Subsidence is a complex problem, both in a technical sense and in governance. This is particularly the case in the Netherlands, which is a low-lying and densely populated country where various causes of subsidence interfere with each other. Coping with subsidence in the Netherlands started already woo years ago. This long history of subsidence, however, along with its slow manifestation, has resulted in a tendency to adaptation rather than mitigation. There is a growing awareness that this focus on adaptation is actually excluding alternative solutions. Potentially cheaper or more effective options may be unknown and not even considered. At the same time, Dutch society is becoming more aware of the severity of human-induced subsidence as it is one of the most prominent current geological hazards. What is needed therefore, is a sound knowledge base facilitating the exploration of solutions outside the traditional way of thinking. Here we present the different knowledge and governance issues at stake. We start with the description of the natural processes that cause subsidence, and the human-induced causes like groundwater management and exploitation of deep geological resources. Then we elaborate how subsidence can be estimated from measurements. We pay specific attention to the utilization of modern ensemble-based techniques to integrate multiple models and data. The objective is to avoid deterministic predictions and instead produce a range of subsidence forecasts with confidence intervals that are in agreement with observational data and their uncertainties. Finally, we describe how technical knowledge can be integrated in decision making by estimating the costs and benefits of different scenarios, thereby offering an array of options for decision makers. Subsidence will keep playing a role in shaping the future of the Netherlands. Human-induced subsidence will continue with new subsurface activities directed towards the energy transition. Incorporating the grim sea level rise predictions, the issue becomes even more serious. It is therefore of paramount importance to maintain and further develop the current knowledge position and to develop proactive mitigation activities.

Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for the conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by the machine learning algorithms which can create categorical predictions about the relative ambiguity level based on the readily available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum gradient phase unwrapping techniques.