Automated flood mapping and monitoring using Sentinel-1 data

Abstract

Flood extent maps derived from Synthetic Aperture Radar (SAR) data can be a key information source for an effective disaster management, helping humanitarian relief organizations and decision makers to obtain spatially-explicit information about inundated areas in a time- and cost-efficient manner. In comparison to manual or semi-automatic flood mapping approaches often utilized in the framework of rapid mapping activities, automatic SAR-based processing chains can substantially reduce the critical time delay between the delivery of satellite data after a crisis event and the subsequent provision of satellite derived crisis information (e.g. the extent of a flood situation) to emergency management authorities. In this work, an automated Sentinel-1 based processing chain designed for flood detection and monitoring in near real-time (NRT) is presented. The work is based upon a TerraSAR-X based flood service which has been adapted to Sentinel-1, including a number of enhancements for improving both robustness and thematic accuracy. 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 minutes after a new dataset is available on the ESA Sentinel Data Hub. Due to the systematic acquisition strategy and high repeat rate of Sentinel-1, the processing chain can be set up as a web-based service which regularly informs users about the current flood conditions in a given area of interest. The processing chain is composed of the following main elements: a) automatic data ingestion through a Python-based script which routinely queries the ESA Sentinel Data Hub for new acquisitions matching user-defined criteria and subsequently downloads them, b) geometric correction and radiometric calibration using the graph processing tool (GPT) of the ESA Sentinel-1 toolbox (S1TBX) which is embedded in the Sentinel Application Platform (SNAP), c) initial classification using an automatic thresholding methodology, d) fuzzy-logic based classification refinement, e) final classification including auxiliary data, and f) dissemination and visualization of the results using a dedicated web client. The thematic accuracy of the processor has been assessed for two test-sites of a flood situation at the border between Greece and Turkey with encouraging accuracies. The accuracy assessment, which was performed separately for both standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that the thematic accuracy of VV polarization is slightly higher than that of VH polarization under calm wind conditions.