The Earth’s Critical Zone (CZ) is the “heterogeneous, near surface environment in which complex interactions involving rock, soil, water, air, and living organisms regulate the natural habitat and determine the availability of life-sustaining resources”. Understanding this heterogeneity requires integrating highly diverse datasets across space, time, formats, and domains. However, dealing with the extreme data fragmentation in environmental Earth sciences remains a major challenge. This session highlights the implementation of FAIR geospatial methodologies to accelerate data assimilation, predictive modelling, and the generation of actionable Earth Intelligence regarding Critical Zone behaviour.
We welcome contributions showing technical workflows, digital infrastructures, and methodologies designed to break down domain silos. These should address the seamless integration of in-situ monitoring networks with multi-sensor remote sensing (RS) data products, focusing on:
• Format harmonization strategies bridging disparate spatial-temporal models and interdisciplinary data sources.
• The application of standard APIs (e.g., OGC standards, openEO) for scalable data access.
• Modern, distributed metadata catalogues enhancing data discoverability.
• Semantic harmonization efforts, including domain ontologies and controlled vocabularies to eliminate ambiguity across disciplines (e.g., Essential Variables (EVs) across the hydrosphere, biosphere, atmosphere, and geosphere).
• The use of geospatial data pipelines for Analysis Ready Data (ARD) and AI-ready data to streamline machine learning workflows.
• Advanced AI modelling (e.g., physics-informed neural networks, deep learning) to extract scalable CZ Earth Intelligence.
• The alignment of CZ science with large-scale initiatives, such as Digital Twins (DestinE) and the Common European Data Spaces (e.g., Green Deal Data Space).
This session aims to foster an interoperable, open-science ecosystem that transforms fragmented observations into scalable Earth Intelligence and actionable insights for modelling and managing our living planet.