Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.

Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2 ), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.

Verschiedene Konzepte der Röntgenmikroskopie haben sich mittlerweile im Labor etabliert und ermöglichen heute aufschlussreiche Einblicke in eine Vielzahl von Probensystemen. Der „Labormaßstab“ bezieht sich dabei auf Analysemethoden, die in Form von einem eigenständigen Gerät betrieben werden können. Insbesondere sind sie unabhängig von der Strahlerzeugung an einer Synchrotron-Großforschungseinrichtung und einem sonst kilometergroßen Elektronen-speicherring. Viele der technischen Innovationen im Labor sind dabei ein Transfer der am Synchrotron entwickelten Techniken. Andere wiederum basieren auf der konsequenten Weiterentwicklung etablierter Konzepte. Die Auflösung allein ist dabei nicht entscheidend für die spezifische Eignung eines Mikroskopiesystems im Ganzen. Ebenfalls sollte das zur Abbildung eingesetzte Energiespektrum auf das Probensystem abgestimmt sein. Zudem muss eine Tomographieanalage zusätzlich in der Lage sein, die Abbildungsleistung bei 3D-Aufnahmen zu konservieren. Nach einem Überblick über verschiedene Techniken der Röntgenmikroskopie konzentriert sich die vorliegende Arbeit auf quellbasierte Nano-CT in Projektionsvergrößerung als vielversprechende Technologie zur Materialanalyse. Hier können höhere Photonenenergien als bei konkurrierenden Ansätzen genutzt werden, wie sie von stärker absorbierenden Proben, z. B. mit einem hohen Anteil von Metallen, zur Untersuchung benötigt werden. Das bei einem ansonsten idealen CT-Gerät auflösungs- und leistungsbegrenzende Bauteil ist die verwendete Röntgen-quelle. Durch konstruktive Innovationen sind hier die größten Leistungssprünge zu erwarten. In diesem Zuge wird erörtert, ob die Brillanz ein geeignetes Maß ist, um die Leistungsfähigkeit von Röntgenquellen zu evaluieren, welchen Schwierigkeiten die praktische Messung unterliegt und wie das die Vergleichbarkeit der Werte beeinflusst. Anhand von Monte-Carlo-Simulationen wird gezeigt, wie die Brillanz verschiedener Konstruktionen an Röntgenquellen theoretisch bestimmt und miteinander verglichen werden kann. Dies wird am Beispiel von drei modernen Konzepten von Röntgenquellen demonstriert, welche zur Mikroskopie eingesetzt werden können. Im Weiteren beschäftigt sich diese Arbeit mit den Grenzen der Leistungsfähigkeit von Transmissionsröntgenquellen. Anhand der verzahnten Simulation einer Nanofokus-Röntgenquelle auf Basis von Monte-Carlo und FEM-Methoden wird untersucht, ob etablierte Literatur¬modelle auf die modernen Quell-konstruktionen noch anwendbar sind. Aus den Simulationen wird dann ein neuer Weg abgeleitet, wie die Leistungsgrenzen für Nanofokus-Röntgenquellen bestimmt werden können und welchen Vorteil moderne strukturierte Targets dabei bieten. Schließlich wird die Konstruktion eines neuen Nano-CT-Gerätes im Labor-maßstab auf Basis der zuvor theoretisch besprochenen Nanofokus-Röntgenquelle und Projektionsvergrößerung gezeigt, sowie auf ihre Leistungsfähigkeit validiert. Es ist spezifisch darauf konzipiert, hochauflösende Messungen an Materialsystemen in 3D zu ermöglichen, welche mit bisherigen Methoden limitiert durch mangelnde Auflösung oder Energie nicht umsetzbar waren. Daher wird die praktische Leistung des Gerätes an realen Proben und Fragestellungen aus der Material¬wissenschaft und Halbleiterprüfung validiert. Speziell die gezeigten Messungen von Fehlern in Mikrochips aus dem Automobilbereich waren in dieser Art zuvor nicht möglich.

