LEVERAGING EARTH OBSERVATIONS WITH MACHINE-LEARNING APPROACHES FOR WATER CYCLE MONITORING

Earth observation satellites provide unprecedented information to monitor the different components of the water cycle, from soil moisture to river dynamics. However, fully exploiting these observations remains challenging due to sensor limitations, data heterogeneity, complex physical processes, and spatio-temporal resolution constraints. This seminar provides an overview of machine-learning approaches that accompany and enhance remote sensing for water cycle analysis. It illustrates how statistical and machine-learning techniques can improve the exploitation of Earth observation (EO) data at different processing levels, from Level 1 to Level 4. Four examples are presented: (i) compressing hyperspectral information to reduce observation dimensionality, (ii) improving the retrieval of soil moisture from space by exploiting spatial patterns and handling missing data, (iii) harmonizing multi-source EO at the global scale for consistent water cycle monitoring, and (iv) modeling river dynamics using data-driven approaches. Together, these examples highlight the potential of machine-learning techniques to better integrate observations and improve our understanding of hydrological processes.