InSAR enables the estimation of spatio-temporal displacements, relative to a reference point and a reference epoch, here defined as the mother image. When dealing with time series, there are several options to treat the mother image in computing and plotting the temporal phase differences, producing distinctly different results, in terms of the estimated displacement parameters and their precision. Here we review the three approaches mostly encountered in literature, discuss the implications of the different approaches, and recommend the 'embracing mother' approach for standard InSAR analyses and visualizations.

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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.

The Kathmandu Valley in Nepal is facing the combined effects of population growth, rapid urbanization, economic development, and climate change. This results in serious water management challenges: growing freshwater demands, declining water tables, drying of streams, and deteriorating water quality. Insufficient surface water supplies have led to increased reliance on groundwater, especially during the dry winter and pre-monsoon seasons (November - May). Despite groundwater’s importance, it is sparsely measured, poorly understood, and insufficiently managed. As it is difficult and costly to measure all groundwater extractions in the Valley, a water balance approach is an alternative method to estimate total net groundwater pumping. Therefore, the aim of this research was to develop and evaluate potential methods for quantifying total pre-monsoon baseflow supplies by extrapolating baseflow measurements of a subsample of watersheds to unmeasured watersheds. Estimated baseflow was used, together with other water balance fluxes and changes in storage, to evaluate net groundwater pumping in the Valley. Three different methods were used: (1) Spatial Analysis, (2) Regression Model, and (3) Black Box (machine learning). All methods relied on streamflow data from 2017 to 2019, collected by citizen scientists from S4W-Nepal. Based on the three methods we presented, we cautiously conclude that it is possible to determine the pre-monsoon baseflow contributions from a sub-sample of head water catchments. Total baseflow estimates for the Valley using Spatial Analysis, Regression Model, Black Box were 2.32, 2.30, 2.65 m3/s respectively. These values show orders of magnitude that correspond with expected values. By using the average baseflow values of all three methods, we were able to close the water balance and make an assumption for the net groundwater pumping in the Valley. Based on a population of 3.5 million, a net groundwater extraction of 96 L/person/day during pre-monsoon was found. This striking outcome emphasizes the need for more discharge and groundwater extraction measurements, to decrease the uncertainties and to refine the methods.

Like other cities in Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Unsafe, new settlements are partly located at the floodplains and the government lags behind in implementing proper land-use policy to control unrestrained settlement. The rivers are not only constrained by uncontrolled settlements, but also by insufficient width and freeboard of bridges, and waste blockages causes problems. Combined with more extreme rain events during the monsoon due to climate change, flooding has become a reoccurring problem in Bhaktapur. To gain better understanding of the river and the corresponding flood risk, historical data is essential. Unfortunately, historical databases of water levels are non-existent for this river. Only starting from monsoon 2019, water levels and discharge have been measured on a regular basis. To reconstruct the missing historical data for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ) in combination with citizen science (CS). The objective of this research was to use Structured Expert Judgment in a flood risk analysis for the city of Bhaktapur. As a result of using SEJ, we were able to obtain sufficient water level data and estimate the return levels of extreme water levels of Hanumante river by fitting a Generalized Extreme Value distribution (GEV). This eventually led to a reverse Weibull fit, which in this case does not seem accurate. This research discusses in detail the advantages and issues of using Structured Expert Judgement in situations like this and also discusses the reliability of the results.

It is observed from satellite radar measurements that the dike between Eemshaven and Delfzijl in the north of the Netherlands shows instability. It can be possible that a development of these instability results in dike failure. The two possible failure mechanisms that may occur are settlement and slope-instability. In this research the displacements for different cross sections of the dike were calculated. By analysing the size and the direction of the displacements it is concluded that the dike shows over a length of approximately 110 meters some first indications for the failure mechanism slope-instability. When slope-instability may indeed occur in the future, a slice of soil will slide aside and the dike will fail. This can cause humanitarian and economic consequences. It is therefore necessary to inform the Waterschap Noorderzijlvest who are responsible for the maintenance to the dike.