Sentinel-1-based flood mapping: a fully automated processing chain

TLDR

The study introduces an automated Sentinel‑1 processing chain for near‑real‑time flood detection and monitoring. The chain automatically ingests Sentinel‑1 data, processes it without user intervention, and delivers flood maps in under 45 min, enabling a web‑based service that continuously informs users of current flood conditions. Accuracy tests on two Greek‑Turkish border sites yielded overall accuracies of 94.0–96.1 % (κ = 0.879–0.910), with VV polarization slightly outperforming VH under calm wind conditions.

Abstract

This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-real-time (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in less than 45 min after a new data set is available on the Sentinel Data Hub of the European Space Agency (ESA). Due to the systematic acquisition strategy and high repetition rate of Sentinel-1, the processing chain can be set up as a web-based service that regularly informs users about the current flood conditions in a given area of interest. The thematic accuracy of the thematic processor has been assessed for two test sites of a flood situation at the border between Greece and Turkey with encouraging overall accuracies between 94.0% and 96.1% and Cohen's kappa coefficients (κ) ranging from 0.879 to 0.910. The accuracy assessment, which was performed separately for the standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that under calm wind conditions, slightly higher thematic accuracies can be achieved by using VV instead of VH polarization data.