Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features

In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user's decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-score < 0.75) over heterogeneous conditions (e.g. rice fields). The algorithm was applied with no changes and/or tuning to independent sites from the Copernicus Emergency Management Service (EMS) activations to simulate operational conditions. Over these sites, the proposed approach achieved greater, more consistent and robust mapping accuracy compared to traditional approaches based on the segmentation of single input features. Moreover, OWA operators offer an appealing way of combining and aggregating multiple information in decision making by modelling uncertainty in decision process.