Flood detection and monitoring are critical for disaster management and risk assessment. Traditional approaches in Switzerland often rely on numerical models for flood prediction. However, these hydrological models typically do not use satellite images. In this paper, we test the hypothesis of whether the image data carries useful flood-related signals or not. The final goal of the research is to improve the accuracy of existing flood prediction models through a multimodal machine learning approach that leverages both numeric and image data. This study evaluates the application of pre-trained deep learning models trained on global Sentinel-2 satellite imagery for water-land classification in the canton of Zurich, Switzerland. We apply a post-processing pipeline using JRC (Joint Research Centre of the European Commission) permanent water data to extract water differences in time-series images that correspond to flood events. The methodology involves applying a pre-trained model to distinguish between water and land, followed by a post-processing step to identify flood-relevant increases of water levels. We validate the approach by calculating the detection accuracy against official flood event labels from the canton of Zurich, revealing both data consistencies and inconsistencies between satellite-derived flood detection and administrative records. While our current implementation uses JRC data, we discuss the advantages of transitioning to permanent water data provided by the Swiss Federal Office for the Environment, which is more accurate and specifically collected for Switzerland. Our findings demonstrate that optical remote sensing captures valuable flood-relevant information that is currently not utilized by existing Swiss hydrological models, including WaSiM, PREVAH, COSMO-LEPS, and EQRN. This work establishes a foundation for enhancing operational flood forecasting systems in Switzerland by integrating satellite imagery with existing hydrological models.
Lehmann et al. (Wed,) studied this question.