Physically-based models represent detailed surface/subsurface transfer, but the required spatial information does not allow their operational use. In situ data on pesticides in a catchment are usually rare and not continuous in time and space. Satellite images, on the other hand, well describe data in space, but only water related, and at limited time frequency. This study aims to exploit these 3 types of information (model,in situ data, images) with data assimilation methods adapted to image data, in order to improve pesticide and hydrological parameters and better understand physical processes. This poster discusses the proposed methodology as well as the available study site data and modeling components.