On-demand Sentinel-1 Interferogram Generation Service for Monitoring of Volcano Deformation

Sentinel-1 interferograms now represent standard products to assess the extent and magnitude of ground deformation in volcanic areas. These measurements provide quantitative constraints on the volume of material accumulating at depth in the magma reservoir, which is essential to anticipate the magnitude of an impending eruption [1]. However, the typical area of interest (AOI) for volcano analysis is much smaller than the 250km x 200km Interferometric Wide (IW) Sentinel-1 SAFE products, the latter spanning three adjacent subswaths and around 30 bursts. Existing InSAR services, such as the SNAPPING service on the Geohazards Exploitation Platform (GEP) [2] or CNES-FormaTerre Flatsim [3], can process entire Sentinel-1 products but do not allow to narrow down the processing to a small AOI within a single or a few consecutive bursts. To complement these already existing general-purpose services, there is a need for the development of an efficient and flexible tool capable of responding to the specific needs of volcano monitoring strategies. Institut de physique du globe de Paris (IPGP) and Université de Lille are developing an online open-access service for on-demand computation of Sentinel-1 interferograms over small AOIs centered on volcanic areas, accessible through a web application. The service back-end is deployed redundantly on the computing cluster S-CAPAD (IPGP) and in the AERIS/ICARE facility (Université de Lille). It relies on the EOS-SAR Python library developed at Kayrros. EOS-SAR implements an accurate Sentinel-1 geometric model [4] accounting for fine timing corrections, which allows to get native co-registration and stitching of bursts, resulting in a time series of well-aligned, geometrically consistent, Sentinel-1 bursts mosaics. The processing can be restricted to arbitrarily small AOIs, within a single or a few consecutive bursts and adjacent sub-swaths, which saves time, computing resources and storage space. The service leverages the Copernicus Data Space Ecosystem (CDSE) S3 object storage service for efficient data access. A Sentinel-1 image crop, located within a burst, can be read from a sub-swath measurement TIFF file, stored on S3, through a single http range request. The service front-end lets users select a volcano of interest, a Sentinel-1 ground track, and the list of dates to process. Existing ground tracks and dates for the selected volcano are retrieved from CDSE catalog APIs. Once the selection is made, a configuration file with the input parameters is sent to the back-end and triggers the processing. After processing completion, results are returned to the user via the interactive web interface, and products (interferograms, coherence maps, orbital fringes, topographic fringes, amplitude maps, etc…) can be downloaded. Planned developments include the optional correction of the atmospheric phase delay from the ERA-5 atmospheric model [5], retrieved via the COPERNICUS Climate Data Store API. The Sentinel-1 interferogram generation service for volcanic areas is developed as part of the “Volcano Space Observatory” platform, funded in the framework of the Horizon Europe, EOSC FAIR-EASE project [6], led by the French Research Infrastructure “Data Terra”. The service aims at offering a practical and efficient solution for the on-demand processing of InSAR products on volcanic targets. Anticipated end-users of the service include volcano observatory teams, scientists and researchers from academia and students training in the field of volcanology and remote sensing. - - - - - - - - - - - - - - - References [1] Shreve, T., Grandin, R., Boichu, M., Garaebiti, E., Moussallam, Y., Ballu, V., ... & Pelletier, B. (2019). From prodigious volcanic degassing to caldera subsidence and quiescence at Ambrym (Vanuatu): The influence of regional tectonics. Scientific Reports, 9(1), 18868. [2] Foumelis, M., Delgado Blasco, J. M., Brito, F., Pacini, F., Papageorgiou, E., Pishehvar, P., & Bally, P. (2022). SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sensing, 14(23), 6075. [3] Thollard, F., Clesse, D., Doin, M. P., Donadieu, J., Durand, P., Grandin, R., ... & Specht, B. (2021). Flatsim: The form@ ter large-scale multi-temporal sentinel-1 interferometry service. Remote Sensing, 13(18), 3734. [4] Akiki, R., Anger, J., de Franchis, C., Facciolo, G., Morel, J. M., & Grandin, R. (2022, July). Improved Sentinel-1 IW Burst Stitching through Geolocation Error Correction Considerations. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 3404-3407). IEEE. [5] Jolivet, R., Grandin, R., Lasserre, C., Doin, M. P., & Peltzer, G. (2011). Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data. Geophysical Research Letters, 38(17).