06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access A MATLAB toolbox for the Scintrex CG-5 gravimeter at GIS(2017) Gu, SiyunThis thesis is about a MATLAB toolbox for the Scintrex CG-5 gravimeter. The aim of this toolbox is to offer a basic data process for gravity measurement, which is compatible for most applications in geodesy. In particular, the toolbox covers: 1. data selection, 2. adjustment, 3. gravity gradient computation, 4. gravity visualization, 5. calibration factor estimation. A graphical user interface enables users without deeper programming knowledge to operate this toolbox and obtain the results like adjusted values or figures.Item Open Access Dynamic water masks from optical satellite imagery(München : Verlag der Bayerischen Akademie der Wissenschaften, 2019) Elmi, Omid; Sneeuw, Nico (Prof. Dr.-Ing.)Investigation of the global freshwater system has a vital role in critical issues e.g. sustainable development of water resources, acceleration of the hydrological cycle, variability of global sea level. Measurement of river streamflow is vital for such investigations as it gives a reliable estimate of freshwater fluxes over the continents. Despite such importance, the number of river discharge gauging station has been decreasing. At the same time, information on the global freshwater system has been increasing because of various types of ground observations, water-use information and spaceborne geodetic observations. Nevertheless, we cannot answer properly crucial questions about the amount of freshwater available on a certain river basin, or the spatial and temporal dynamics of freshwater variations and discharge, or the distribution of world’s freshwater resources in the future. The lack of comprehensive measurements of surface water storage and river discharge is a major impediment for a realistic understanding of the hydrological water cycle, which is a must for answering the aforementioned questions. This thesis aims to improve the methods for monitoring the surface extent of inland water bodies using satellite images. Satellite imaging systems capture the Earth surface in a wide variety of spectral and spatial resolution repeatedly. Therefore satellite imagery provides the opportunity to monitor the spatial change in shorelines, which can serve as a way to determine the water extent. Each band of a multispectral image reveals a unique characteristic of the Earth surface features like surface water extent. However selecting the spectral bands which provide the relevant information is a challenging task. In this thesis, we analyse the potential of multispectral transformations like Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) to tackle this issue by condensing the information available in all spectral bands in just a few uncorrelated variables. Moreover, we investigate how the change between multispectral images at different epochs can be highlighted by using the transformations. This study proposes an automatic algorithm for extracting the lake water extent from MODIS images and generating dynamics lake masks. For improving the accuracy of the lake masks and computational efficiency of the algorithm, two masks are defined for limiting the search area. The restricting masks are developed according to DEM of the surrounding area together with a map of the long-term variation of pixel values. Subsequently, an unsupervised pixel-based classification algorithm is applied for defining the lake coastline. The algorithm particularly deals with the challenges of generating long time series of lake masks. We apply the algorithm on five lakes in Africa and Asia, each of which demonstrates a challenge for lake area monitoring. However in the validation section, we demonstrate that the algorithm can generate accurate dynamic lake masks. Rivers show diverse behaviour along their path due to the contribution of different parameters like gradient of the elevation, river slope, tributaries and river bed morphology. Therefore for generating accurate river reach mask, we need to consider additional sources of information apart from pixel intensity. The region-based classification algorithm that we propose in this study takes advantages of all types of available information including pixel intensity and spatial and temporal interactions. Markov Random Fields provide a flexible frame for interaction between different sources of data and constraint. To find the most probable configuration of the field, the Maximum A Posteriori solution for the MRF must be found. To this end, the problem is reshaped as an energy minimization. The energy function is minimized applying graph cuts as a powerful optimization technique. The uncertainty in the graph cuts solution is also measured by calculating the minimum marginal energies. The proposed method is applied to four rivers reaches with different hydrological characteristics. We validate the obtained river area time series by comparing with in situ river discharge and satellite altimetric water level time series. Moreover, in this study, we present river discharge estimation models using the generated river reach masks. Our aim is to find an empirical relationship between the average river reach width and river discharge. The statistics in the validation periods support the idea of using river width-discharge prediction models as a complementary technique to the other spaceborne geodetic river discharge prediction approaches.Item Open Access The zero gravity curve and surface and radii for geostationary and geosynchronous satellite orbits(2017) Sjöberg, L. E.; Grafarend, Erik W.; Joud, M. S. S.A geosynchronous satellite orbits the Earth along a constant longitude. A special case is the geostationary satellite