Post-Flood Channel Mapping in Arid Northern Oman: A comparison of Optical and SAR based approaches

In April 2024, an extreme flash-flood event occurred in northern Oman. It was prompted by rainfall exceeding one to two years of the regional average within 24 hours. This study assesses three remote-sensing approaches for mapping flood-activated channels in an arid environment: Sentinel-2 Tasseled Cap Transformation (TCT) Brightness, Sentinel-1 amplitude change detection (ACD), and Sentinel-1 interferometric coherence change detection (CCD). The analysis encompassed multi-temporal optical and SAR datasets as well as hydrological terrain indices derived from TanDEM-X elevation data.

TCT and ACD were conducted via the Google Earth Engine API using the harmonized Sentinel-2 surface reflectance collection and radiometrically and terrain corrected Sentinel-1 GRD data. The CCD processing was implemented using a hybrid workflow combining the pyroSAR Python API and the Sentinel Application Platform (SNAP), integrated within an Open Data Cube environment. Long temporal baseline coherence was estimated using annual November acquisitions from 2015–2023. Flood-induced changes were isolated using short (12-day) temporal baseline SAR coherence centred on the April 2024 event and compared to InSAR coherence under stable conditions.

Results show that CCD provides the clearest and most spatially consistent delineation of flood-activated channels. Coherence differences within active channels decreased by up to 0.6 compared to stable conditions, clearly distinguishing disturbed surfaces. The robustness of CCD was verified through a sensitivity analysis. It is less affected by noise than ACD and is effective in integrating flood-related changes over time into a single product. TCT Brightness successfully highlighted bleaching of alluvial deposits under clear-sky conditions, while ACD was most informative where surface water persisted at the time of SAR acquisition.

The combined analysis demonstrates that Sentinel-1 CCD, supported by optical data and terrain metrics, offers a robust and transferable approach for post-event flood mapping in arid regions. Its compatibility with Sentinel-1 acquisition strategies makes it particularly suitable for rapid flood assessment in the context of increasingly frequent extreme rainfall events in arid environment. Integrating DEM-derived morphometrics with event-based observations will allow for identification of where DEM-based channel predictions remain robust and where morphological updating is required.