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 Crop water productivity mapping and benchmarking using remote sensing and Google Earth Engine cloud computing(2022) Ghorbanpour, Ali Karbalaye; Kisekka, Isaya; Afshar, Abbas; Hessels, Tim; Taraghi, Mahdi; Hessari, Behzad; Tourian, Mohammad J.; Duan, ZhengScarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49-0.55 (kg/m3) for irrigated wheat, 0.27-0.34 for rainfed wheat, 1.7-2.2 for apples, 1.2-1.7 for grapes, 5.5-6.2 for sugar beets, and 0.67-1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.Item Open Access Understanding the limitations of Sentinel-3 inland altimetry through validation over the Rhine River(2022) Schneider, Nicholas M.Satellite altimetry is developing into one of the most powerful measurement techniques for long-term water body monitoring thanks to its high spatial resolution and its increasing level of precision. Although the principle of satellite altimetry is very straightforward, the retrieval of correct water levels remains rather difficult due to various factors. Waveform retracking is an approach to optimize the initially determined range between the satellite and the water body on Earth by exploiting the information within the power-signal of the returned radar pulse to the altimeter. Several so-called retrackers have been designed to this end, yet remain one of the most open study areas in satellite altimetry due to their crucial role they play in water level retrieval. Moreover, geophysical properties of the stratified atmosphere and the target on Earth have an effect on the travel time of the transmitted radar pulse and can amount to severalmeters in range. In this study we provide an overall analysis of the performances of the retrackers dedicated to the Sentinel-3 mission and the applied geophysical corrections. For this matter, we focus on nine different locations within the Rhine River basin where locally gauged data is available to validate the Sentinel-3 level-2 products. Furthermore, we present a reverse retracking approach in the sense that we use the given in-situ data to determine the offset to each altimetry-derived measurement of every epoch. Under the assumption that these offsets are legitimate, they can be seen as an a-posteriori correction which we project onto the range and thus on a waveform level. Further analyses consist in the investigation of the relationship these a-posteriori corrections have to the waveform properties of the same epoch. Later, the question whether the a-posteriori corrections to the initial retracking gates are appropriate for the retrieval of correct water levels, drives us to assign a probability to each and every bin of the waveform. Following this idea, we design stochastic-based retrackers which determine the retracking gate for water level retrieval from the bin with the highest probability assigned to it. To distribute the probabilities across all bins of the waveform, we consider three empirical approaches that take both the waveform itself and its first derivative into account: Addition, multiplication and maximum of both signals. For all three of the new retrackers, we generate the water level timeseries over the aforementioned sites and validate them against in-situ data and the retrackers dedicated to the Sentinel-3 mission.Item Open Access Orbitverdichtung mittels Kalman-Filterung am Beispiel der Satellitenmission GRACE(2011) Leinss, BenediktDas übergeordnete Thema der vorliegenden Arbeit ist die Verdichtung von Satellitenorbits. Grundsätzlich besteht hierbei die Aufgabe darin, für einen gegebenen Orbit eine korrespondierende Satellitenbahn mit einer höheren zeitlichen Auflösung zu bestimmen. Eine solche Orbitverdichtung ist etwa bei einigen Ansätzen zur Schwerefeldbestimmung aus Level-1B-Daten der Satellitenmission GRACE nötig, weil dabei die GRACE-Navigationslösungen mit den K-Band-Beobachtungen zu kombinieren sind und sich diese Datensätze gerade hinsichtlich ihrer zeitlichen Abtastung unterscheiden. Angesichts dieser Situation wird hier eine Orbitverdichtung mittels der erweiterten Kalman-Filterung (EKF) konzipiert und in einer Matlab*-Toolbox umgesetzt. Neben einer Funktion, mit der sich prinzipiell weitgehend beliebige Orbits verdichten lassen, wird außerdem ein Filter entworfen, das einzig für GRACE ausgelegt ist und es ermöglicht, auch die hochgenauen K-Band-Relativgeschwindigkeiten in die Verdichtung einzubeziehen. Um die Systemzustände und die zugehörigen Kovarianzen zu prädizieren, werden unterschiedliche Orbitintegrationen realisiert. Die meisten davon basieren auf der numerischen Integration von gewöhnlichen DGL-Systemen erster Ordnung in kartesischen, quasi-inertialen Koordinaten. Der hier komplexeste Integrator trägt den Störbeschleunigungen aufgrund der Anisotropie des Erdgravitationsfelds Rechnung, indem geeignete Gravitationsfeldmodelle genutzt werden. Sämtliche Orbitintegratoren und die Routinen zur EKF-Orbitverdichtung werden mithilfe simulierter Daten durch Tests verifiziert. Außerdem vergleicht man die verschiedenen Prädiktionsmethoden miteinander im Hinblick auf die erzielten Genauigkeiten und deren Berechnungseffizienz. Bei der gegen Ende durchgeführten Prozessierung realer GRACE-Daten zeigt sich, wie auch bei den vorherigen Tests, dass es dank der Kalman-Filterung gelungen ist, verdichtete GRACE-Orbits zu berechnen, welche erheblich besser zu den gemessenen K-Band-Relativgeschwindigkeiten passen als die gegebenen Navigationslösungen.Item Open Access Forecasting next year's global land water storage using GRACE data(2024) Li, Fupeng; Kusche, Jürgen; Sneeuw, Nico; Siebert, Stefan; Gerdener, Helena; Wang, Zhengtao; Chao, Nengfang; Chen, Gang; Tian, KunjunExisting approaches for predicting total water storage (TWS) rely on land surface or hydrological models using meteorological forcing data. Yet, such models are more adept at predicting specific water compartments, such as soil moisture, rather than others, which consequently impedes accurately forecasting of TWS. Here we show that machine learning can be used to uncover relations between nonseasonal terms of Gravity Recovery and Climate Experiment (GRACE) derived total water storage and the preceding hydrometeorological drivers, and these relations can subsequently be used to predict water storage up to 12 months ahead, and even exceptional droughts on the basis of near real‐time observational forcing data. Validation by actual GRACE observations suggests that the method developed here has the capability to forecast trends in global land water storage for the following year. If applied in early warning systems, these predictions would better inform decision‐makers to improve current drought and water resource management.