that is located at a constant position above the equator. The ideal position of a geostationary satellite is at the level of zero gravity, i.e. at the geocentric radius where the gravitational force of the Earth equals the centrifugal force. These forces must be compensated for several perturbing forces, in particular for the lunisolar tides. Considering that the gravity field of the Earth varies not only radially but also laterally, this study focuses on the variations of zero gravity not only on the equator (for geostationary satellites) but also for various latitudes. It is found that the radius of a geostationary satellite deviates from its mean value of 42164.2 km only within ±2 m, mainly due to the spherical harmonic coefficient J22, which is related with the equatorial flattening of the Earth. Away from the equator the zero gravity surface deviates from the ideal radius of a geosynchronous satellite, and more so for higher latitudes. While the radius of the former surface increases towards infinity towards the poles, the latter decreases about 520 m from the equator to the pole. Tidal effects vary these radii within ±2.3 km.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 Estimation of significant wave height using GNSS interferometric reflectometry(2025) Wilczynski, Martin JanReflected GNSS signals contain valuable information about their reflecting surface. While reflections are typically discarded in geodetic applications, GNSS-R makes use of them, particularly in environments with large reflective areas such as water. In coastal regions, surface waves are a dominant environmental feature. A commonly used parameter to describe wave conditions is the significant wave height (SWH). Its estimation using satellite altimetry can be challenging in coastal zones due to the mixture of land and water within the sensor footprint. In such cases, in-situ measurements are often required to bridge these observational limitations. This study investigates the estimation of SWH from ground-based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) using standard geodetic antennas. Reflected GNSS signals contain interference signatures visible in the signal-to-noise ratio (SNR), from which surface roughness can be inferred. Following an modeling approach, a roughness parameter s of the reflecting surface was estimated for individual reflection arcs across multiple GNSS frequencies. In a second step, s was related to in-situ SWH from nearby moorings using a Total Least Squares regression. Two coastal stations along the German North Sea coast and one station on the U.S. West Coast were analyzed. Results show that only 10% of the available time series is sufficient to derive stable parameters, enabling robust SWH predictions over the remaining 90% of the observation period. The resulting RMSE values range from approximately 18 cm to 34 cm across the analyzed frequencies. A transferability analysis demonstrates that parameter sets derived at one site can be applied to others without recalibration. For two of the stations, such transfer is successful for 6-7 of the 9 available frequencies, highlighting the potential for generalized parameter models. An azimuth dependent analysis showed that signals arriving from the direction of the incoming waves exhibited smaller RMSE values than those from other directions, with the opposite direction resulting in the largest errors. Time-series analyses indicate that GNSS-IR-derived SWH generally follows the temporal evolution of the in-situ observations. Moreover, periods without mooring measurements can be reconstructed using GNSS-IR.Item Open Access Spaceborne river discharge from a nonparametric stochastic quantile mapping function(2021) Elmi, Omid; Tourian, Mohammad J.; Bárdossy, András; Sneeuw, NicoThe number of active gauges with open‐data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground‐based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge's uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here, we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: (a) generating realizations of river discharge and width time series using Monte Carlo simulation, (b) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, and (c) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po Rivers, and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodeling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.Item Open Access Analyzing and modeling environmental loading induced displacements with GPS and GRACE(2015) Chen, Qiang; Sneeuw, Nico (Prof. Dr.-Ing.)The redistribution of atmospheric, oceanic and hydrological masses on the Earth's surface varies in time and this in turn loads and deforms the surface of the solid Earth. Analyzing such environmental loading signal and modeling its induced elastic displacements are of great importance for explaining geophysical phenomena. Based on the well-established loading theory, this thesis makes use of two different space-borne measurements, i.e. GPS and GRACE, along with other environmental loading data to investigate three different aspects of environmental loading and its induced elastic deformations: Firstly, an increasing concern is observed recently over time variable seasonal signals in geodesy. Several model based approaches were applied to extract amplitude and phase modulated annual and semiannual signals. In view of this phenomenon, this thesis introduces an alternative approach, namely, singular spectrum analysis (SSA). With respect to these model-dependent approaches, the advantage of SSA lies in data-driven and model-independence. Several aspects regarding the application of SSA, e.g. optimal choice of window size, are investigated before showing its abilities. Through