Land subsidence is a complicated phenomenon, relevant in the Holocene coastal-plain of the Netherlands, that can be triggered by many different mechanisms and components. To cope with the effects of land subsidence, detailed information on which mechanisms are causing subsidence and its variation over time and space are needed. Subsidence studies predominantly focus on grass and agricultural areas with organic soils above the groundwater level. In this study, subsidence is also evaluated for dikes and the urban environment. It is attempted to model subsidence behaviour of four different projects, grass plots within the Krimpenerwaard polder, locations along the N3 Dordrecht, an embankment constructed for the A5 Badhoevedorp and locations along the Markermeerdikes, and to verify these model results with subsidence measurements available. Existing one-dimensional models for different subsidence components are used in the evaluations. For grass and agricultural areas it is found that subsidence behaviour could be modelled and the results lie within the range of uncertainty of the subsidence measurements used. However, the equations used are often empirical and do not include all relevant influencing factors and couplings. Compression by degradation of organic material as a response to drainage of organic soil is the main subsidence component in these areas. Anaerobic degradation of organic material is also included in the modelling approach, a component that is often neglected in other subsidence studies. Two infrastructure related projects provided insight to subsidence components in the urban environment. Compression by loading is the main subsidence component relevant in this area type, and thus settlement models are suitable to approximate the subsidence behaviour. However, a slight underestimation of creep shows that the existence of a small additional subsidence component for a situation with soft soil layers pushed below the water table could not be excluded. Unravelling subsidence behaviour from dikes showed to be more complicated, where an interplay between different components is relevant. Based on the evaluation in this study compression by loading and compression by an oxidation or shrinkage component are indicated the main subsidence components. Many uncertainties and assumptions used in this evaluation influenced the results. For all area types disentangling subsidence into contributions of separate components based on a total measured subsidence signal causes large uncertainties, as modelled subsidence contributions for individual components could not be verified.

Providing farmers with the tools to monitor their crops continuously and reliably can aid the scaling of global food production to meet the ever-growing demand. The optical Normalised Difference Vegetation Index (NDVI) is commonly used to monitor crop greenness as an indicator for biomass, but it is limited by clouds and signal saturation. Synthetic Aperture Radar (SAR) imagery, which is not hampered by clouds, can be used to complement the NDVI. The Biomass Proxy (BP) combines the NDVI and SAR data, but spatial biomass estimations still strongly depend on the NDVI and therefore face the same limitations. This study aims to assess the potential value of spatial SAR data to approximate the in-field biomass distribution. The SAR signal from Sentinel-1 and the NDVI signal from Sentinel-2 were analysed temporally and spatially for fields of maize, barley, oat, and spring wheat in the Dutch province of Flevoland. It was assumed that consistent SAR patterns in the spatial signal correspond to biophysical changes in the monitored crops. A framework to detect these patterns and include them in the BP was created based on combining cluster detection with spatial autocorrelation. The components of this framework demonstrate that backscatter intensity, phenological stage and crop type influence the probability of consistent patterns and that consistent patterns could not be observed from the spatial NDVI signal. Moreover, it was found that the BP’s sensitivity to the input signals depends on crop type. With the knowledge of when and where consistent patterns occur, targeted research can be done to understand the spatial SAR signal better and, thereby, optimally use all available information.

Satellite radar interferometry (InSAR) is a powerful technique for monitoring deformation phenomena. While deformation phenomena occur in a three-dimensional (3D) world, one of the limitations of the InSAR phase observations is that they are only sensitive to the projection of the 3D displacement vector onto the radar line-of-sight (LoS) direction. To uniquely estimate the three displacement components, we would require at least three sets of spatiotemporally coinciding independent (STCI) LoS observations, (i.e., scatterers on an object that is not subject to internal deformations, observed at the same time) available over the same Region of Uniform Motion (RUM). More importantly, the system of equations needs to have a full rank coefficient matrix. Unfortunately, in most practical situations at most two sets of STCI LoS observations are available, resulting in an underdetermined system with an infinite amount of possible solutions. Within the InSAR literature we encounter different approaches to address the underdeterminancy problem, unfortunately often with either mathematical or semantic flaws. We concluded that the InSAR community has no uniform way of addressing the underdeterminancy problem. We developed a taxonomy for the different fallacious approaches that can help by evaluating InSAR results and reviewing InSAR papers. Moreover, using the east-north-up (ENU) reference frame for decomposing the LoS observations provides results that are not tuned to the needs of the end-user of an InSAR product. Therefore, we developed an alternative solution to the underdetermined problem, in the form of a `strap-down' approach, which uses a local strap-down reference system that is fixed to the deformation phenomenon with transversal, longitudinal, and normal (TLN) components. For many practical cases, such as line-infrastructure, landslides, or subsidence bowls, analysis of the main driving forces supports the assumption that significant deformations in the longitudinal direction are unlikely. We found that using the strap-down approach gives physically more relevant estimates. Moreover, it results in more relevant estimates since it properly includes all uncertainties. We can further conclude that the conventional way of communicating (PS)-InSAR results by means of a `dot distribution map' is sub-optimal when considering the quality of the estimates. For many cases, `vector arrow maps', or traditional geodetic vector-based visualizations, including error ellipses are a viable and more optimal alternative.

