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Surface Water Detection from Sentinel-1

Synthetic Aperture Radar (SAR) data from Sentinel-1 mission is a valuable all-weather observation for various monitoring applications on the land surface. In particular, SAR observations have unique signatures over surface water which makes them appropriate to develop a global surface water monitoring system. Existing global surface water products rely on multispectral observations which have significant shortcomings in cloudy regions. In this study, we present a novel Convolutional Neural Network (CNN) model applied to Sentinel-1 observations at global scale to detect surface water. We used an existing product as training data, and implemented several fine tuning strategies to improve accuracy of the model in places with complex land cover type. We provide an accuracy assessment against the existing product as well as an independent human-labeled dataset. This new surface water product has higher spatial resolution (10 m) compared to the existing products, is not impacted by cloud coverage, and can be run in near-real time to detect any surface water changes.