Automated flood mapping and monitoring using Sentinel 1 data
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.