Integrating satellite remote sensing data and hydrological models by data assimilation for a near real time estimation of the soil water content at local scale.

Climate change affects the Earth system at all levels (IPCC et al., 2007). The Monitoring and prediction of droughts and flood events, agricultural production, and analysis of energy and water will continue to gain importance, accordingly. Especially agricultural systems are of the main affected by rising temperatures, extreme precipitation events, and droughts, all of which can lead to crop failures (Lobell et al., 2011). Approximately 40% of the world's crop production comes from irrigated agriculture (Vereecken et al., 2009), the future expansion of which will continue to provide adequate food for the population. However, efficient irrigation must be ensured to prevent unnecessary groundwater depletion (Richey et al., 2015). To increase efficiency and safeguard yields, novel technologies need to be developed for innovative, real-time water management strategies that will allow farmers to make management decisions at the right time (OECD, 2010). Predicting the overall water supply and its components (e.g., soil water content and groundwater) for plants growth and at each growing stage would assure a sustainable irrigation. Therefore, the aim of this study is to predict the root zone soil water content which is one of the main components of the total water supply for plant growth. For this purpose, spaceborne remote sensing data from C- and L-band Synthetic Aperture Radar will be used. These data provide valuable information about the surface soil moisture only. But by integration into a hydrologic model in a data assimilation framework the soil moisture of the root zone as well as the groundwater recharge can be estimated to identify the actual irrigation requirements and resources.