Improve prediction models by adaptation to Earth Observation data characteristics. We will rely on the framework of kernel learning, which has emerged as the most appropriate framework for remote sensing data analysis in the last decade. The new retrieval models will be adapted to the particular signal characteristics, such as unevenly sampled time series and missing data, non-Gaussianity, presence of heteroscedastic and non-stationary processes, and non-i.i.d. (spatial and temporal) relations. Models based on kernels and GPs will allow us to advance in uncertainty quantification using predictive variances under biophysical constraints. Advances in sparse, reduced-rank and divide-and-conquer schemes will address the computational cost problem. The proposed kernel framework aims to improve results in terms of accuracy, reduced uncertainty, consistency of the estimations and computational efficiency.
Discover knowledge and causal relations in Earth observation data. We will investigate graphical causal models and regression-based causal schemes applied to large heterogeneous EO data streams. This will require improved measures of (conditional) independence, designing experiments in controlled situations and using high-quality data. Learning the hierarchy of the relations between variables and related covariates, as well as their causal relations, may in turn allow the discovery of hidden essential variables, drivers and confounders. Moving from correlation to dependence and then to causation concepts is fundamental to advance the field of Earth Observation and the science of climate change.