  Public satellite missions are commonly bound to a trade-off between spatial
and temporal resolution as no single sensor provides fine-grained acquisitions
with frequent coverage. This hinders their potential to assist vegetation
monitoring or humanitarian actions, which require detecting rapid and detailed
terrestrial surface changes. In this work, we probe the potential of deep
generative models to produce high-resolution optical imagery by fusing products
with different spatial and temporal characteristics. We introduce a dataset of
co-registered Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat
surface reflectance time series and demonstrate the ability of our generative
model to blend coarse daily reflectance information into low-paced finer
acquisitions. We benchmark our proposed model against state-of-the-art
reflectance fusion algorithms.
