Service for automated processing and correction of DInSAR deformation maps

One of the enduring facets within contemporary monitoring systems resides in the automation of data processing. This methodology ensures expeditious access to the most current and objectively derived results. Several systems for terrain monitoring have been realised in the last few years, with the leading role of the European Ground Motion Service (EGMS), part of the Copernicus program. Still most of these systems rely on the usage of the advanced Interferometric Synthetic Aperture Radar (InSAR) techniques which are not capable of exploring more dynamic and complex terrain change patterns as those related to underground mining works in Central Europe. A extensive study within the frames of the Polish realisation of the European Plate Observing System (EPOS) project, comprising long term monitoring between the years of 2016 and 2023 revealed the necessity of usage of the classical DIfferential InSAR (DInSAR) for more detailed study of the processes happening in the area of the Upper Silesian Coal Basin (USCB) in Poland. Within the project EPOS-PL+ we have developed an automated system for DInSAR processing of SAR data from Sentinel-1 satellite. The system also includes modules for processing of third party mission X-band data. 
This processing approach excels in managing significant deformations with reduced coherence, unlike methods relying on stable scatterers. The automated framework encompasses data retrieval, Line of Sight (LOS) deformation computation, trend elimination for atmospheric correction, and assessment of interferogram quality. The final step involves decomposing the LOS deformation into vertical and east-west components.
Upon initiation of the application, the user delineates parameters such as the region of interest by a shapefile, the period of study, and ascending and descending orbits. Subsequently, ingress into the Alaska Satellite Facility service repository, and data is procured for subsequent processing utilising the DInSAR method facilitated by the snappy library. This library enables script-based manipulation of the SNAP program using the Python language.
The subsequent phase involves detrending the data. Raw 1D deformation maps exhibit discernible trends, primarily attributable to atmospheric variations between successive acquisitions. To overcome this problem, a plane is fitted to the deformation data, and the estimated values are differentially subtracted from the original dataset. This estimation is implemented through two distinct methodologies. The more intricate approach include sthe identification of stable points based on nine coherence maps correlating with the deformation values, followed by the fitting of a plane. The simpler approach involves the fitting of a plane to the entire set of deformation data.
The quality check stage involves examining the dataset's pixels for coherence levels exceeding a set threshold (e.g., 0.2). Pixels failing coherence criteria are excluded, and linear interpolation is applied only to selected pixels. This approach minimizes phase unwrapping errors' propagation and effectively removes atmospheric effects in the final analysis. In instances of significant data gaps, the ensemble of adjacent images used for interpolation is expanded to reduce the impact of individual map errors. The enhanced DInSAR data are then projected into 2D components, namely the vertical and east-west (horizontal) dimensions.