ISAR imaging is a well known technique which uses range and Doppler information from a radar to generate an image of a moving target. Research performed on the combination of a large receive array with airborne ISAR is scarce. This thesis is aimed to reach the highest possible image quality for AMBER, a radar with a 24 element receive array which was developed by TNO. Optimizing the image quality was split up in two parts, motion compensation and clutter suppression. A literature study is performed to research the existing methods. A performance evaluation is performed on different motion compensation techniques based on a simulation which is set up to resemble the radar parameters of AMBER. Based on the results of this evaluation, keystone formatting combined with image contrast maximization is concluded to be the best fitting approach. For clutter suppression a similar approach is followed with real measured data. Apart from the existing techniques, MVDR, DPCA and ODPCA, a new technique is proposed which filtered a target from clutter based on motion. It is referenced to as the IDPCA. The techniques show better image quality than regular beam forming. To enhance the image quality more, a method is searched to combine different clutter suppression techniques. This leads to a unique method which is proposed in this thesis. The new technique exploits the circular phase variance of pixels in the range Doppler image between different sub arrays. With this approach, strong clutter can be filtered in the range Doppler domain based on its angle of arrival. The newly proposed technique is applied as a mask, as it does not contain amplitude information of the target. Combined with the ODPCA method, the final image is generated. The combination of techniques shows clear improvements from the other discussed approaches.

From three coherent SAR images it is possible to estimate three interferograms. Combined in a circular way, the sum of the three interferometric phases is called the closure phase which necessarily adds up to zero on a single pixel level. However, if the interferograms are spatially averaged, phase consistency is not guaranteed. In most of the interferometric studies, those mismatches were assumed to be caused by decorrelation noise alone, and were either not considered or deemed negligible, eluding further investigations of its origin. However, recent publications have confirmed that inconsistent phase closures are systematic and not the exception, pointing to an underlying geophysical cause. Comparisons of the spatial signatures of phase closures with land cover maps suggest a spatial and temporal correlation that is related to the characteristics of different land cover types. Since interferometric measurements are sensitive to variations of the dielectric constant, those similarities have been attributed to dynamics in vegetation and soil moisture. A closure phase significance test developed at the Geoscience and Remote Sensing department at TU Delft aimed to increase the signal-to-noise ratio of this geophysical signal component by providing a significance ratio for phase closures. However, the sensitivity of (significant) phase closures to dynamics in vegetation and soil over different land cover types has not been assessed yet. Here we show that with enough averaging of the interferometric phase, the spatial and temporal characteristics of closure phase can be used to distinguish between different land cover types. We found that the degree of spatial averaging has a significant impact on both the phase closure values and its spatial and temporal consistency. The magnitudes of significant phase closures generally increased over low-vegetated land covers, suggesting that closure phases are most sensitive to soil moisture dynamics, whereas vegetation cover was associated with decreasing phase closure magnitudes and spatial inconsistency. Besides spatial averaging, significant differences were observed between closure phases from different polarizations. Furthermore, we found that amplitude backscatter and closure phase are spatially and temporally correlated, pointing to similar influencing mechanisms. Our results demonstrate the importance of applying a closure phase significance test and describe the effect of spatial averaging on the characteristics of phase closures with respect to different land cover types. We anticipate this study to provide useful steps towards using the closure phase for soil and vegetation monitoring in the future. For example, the findings could be used to further exploit potential synergies with amplitude backscatter for soil moisture retrieval from closure phase or develop more sophisticated methods for land cover mapping using InSAR. If not used for applications linked to land cover, vegetation or soil, being able to better predict the effect of those parameters on the interferometric phase and coherence, eventually enables to separate their contribution from other signals, such as deformation estimates. Additional research is needed to relate significant phase closures to moisture changes in vegetation.

Stereo Thermo-Optically Enhanced Radar for Earth, Ocean, Ice, and land Dynamics (STEREOID) is one of the candidates of the ESA (European Space Agency) , Earth Explorer 10 missions. The novel constellation system will consist of the active Sentinel-1 satellites and two passive spacecrafts, which can provide flexible baseline configurations. The main objective of the mission lies in monitoring the variation of spatially diverse ice sheets, the eruptions of earthquakes, the volcano activities, and the landslides, playing therefore an extremely important role in understanding the global climate dynamics and the geophysical processes involved. The purpose of the thesis is to develop an end-to-end simulator incorporating the STEREOID bistatic configuration operating in TOPS ( Terrain Observation by Progressive Scans) mode and evaluate its performance. To achieve this goal, the key component of the simulator, the SAR (Synthetic Aperture Radar) processing kernel was first implemented. The kernel employs an imaging algorithm which assists in image formation and focusing for different bistatic geometries generated by relevant working modes of the STEREOID mission. This is further extended to bistatic TOPS acquisition mode with azimuth beamforming under dual antenna receiver configuration of STEREOID. The performance of STEREOID mission is evaluated under different bistatic geometries and the dual beamforming strategy is evaluated for parameters such as resolution, pointing errors and gain imbalances. This is evaluated to analyse and understand the importance of calibration errors introduced into the system.