  Earth observation (EO) satellite missions have been providing detailed images
about the state of the Earth and its land cover for over 50 years. Long term
missions, such as NASA's Landsat, Terra, and Aqua satellites, and more
recently, the ESA's Sentinel missions, record images of the entire world every
few days. Although single images provide point-in-time data, repeated images of
the same area, or satellite image time series (SITS) provide information about
the changing state of vegetation and land use. These SITS are useful for
modeling dynamic processes and seasonal changes such as plant phenology. They
have potential benefits for many aspects of land and natural resource
management, including applications in agricultural, forest, water, and disaster
management, urban planning, and mining. However, the resulting satellite image
time series (SITS) are complex, incorporating information from the temporal,
spatial, and spectral dimensions. Therefore, deep learning methods are often
deployed as they can analyze these complex relationships. This review presents
a summary of the state-of-the-art methods of modelling environmental,
agricultural, and other Earth observation variables from SITS data using deep
learning methods. We aim to provide a resource for remote sensing experts
interested in using deep learning techniques to enhance Earth observation
models with temporal information.