Metallic nanostructures possess the ability to support resonances in the visible wavelength regime which are related to localized surface plasmons. These create highly enhanced electric fields in the immediate vicinity of metal surfaces. Nanoparticles with dipolar resonance also radiate efficiently into the far-field and hence serve as antennas for light. Such optical antennas have been explored during the last two decades, however, mainly as standalone units illuminated by external laser beams and more recently as electrically driven point sources, yet merely with basic antenna properties. This work advances the state of the art of locally driven optical antenna systems. As a first instance, the electric driving scheme including inelastic electron tunneling over a nanometer gap is merged with Yagi-Uda theory. The resulting antenna system consists of a suitably wired feed antenna, incorporating a tunnel junction, as well as several nearby parasitic elements whose geometry is optimized using analytical and numerical methods. Experimental evidence of unprecedented directionality of light emission from a nanoantenna is provided. Parallels in the performance between radiofrequency and optical Yagi-Uda arrays are drawn. Secondly, a pair of electrically connected antennas with dissimilar resonances is harnessed as electrodes in an organic light emitting nanodiode prototype. The organic material zinc phthalocyanine, exhibiting asymmetric injection barriers for electrons and holes, in conjunction with the electrode resonances, allows switching and controlling the emitted peak wavelength and directionality as the polarity of the applied voltage is inverted. In a final study, the near-field based transmission-line driving of rod antenna systems is thoroughly explored. Perfect impedance matching, corresponding to zero back-reflection, is achieved when the antenna acts as a generalized coherent perfect absorber at a specific frequency. It thus collects all guided, surface-plasmon mediated input power and transduces it to other nonradiative and radiative dissipation channels. The coherent interplay of losses and interference effects turns out to be of paramount importance for this delicate scenario, which is systematically obtained for various antenna resonances. By means of the here developed semi-analytical toolbox, even more complex nanorod chains, supporting topologically nontrivial localized edge states, are studied. The results presented in this work facilitate the design of complex locally driven antenna systems for optical wireless on-chip communication, subwavelength pixels, and loss-compensated integrated plasmonic nanocircuitry which extends to the realm of topological plasmonics.

Ongoing changes in spaceflight – continuing miniaturization, declining costs of rocket launches and satellite components, and improved satellite computing and control capabilities – are advancing Satellite Formation Flying (SFF) as a research and application area. SFF enables new applications that cannot be realized (or cannot be realized at a reasonable cost) with conventional single-satellite missions. In particular, distributed Earth observation applications such as photogrammetry and tomography or distributed space telescopes require precisely placed and controlled satellites in orbit. Several enabling technologies are required for SFF, such as inter-satellite communication, precise attitude control, and in-orbit maneuverability. However, one of the most important requirements is a reliable distributed Guidance, Navigation and Control (GNC) strategy. This work addresses the issue of distributed GNC for SFF in 3D with a focus on Continuous Low-Thrust (CLT) propulsion satellites (e.g., with electric thrusters) and concentrates on circular low Earth orbits. However, the focus of this work is not only on control theory, but control is considered as part of the system engineering process of typical small satellite missions. Thus, common sensor and actuator systems are analyzed to derive their characteristics and their impacts on formation control. This serves as the basis for the design, implementation, and evaluation of the following control approaches: First, a Model Predictive Control (MPC) method with specific adaptations to SFF and its requirements and constraints; second, a distributed robust controller that combines consensus methods for distributed system control and $H_{\infty}$ robust control; and finally, a controller that uses plant inversion for control and combines it with a reference governor to steer the controller to the target on an optimal trajectory considering several constraints. The developed controllers are validated and compared based on extensive software simulations. Realistic 3D formation flight scenarios were taken from the Networked Pico-Satellite Distributed System Control (NetSat) cubesat formation flight mission. The three compared methods show different advantages and disadvantages in the different application scenarios. The distributed robust consensus-based controller for example lacks the ability to limit the maximum thrust, so it is not suitable for satellites with CLT. But both the MPC-based approach and the plant inversionbased controller are suitable for CLT SFF applications, while showing again distinct advantages and disadvantages in different scenarios. The scientific contribution of this work may be summarized as the creation of novel and specific control approaches for the class of CLT SFF applications, which is still lacking methods withstanding the application in real space missions, as well as the scientific evaluation and comparison of the developed methods.