Abstract

Within the European Space Agency (ESA) Climate Change Initiative (CCI) project framework it is fundamental to generate High Resolution (HR) annual surface water maps. In this work we present an innovative approach aimed at this task using multi-temporal Sentinel-1 SAR data. Mapping water bodies with dual-polarized radar data everywhere in the world is challenging, as the dual-pol backscatter intensity signal is strongly affected by many factors such as terrain an acquisition geometry. In this study, an existing Medium Resolution Land Cover (MRLC) map is ecploited to automatically collect sample points and build a k-means unsupervised model. A qualitative analysis is first performed in three test areas. Preliminary quantitative analysis is then presented, showing 97% extraction accuracy.

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