applying SSA to the lake level time series of Lake Urmia (Iran) and the basin averaged equivalent water height time series of the Congo basin, the capabilities of SSA in separating time varying seasonal signals are demonstrated. In addition, we find that SSA is also able to extract the non-linear trend as well as long-term oscillations from geodetic time series. Secondly, we look into the comparison between GPS and GRACE with an emphasis on GRACE data filtering. Three types of deterministic filters and two types of stochastic filters are studied and compared over GPS sites from two regions, i.e. the Europe area and the Amazon area. The comparisons indicate that no single filtering scheme could provide consistently better performance over other considered filters. However, we find that the stochastic filters generally show better performance than the deterministic filters. The DDK 1 filter outperforms other filters in the Europe area and the regularization filter of parameter lambda=4, which follows the concept of the DDK filters, shows optimal performance in the Amazon area. The combination of the isotropic Gaussian filter of a low smoothing radius, e.g. around 300 km with the destriping filter is proved to be optimal filter choice if only the deterministic filters are considered. Thirdly, based on an overview of displacements modeling at various spatial scales, we evaluate three methods, i.e. two types of half-space approaches and the classic Green function approach, by using a high spatial resolution local load data along the lower Mississippi river when a severe flood happened in 2011. The equivalence between the two half-space approaches, i.e. point load approach and surface load approach, are demonstrated with the local load data. However, the point load approach is recommended for practical use in terms of computational efficiency. In addition, within such a limited spatial extent, we investigate the differences between the half-space approach and the Green function approach. It is shown that the half-space approach predicts larger displacements than the Green function approach and agrees better with the observed deformations at 11 considered GPS sites. Meanwhile, strong global environmental loading effects are found via two global hydrological models, i.e. GLDAS and MERRA. Thus, a reduction of these far-field loading effects beforehand is suggested before probing the local crustal structure using the half-space approach. Last but not least, based on the local load data, the effects of site-dependent Green functions are studied with two types of site-dependent Green functions, which were generated by modifying the local crustal structure of the REF Earth model using the CRUST 1.0 and CRUST 2.0 models. A relative RMS of differences of more than 5% in vertical component and 25% in horizontal components are found with respect to the PREM Earth model based Green functions. It indicates that the Green functions could contribute more uncertainties in loading induced displacements modeling than reported in the literature.Item Open Access The optimal regularization and its application in extreme learning machine for regression analysis and multi-class classification(2018) Qian, KunExtreme Learning Machine (ELM) proposed by Huang et al. (2006) is a newly developed single layer feed-forward neural network (SLFN). It is attractive for its high training efficiency and satisfactory performance, especially when dealing with a large amount of data, which are often in high-dimensional space. However, current ELM cannot solve the over-fitting problem among other several problems. While minimizing residuals of output errors for the training data, it tends to generate an over-fitting model, whose generalization ability is relatively weak. Even if the model fits the training data perfectly, it performs unsatisfactory for the testing data. In training process, we aim to minimize residuals of output errors of training data. It tends to generate an over-fitting model, which has poor generalization ability. The model maybe fit the training data perfectly, but performs badly in testing data. Furthermore, in order to improve accuracy, the traditional way is increasing the number of hidden-layer neurons, but excessive hidden-layer neurons result in an ill-posed normal matrix and a model which is over sensitive to the change of the training data. In such case, the performance of ELM is significantly affected by the outliers in the training data. In order to overcome these problems, we apply the regularization to the original ELM. In this study, the A-optimal design regularization is performed to improve the generalization ability and stability of ELM. The performance of ELM with the A-optimal design regularization will be evaluated through two main applications, respectively, regression analysis and satellite image multi-class classification.Item Open Access GPS time-variable seasonal signals modeling(2015) Chen, QiangSeasonal signals (annual plus semi-annual) in GPS time series are of great importance for understanding the evolution of regional mass, i.e. ice and hydrology. Conventionally these signals (annual and semi-annual) are derived by least-squares fitting of harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e. they will have a time-variable amplitude and phase. Recently, Davis et al. (2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. In this study, a non-parametric approach, singular spectrum analysis (SSA) is introduced. It uses time domain data to extract information from short and noisy time series without prior knowledge of the dynamics affecting the time series. A prominent benefit is that obtained trends are not necessarily linear and extracted