Periodic Reporting for period 4 - SEDAL (Statistical Learning for Earth Observation Data Analysis.)
Reporting period: 2020-03-01 to 2020-08-31
The WP1 improved prediction models by adaptation to Earth Observation data characteristics. We mainly relied on the frameworks of kernel learning, Bayesian inference, and deep learning to tackle the inverse problem posed in EO data processing. Gaussian Processes (GPs) allowed to include spatial-spectral-temporal relations, signal-to-noise feature relations, and confidence intervals for the predictions. We developed GPs for including physics-inspired priors, noise characteristics, multisource sensor fusion, multi-output regression, emulating physical models, data-model assimilation strategies, and model's efficiency.
The WP2 developments in feature analysis, knowledge extraction, and causality in Earth observation data required improved measures of (conditional) independence, designing experiments in controlled situations, and using high-quality data. We advanced in feature extraction and fusion methods, methods for information coding, automatic feature ranking techniques with GPs, incorporation of active learning and Bayesian optimization strategies, and developed the framework of sensitivity maps for kernel methods, which give information about the most sensitive features and characteristics about their sampling ultimately deployed in model inversion and emulators. Importantly, we developed several causal discovery algorithms; based on Granger causality, unbiased convergent cross-mapping, and additive noise models and kernel deviance measures, and showcased performance in Earth and Climate sciences.
The WP3 showcased relevant applications in geosciences and remote sensing. We extended the application domain from land/vegetation to water (lakes, ocean) and atmosphere (atmospheric temperature/moisture and trace gases profiles) domains, and developed a wide diversity of methods and applications to deal with: 1) Vegetation monitoring at local and regional scales (mainly chlorophyll content, leaf area index, FAPAR, sun-induced fluorescence, water content, as well as new products for vegetation monitoring from passive microwave derived vegetation optical depth (VOD); 2) Carbon, water, energy, heat fluxes monitoring at global scale (e.g. gross primary production, latent heat), plant drivers (e.g. plant traits, spring-onset, maximum light use efficiency, wood density); and soil moisture; 3) water type classification, oceanic chlorophyll, suspending matter, and ocean colour monitoring; and 4) atmospheric profile parameter estimation (e.g. temperature, moisture).