Our database contains previews of images of ocean and sea suface taken by various remote sensing  instruments. A variety of satellite and aircraft microwave imaging tools present their capabilities to register the whole array of oceanic and atmospheric phenomena.

        Spaceborne high resolution radar images of the ocean have already revealed a wealth of information on numerous dynamical processes. However, to date, the interpretation of radar images in terms of underlying processes is often heuristic and based on pattern recognition. The data could be exploited more efficiently on the basis of physical radar imaging models that relate image intensity variations quantitatively to physical processes at the ocean surface. Such models are being used for selected applications like the remote sensing of long surface waves or the underwater bottom topography in coastal waters. Also the radar imaging mechanisms of most other phenomena are theoretically understood. But some remaining problems hamper the use of physical models in operational contexts: In many cases, available models are not suited for general applications and require costly adaptation or tuning for particular scenarios. Furthermore, they are usually formulated as forward models which convert physical parameters into radar signatures but not vice versa. The inversion of such models, which are usally nonlinear, can be difficult and time-consuming and lead to ambiguous results. Finally, radar signatures can also be ambiguous in the sense that it is not obvious if a particular feature in an image results from an oceanic or an atmospheric phenomenon or a combination of both. The interpretation of such radar signatures can require a lot of user interaction, experience, and/or additional information.

        Some promising new findings and developments from recent international projects encourage the ADI'DAS consortium to believe that this situation can be significantly improved.