Time Series Interferometric Synthetic Aperture Radar (TSInSAR) methods have been widely and successfully applied for spatiotemporal ground deformation monitoring. The main groups of methodological approaches are often referred to as Persistent Scatterer (PS), Small Baseline (SB), and hybrid approaches that incorporate PS and SB concepts. While TSInSAR techniques have long been able to provide accurate deformation rates for various applications, their corresponding performance in complex environments such as mining areas has to be investigated. This study focuses on comparing the performance of three open source TSInSAR toolboxes (Stamps, Giant, Mintpy) over an extended region that includes an active opencast coal mine. We present the deformation results of each TSInSAR method on a Sentinel-1 dataset of 125 acquisitions spanning around 2.5 years over the Ptolemaida-Florina coal mine site that is characterized by several environmental and surface deformation conditions. First, a cross-comparison analysis is presented over different land cover classes. The study shows that all TSInSAR methods are capable for generating similar ground deformation results when the area has stable ground scattering conditions and the dataset sufficient temporal sampling. The most controversial results between TSInSAR approaches were found in land cover classes that include medium to high vegetation. An external comparative analysis between the different results from TSInSAR methods and leveling measurements is also performed. Stamps approach presented the best agreement with the in-situ deformation rates. The Giant approach yielded the best cumulative deformation results due to our a priori knowledge of temporal behavior of deformation in the vicinity of the leveling locations. Finally, we discuss the main pros and cons of each TSInSAR approach and we highlight the importance of comparison analysis that can provide insights and can lead to better interpretation of the results.

This study investigates the potential of Sentinel-1 data in assisting flood modeling procedures. Two different synthetic aperture radar (SAR) processing methodologies, one simplified based on single-flood image thresholding and one automatic based on SAR statistical temporal analysis, were exploited to delineate the flooding caused by a storm event that took place in Spercheios River, Central Greece. The storm event was simulated by coupling a HEC-HMS hydrologic model and an integrated 1D/2D HEC-RAS hydraulic model. Both SAR methodologies were compared to each other and also used as a reference to test the sensitivity of the hydraulic model in the variation of upstream discharge and roughness coefficient. Model sensitivity was investigated with respect to the change in the derived inundation extent and three additional metrics: the Critical Success Index (CSI), the Hit Rate (HR), and the False Alarm Ratio (FAR). The model response was found to be affected in the following order: by the upstream inflow, and by the variation of the roughness coefficient in the main channel and in the land use “cultivated crops”. The discrepancies observed between model- and SAR-derived inundation products are associated with the uncertainty accompanying the SAR processing and the utilized satellite data itself, the underlying topography, and the structural uncertainty inherent in the modeling procedure. Regarding the SAR methodologies tested, the second one (FLOMPY approach) proved to be more suitable, yielding a more coherent and realistic flooded area. According to the applied metrics and considering as reference the FLOMPY result, model performance ranged between 22–27.5% (CSI), 36.9–60.4% (HR), and 62.1–68.2% (FAR).

A new automatic, free and open-source python toolbox for the mapping of floodwater is presented. The output of the toolbox is a binary mask of floodwater at a user-specified time point within geographical boundaries. It exploits the high spatial (10m) and temporal (6 days per orbit over Europe) resolution of Sentinel-1 GRD intensity time series and is based on four processing steps. In the first step, a selection of Sentinel-1 images related to pre-flood (baseline) state and flood state is performed. In the second step, the preprocessing of the selected images is performed in order to create a co-registered stack with all the pre-flood and flood images. In the third step, a statistical temporal analysis is performed and a t-score map that represents the changes due to a flood event is calculated. Finally, in the fourth step, a classification procedure based on the t-score map is performed to extract the final flood map. A thorough analysis based on several flood events is presented to demonstrate the main benefits, limitations and the potential of the proposed methodology. The validation was performed using Copernicus Emergency Management Service (EMS) products. In all case studies, overall accuracies were higher than 0.95 with Kappa scores higher than 0.76. We believe that the end-user community can benefit by exploiting the flood maps of the proposed methodological pipeline by using the provided open-source toolbox.