Optical remote sensing images are subject to cloud phenomena that can cause information loss in Earth observation. The main alternative is represented by the synthetic aperture radar images. However, many Earth monitoring applications exploit specific spectral features defined for multispectral data only. In this work, we propose a method that aims to recover several spectral features through deep learning-based data fusion of Sentinel-1 and Sentinel-2 time-series. The proposed approach has been experimentally validated for radiometric indexes such as the normalized difference vegetation index, the normalized difference water index, the soil-adjusted vegetation index and the atmospherically resistant vegetation index. Both numerical and visual results show that the proposed solution outperforms consistently the compared methods.