The unprecedented volumes of satellite Earth observation data gathered today allow for thorough investigation of Earth's climate system and its interactions with the biosphere. Several international research initiatives and scientific projects are focused on the application of mathematical and statistical methods to extract insights about the functioning of the hydrosphere, biosphere and atmosphere, from this emerging Earth information data-cube. However, this data-cube is highly dimensional, thus innovative data mining and big data tools are required, and machine-learning methods – such as neural networks, tree ensembles, random forests or Gaussian processes, among others – can offer new means to extract valuable information in a rigorous manner.

As we progress through an exponential increase in satellite data availability, this session aims to bring researchers together to discuss the current state in big data and machine learning applications to Earth sciences and remote sensing. We aim to both (a) discuss current efforts, and (b) identify common challenges for the future. We encourage authors to submit presentations on: machine learning applied to geosciences and remote sensing, data-driven methods to analyse spatiotemporal dynamics and causal relationships in Earth observations, enlightening opinions about interface between mathematics and climate science.