Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption

Abstract

Volcanic ash deposits affect buildings, vegetation, and population. After a volcanic eruption, it is critical to detect the areas affected by ash deposits to achieve an advisable management of the emergency. Optical sensors have been widely used to carry out this task, but they are limited by solar illumination and weather conditions. As an alternative, Synthetic Aperture Radar (SAR) data are not affected by those limitations. Recently, a Temporal Decorrelation Model (TDM) that uses SAR data was proposed for detecting and mapping ash deposits, but it has only been applied to L-band data. Today there is available a huge quantity of C-band data acquired by the Sentinel-1 constellation. In this study we applied the TDM to Sentinel-1 data in order to assess its performance for detecting volcanic ash deposits after an eruption. We selected the eruption of Taal volcano in The Philippines on January 12, 2020, as our case study. We computed more than 4000 interferometric pairs from a dataset of 93 images acquired before, during, and after the eruption. Our results show that TDM can be applied to C-band data, despite the higher temporal decorrelation suffered by them. Our final probability map is consistent with the field evidence reported by the Philippines Institute of Volcanology and Seismology (PHILVOLCS) and the isopachs map reported in the literature. This new application provides a novel framework for the coherence exploitation of C-Band data. Also, this approach could be applied to detection and monitoring of other natural disasters.