oscillations can be amplitude and phase modulated. In this work, the capability of SSA for analyzing time-variable seasonal signals from GPS time series is investigated. We also compare SSA-based results to two model-based results, i.e. least-squares analysis and Kalman filtering. Our results show that singular spectrum analysis could be a viable and complementary tool for exploring modulated oscillations from GPS time series. Based on the SSA-derived seasonal signals, we look into the effects of the input noise variances in the framework of Kalman filtering. Two Kalman filtering based approaches with different process noise models are compared over 79 GPS sites. We find that the basic Kalman filtering technique with the input noise model suggested by Davis et al. (2012) turns out to be optimal.Item Open Access Hydrogeodesy : a Bayesian perspective(2025) Tourian, Mohammad J.; Sneeuw, Nico (Prof. Dr.)While historically focused on local scales, modern hydrologic studies have increasingly adopted a global perspective, recognizing water as a finite resource and the interconnection between regions. This global perspective puts hydrology within the water cycle framework, offering a comprehensive view of water dynamics across regions and scales. Despite this framework’s conceptual clarity, accurately quantifying the global water cycle remains challenging due to the complexity of capturing localized and large-scale patterns, variations in topography, climate, and land use, as well as temporal variability. These complexities hinder comprehensive measurements, resulting in knowledge gaps around key water cycle components, including river discharge, surface water storage, soil moisture dynamics, and subsurface water storage and flow. Inspired by the existing knowledge gaps in the water cycle, an emerging field known as Hydrogeodesy comes to the forefront. Hydrogeodesy is the discipline that uses terrestrial and primarily spaceborne geodetic data, both geometric and gravimetric, to support global water cycle quantification. Utilizing technologies such as satellite altimetry, gravimetry, imaging, InSAR, GNSS, and GNSS-Reflectometry, hydrogeodesy offers direct or indirect measurements of key water cycle components, including terrestrial water storage, and river discharge, significantly advancing our understanding of water dynamics. Despite advancements in spaceborne geodetic sensors, hydrogeodesy faces challenges such as limitations in the spatiotemporal resolution of satellite measurements, measurement uncertainties, unobserved variables, inconsistencies in background models, and the difficulty of separating aggregated measurements. Possible solutions to these challenges involve combining different data types, including satellite, ground-based observations, and model outputs, to benefit from their complementary strengths. However, this presents its own challenges, as it requires reconciling datasets with varying resolutions, accuracies, and temporal scales. To address some of the challenges listed above, Bayesian approaches offer viable solutions by providing probabilistic interpretations and uncertainty quantification. Bayesian approaches offer a robust framework for updating prior knowledge with new data to yield a posterior distribution, enabling a probabilistic interpretation and explicit uncertainty estimation of parameters. This is especially valuable in hydrogeodesy, where parameters like river discharge, soil moisture, and groundwater storage are often estimated indirectly and carry substantial uncertainties. This habilitation thesis provides a foundational discussion on Bayesian modeling and statistics and demonstrates the versatility and power of Bayesian methods in enhancing our understanding of water cycle components by presenting three distinct Bayesian applications in hydrogeodesy. The first study applies a Bayesian approach, specifically the Kalman filter, to estimate river discharge using spaceborne geodetic measurements. In hydrogeodetic studies, the Kalman filter and dynamic systems are especially valuable, as they enable the integration of multiple data sources and the continuous updating of estimates with incoming measurements. This is particularly beneficial for river systems, which inherently function as a dynamic system. To assess this potential, a method is introduced that uses the cyclostationary properties of discharge as prior information, while observed altimetric discharge data provide the likelihood. Together, these yield a posterior providing an unbiased daily discharge estimate. The method is applied to the Niger River basin and its main tributaries and validated against in situ data from 18 gauges. Results show a high average Correlation Coefficient (CC) of 0.9 and an average relative Root Mean Squared Error (RMSE) and bias of 15%. This method effectively estimates daily river discharge across entire basins and shows promise for global application, especially in data-scarce regions. With satellite altimetry data from multiple virtual stations and historical discharge data, daily discharge estimates with an error under 20% could be attainable in many river basins worldwide. The growing availability of spaceborne geodetic data, such as that provided by SWOT, further enhances this potential by delivering comprehensive measurements of river height and width, along with global discharge estimates. In most real-world applications, including hydrogeodesy, the Gaussianity assumption required by the Kalman filter does not hold, limiting its applicability. Inspired by this challenge, and motivated by the need to overcome the limitations of the poor spatial resolution of the GRACE and GRACE-FO missions, the second study proposes a Bayesian method to