Climate extremes and unsustainable development are increasingly disrupting soil and water dynamics. Heavy rainfall, prolonged droughts, and human pressures are reshaping water and sediment flows, accelerating land degradation, and threatening food and water security. Addressing these challenges requires continuous monitoring and innovative data integration across various spatial and temporal scales. In this context, sensing technologies offer unprecedented opportunities to monitor the Earth’s surface and understand its processes, especially when they're enhanced by Artificial Intelligence (AI) and advanced computational tools to enhance predictive capability and pattern recognition.
This session invites contributions that push the boundaries of how we observe, measure, and monitor soil and/or soil-water dynamics. We are looking to showcase and discuss research that spans a wide range of scales, from local plots to global systems, and employs a variety of sensing techniques, from proximal sensors (i.e., in-field and machinery sensors) to remote sensing (i.e., UAVs, airborne systems, and satellites).
We also welcome studies that explore the fusion of diverse geospatial datasets (e.g., soil sensors, LiDAR, photogrammetry, and satellite imagery) to gain a more holistic, multi-scale understanding of these processes. This also includes research that uses sensed data as input for modelling and/or aim at predict future scenarios under changing conditions.
This session is open to, but not limited to, the following topics and application fields (e.g., agriculture, forestry, urban development, and mountain environments):
Advanced proximal sensing and ground-based monitoring
• Remote sensing, UAV, airborne, and satellite remote sensing of soil and water investigations
• Fusion and integration of multi-source geospatial data
• AI and machine learning for soil and water analysis
• Novel monitoring workflows, protocols, and open-source tools
• Linking sensing data with process-based or distributed models
• Critical evaluations of opportunities and limitations of emerging technologies
• Translating sensing and modeling into decision-support frameworks
Interdisciplinary contributions are more then welcome, as well as the participation of early career scientists (ECS).