Derivation of high-resolution (HR) spatial distribution of data is a fundamental problem in Earth observation, either in the case of physical variables or when the data is obtained as the output of a classification process. We show that the problem can be resolved through information fusion at different scales. In the case of physical intensive variables of fully developed turbulence, as encountered in satellite Oceanography and Ocean/Climate interactions, we show that the derivation of HR data can be solved using a new method based on an approximation of the energy cascade, expressed in a microcanonical formulation, and associated to turbulent signal provided by HR satellite data. The generality of the approach offers the opportunity to infer different oceanic signals from Low Resolution (LR) to HR. The basic idea is to use optimal cascading to decrease the spatial resolution of the HR signal, then use the signal available at LR, transmit that information along the scales back to higher spatial resolution using the cascade to obtain a new HR signal. The process has been successfully used to obtain oceanic currents [1, 2], oceanic partial pressure of CO2 [3], and more recently sea surface salinity. [1] H. Yahia, J. Sudre, C. Pottier and V. Garçon, 2010, Motion analysis in oceanographic satellite images using multiscale methods and the energy cascade, Pattern Recognition, DOI: 10.1016/J.patcog.2010.04.011. [2] J. Sudre, H. Yahia, O. Pont, and V. Garçon, 2015, Ocean turbulent dynamics at superresolution from optimal multiresolution analysis and multiplicative cascade, IEEE TGRS, DOI: 10.1109/TGRS.2015.2436431 [3] I. Hernández-Carrasco, J. Sudre, V. Garçon, H. Yahia, C. Garbe, A. Paulmier, B. Dewitte, S. Illig, I. Dadou, M. González-Dávila, and J. M. Santana-Casiano, 2015, Reconstruction of super-resolution ocean pCO2 and air-sea fluxes of CO2 from satellite imagery in the southeastern Atlantic, Biogeosciences, DOI: 10.5194/bg-12-5229-2015