downscale GRACE data, proposing a nonparametric method to infer the posterior distribution directly, without any assumption for the likelihood or posterior. The prior distribution is obtained based on GRACE data values using the monthly variation of GRACE data. To model the likelihood functions, copulas are employed to capture dependencies among multivariate distributions. Monthly empirical copulas are constructed and fitted to analytical copulas, conditioned on specific quantile values, reflecting the dependency between GRACE and fine-scale data. A key advantage of this copula-supported Bayesian approach is its capacity to represent uncertainties in both data and models, even with variable input quality. The proposed downscaling approach is applied to the Amazon Basin, utilizing four different fine-scale datasets: WGHM, PCR-GLOBWB, SURFEX-TRIP, and the ensemble of flux data and soil moisture data from GLEAM and ASCAT. Validation is conducted against two independent datasets: space-based Surface Water Storage Change (SWSC) and GPS-observed Vertical Crustal Displacement Change (VCDR). In SWSC validation, downscaled results capture spatial variations in river storage with high CC and a relative RMSE of 26%. VCDR validation involves two analyses: comparing GPS-VCDR with TWSF-based VCDR using Green’s function convolution, where downscaled products yield RMSE values between 2.27 and 5.65 mm/month, outperforming input fine-scale data with 14 mm/month RMSE. In terms of CC, downscaled results achieve an average value of -0.81 versus -0.73 for the input. The proposed Bayesian framework effectively downscales GRACE data, with performance highly dependent on input data quality. The copula-supported Bayesian approach offers valuable uncertainty quantification even with inconsistent input data. This method aids in understanding water storage variations in small catchments, supporting local hydrological studies, and can be applied to other water cycle parameters as an alternative to traditional methods. Although a direct posterior is obtained for each grid cell in the downscaling study, spatial dependencies among neighboring grid cells are not considered. Graphical models are particularly well-suited for capturing such spatial dependencies. To address this limitation - and inspired by the challenge of noisy water level estimates from satellite altimetry over inland water bodies - the third study presents a Bayesian approach that formulates a probabilistic graphical model known as a Markov Random Field (MRF), with a Maximum A Posteriori estimation of the MRF (MRF-MAP) as the objective. There to improve inland altimetry, a retracking method is proposed. Unlike conventional retracking methods that target a single waveform point, a holistic approach by identifying retracking lines within 2D radargrams, treating the radargram as a segmented image. This segmentation divides the radargram into Front and Back segments, resembling a binary image segmentation task. The proposed MRF-MAP framework uses spatial dependencies as prior information, with the likelihood based on the temporal evolution of pixel labels across groundtrack cycles. Two temporal energy functions are applied: 1D, based solely on pixel intensity, and 2D, which includes both intensity and bin values, with the posterior probability maximized using the maxflow algorithm. The maxflow algorithm is then applied to obtain MAP solution, yielding a segmented radargram where the retracking line is defined as the boundary between segments. The proposed retracker method is applied to both pulse-limited and SAR altimetry datasets across nine U.S. lakes and reservoirs with varying altimetry characteristics. Validated against in situ data, the proposed method improves RMSE by approximately 0.25 m with the 1D temporal energy function and 0.51 m with the 2D function. The main advantage of the proposed method is its robustness against unexpected waveform variations, making it especially valuable for complex radargrams where conventional retrackers often deliver outliers. By integrating both spatial and temporal information, this method offers a more comprehensive understanding of the data and has broad applicability, such as improving the classification of SWOT pixel cloud points by incorporating spatiotemporal detail. Through these case studies, the thesis illustrates the advantages of Bayesian approaches in improving the accuracy and reliability of hydrological estimates - such as river discharge, terrestrial water storage, and water level measurements - derived from spaceborne geodetic sensors. By integrating theoretical insights with practical applications, the thesis demonstrates how Bayesian methods can effectively improve spatiotemporal resolution, obtain uncertainties, enhance data fusion, and accommodate the complexities inherent in hydrological systems. This combination of foundational knowledge and real-world examples shall establish a base for advancing the use of Bayesian approaches in hydrogeodetic research and beyond. Moreover, by highlighting the challenges in hydrogeodesy, this thesis provides a clear direction for future research and development in the field. It emphasizes critical areas requiring attention, such as improving the spatial and temporal resolution of hydrological estimates, addressing inherent uncertainties in geodetic observations, and developing more effective methods for assimilating diverse data sources. The thesis encourages the refinement of geodetic data processing techniques and the adoption of probabilistic frameworks, such as Bayesian modeling, in future work. Building upon the work presented here, future studies can ultimately achieve more accurate and reliable insights into the Earth's systems.