing units in our hybrid intelligence system mirroring the functionality of electronic processing units .