In this tutorial, we will present the remote sensing image processing chain, and take the attendants on a tour of different strategies for feature extraction, classification, unmixing, retrieval, and pattern analysis for data understanding. On the one hand, we will present powerful methodologies for remote sensing data classification: extracting knowledge from data, including interactive approaches via active learning, classifiers that encode prior knowledge and invariances, semi-supervised learning that exploits the information of unlabeled data, and domain adaptation to compensate for shifts in the ever-changing data distributions. On the other hand, we will pay attention to recent advances in bio-geophysical parameter estimation that incorporate heteroscedasticity, online adaptation, and problem understanding. From there, we will take a leap towards the more challenging step of understanding the geoscience problems from data by reviewing the latest advances in (directed) graphical models, structure learning, and empirical causal inference. Beyond theory, we will also present results of recent studies illustrating all the covered issues. Finally, we will provide code to the attendees to try the different methodologies and provide a solid ground for their future experimentations.