Learning Sentinel-2 reflectance dynamics for data-driven assimilation
  and forecasting
Anthony Frion Lucas Drumetz Guillaume Tochon Mauro Dalla Mura Abdeldjalil Aïssa El Bey
Over the last few years, massive amounts of satellite multispectral and
hyperspectral images covering the Earth's surface have been made publicly
available for scientific purpose, for example through the European Copernicus
project. Simultaneously, the development of self-supervised learning (SSL)
methods has sparked great interest in the remote sensing community, enabling to
learn latent representations from unlabeled data to help treating downstream
tasks for which there is few annotated examples, such as interpolation,
forecasting or unmixing. Following this line, we train a deep learning model
inspired from the Koopman operator theory to model long-term reflectance
dynamics in an unsupervised way. We show that this trained model, being
differentiable, can be used as a prior for data assimilation in a
straightforward way. Our datasets, which are composed of Sentinel-2
multispectral image time series, are publicly released with several levels of
treatment.