19 Abstract This paper presents the intelligent techniques approach for flood monitoring using Synthetic Aperture Radar (SAR) satellite images. We applied artificial neural networks and Self-Organizing Kohonen Maps (SOMs), to SAR image segmentation and classification. Our approach was used to process data from different SAR satellite instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1/2) for different flood events: Tisza River, Ukraine and Hungary in 2001; Huaihe River, China in 2007; Mekong River, Thailand and Laos in 2008; Koshi River, India and Nepal in 2008; Norman River, Australia in 2009; Lake Liambezi, Namibia in 2009; Mekong River, Laos in 2009. This approach was implemented using Sensor Web paradigm for integrated system for flood monitoring and management. Keywords Flood • Synthetic Aperture Radar (SAR) • Artificial neural networks • Sensor Web paradigm Introduction In recent decades the number of hydrological natural disasters has increased considerably. According to Scheuren et al. (2008), we have witnessed during 2000–2007 a strengthening of the upward trend, with an average annual growth rate of 8.4%. Hydrological disasters, such as floods and wet mass movements, represent 55% of the overall disasters reported in 2007, had a tremendously high human impact (177 million victims) and caused high economic damages, accounting for 24.5 billion USD (Scheuren et al. 2008). Earth observation (EO) data from space can provide valuable and timely informa- tion when one has to respond to and mitigate emergencies such as floods. Satellite observations enable the acquisition of data for large and hard-to-reach territories, as N. Kussul (), A. Shelestov, and S. Skakun Space Research Institute NASU-NSAU, Kyiv, Ukraine e-mail: inform@ikd.kiev.ua; serhiy.skakun@ikd.kiev.ua Flood Monitoring from SAR Data Nataliia Kussul, Andrii Shelestov, and Sergii Skakun F. Kogan et al. (eds.), Use of Satellite and In-Situ Data to Improve Sustainability, NATO Science for Peace and Security Series C: Environmental Security, DOI 10.1007/978-90-481-9618-0_3, © Springer Science+Business Media B.V. 2011