Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data
David Marzi, Paolo Gamba
Abstract
Climate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is a lack of high-resolution water body maps at the global scale. In this paper we present a fully automated procedure for the extraction of fine spatial resolution (10 m) inland water land cover maps for any region of the Earth by means of a relatively simple K-Means clustering model applied to multitemporal features extracted from Sentinel-1 SAR sequences. Indeed, due to heavy cloud coverage conditions in many locations, multispectral sensors are not suitable for global water body mapping. For this reason, in this work we deal only with SAR data, and specifically with multitemporal Sentinel-1 data sequences. The experimental results, obtained for three geographical areas selected because of their wide diversity in terms of geo-morphology and climate, show an almost complete consistency with existing data sets, and improve them thanks to their finer spatial details.