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For numerous earth observation applications, one may benefit from varioussatellite sensors to address the reconstruction of some process or informationof interest. A variety of satellite sensors deliver observation data withdifferent sampling patterns due satellite orbits and/or their sensitivity toatmospheric conditions (e.g., clour cover, heavy rains,...). Beyond the abilityto account for irregularly-sampled observations, the definition of model-driveninversion methods is often limited to specific case-studies where one canexplicitly derive a physical model to relate the different observation sources.Here, we investigate how end-to-end learning schemes provide new means toaddress multimodal inversion problems. The proposed scheme combines avariational formulation with trainable observation operators, {\em a priori}terms and solvers. Through an application to space oceanography, we show howthis scheme can successfully extract relevant information fromsatellite-derived sea surface temperature images and enhance the reconstructionof sea surface currents issued from satellite altimetry data.

Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields