Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed for this scope, to the best of our knowledge, none of them provide an open source framework that can be applied to the reconstruction of remote sensing dataset of size in the order of PetaBytes with good performance and reasonable computational time. Davide Consoli presents here a new method that he and his team implemented in OpenGeoHub to tackle those challenges. In addition to the reconstructed time series, the method outputs a quality assessment layer to quantify the expected effectiveness of the reconstruction.