This interdisciplinary doctoral dissertation addresses land subsidence in different and diverse study cases in the world, employing advanced techniques and methodologies to measure their magnitude and comprehensively explore its causes, and implications. Investigating areas such as the San Luis Potosi metropolitan area, Alaşehir-Sarıgöl sub-basin (ASSB) in Türkiye, and the Alto Guadalentín Valley in Spain, the research unveils critical insights into the complex dynamics of subsidence phenomena. Utilizing advanced remote sensing techniques like Persistent Scatterer Interferometry (PSI) and Coherent Pixels Technique (CPT), the study assesses subsidence rates and correlates them with factors such as trace faults, groundwater extraction, and soft soil thickness. Validation methodologies were developed and proposed to the scientific community on the first stage, integrating Global Navigation Satellite System (GNSS) benchmarks, enhance the reliability of Differential Synthetic Aperture Radar Interferometry (DInSAR) measurements, ensuring a robust foundation for subsequent analyses. The research aims to contribute to the understanding of land subsidence and contribute to create a decision-support framework to mitigate the phenomenon while addressing specific research objectives within each identified topic of inquiry. The research topic 1 includes the “DInSAR for monitoring land subsidence in overexploited aquifers”. In the San Luis Potosi metropolitan area (Mexico), the application of CPT technique reveals intriguing correlations between trace faults, land subsidence, and groundwater extraction. Specifically, areas in the municipality of Soledad de Graciano Sánchez exhibit subsidence values ranging between -1.5 and -3.5 cm/year, while in San Luis Potosi, values range from -1.8 to -4.2 cm/year. The validation of CPT results against five Global Navigation Satellite System (GNSS) benchmarks establishes a robust correlation of 0.986, underlining the reliability of InSAR-derived deformations. Additionally, in regions like the Alaşehir-Sarıgöl sub-basin (Türkiye), where water stress is heightened due to intensive agricultural irrigation, the study explores the roles of tectonic activity and groundwater withdrawal in land subsidence. Utilizing the P-SBAS algorithm, 98 Sentinel-1 SAR images in ascending orbits and 123 in descending orbits were analysed, covering the period from 2016 to 2020. Independent Component Analysis was applied to distinguish long-term displacements from seasonal variations in the DInSAR time series data. Displacement rates of up to -6.40 cm/year were identified, thus, the proposed P-SBAS algorithm facilitates the monitoring of displacement, revealing direct correlations between DInSAR displacement and critical factors like aquitard layer compaction. These findings contribute valuable insights into the dynamic interactions shaping overexploited aquifers. The research topic 2, developing parallelly to topic 1, consists of the “Validation of DInSAR data applied to land subsidence areas”. Addressing the imperative for validation methodologies in subsidence assessments, a systematic approach introduces statistical analyses and classification schemes. This methodology is designed to validate and refine DInSAR data, enhancing the reliability of subsidence assessments. By normalizing Root Mean Square Error (RMSE) parameters with the range and average of in-situ deformation values and employing the squared Pearson correlation coefficient (R²), a classification scheme is established. This scheme facilitates the acceptance/rejection of DInSAR data for further analyses through the application of automatic analysis supported by a Matlab © code, ensuring a more accurate representation of land subsidence phenomena. The research topic 3 covers the exploitation of DInSAR data for assessing flooding potential and determining characteristic parameters of aquifer systems. The first one is “Impact of land subsidence on flood patterns”. The study in the Alto Guadalentín Valley, a region experiencing extreme flash floods jointly with high-magnitude land subsidence, integrates flood event models, Differential interferometric SAR (DInSAR) techniques, and 2D hydraulic flow models. Through Synthetic Aperture Radar (SAR) satellite images and DInSAR, land subsidence's magnitude and spatial distribution are quantified. The results demonstrate significant changes in water surface elevation between the two 1992 and 2016 temporal scenarios, leading to a 2.04 km² increase in areas with water depths exceeding 0.7 m. These outcomes, incorporated into a flood risk map and economic flood risk assessment, underscore the pivotal role of land subsidence in determining inundation risk and its socio-economical implications. The research offers a valuable framework for enhancing flood modelling by considering the intricate dynamics of land subsidence. The second application of DInSAR data is about the “Automatic calculation of skeletal storage coefficients in aquifer systems”. In response to the need for automating data analysis for specific storage coefficients in aquifer systems, a MATLAB© application is introduced. This application streamlines the correlation between piezometric levels and ground deformation, significantly reducing analysis time and mitigating potential human interpretation errors. The developed application integrates temporal groundwater level series from observation wells and ground deformation data measured by in-situ or remote sensing techniques (e.g., DInSAR). Through the automatic construction of stress-strain curves, the application contributes to the estimation of skeletal storage coefficients, offering a valuable tool for evaluating aquifer system behaviours. This comprehensive research, guided by the complexities of these three distinct research topics, yields detailed insights and methodological advancements. By integrating diverse datasets and employing advanced techniques, this dissertation offers a multidimensional understanding of land subsidence dynamics and provides a robust foundation for sustainable groundwater management globally. / This research is funded by the PRIMA Programme supported by the European Union (Grant agreement 1924), project RESERVOIR.