Soils play a fundamental role in sustaining agroecosystem productivity and providing ecosystem services essential for sustainable land and water management. Effective management of soil and water resources requires a detailed understanding of the physical, chemical, and biological processes governing the soil–plant–atmosphere continuum. However, measuring soil state variables and hydraulic parameters remains challenging due to nonlinear interactions controlling heat and mass transfer across scales.
Recent advances in Earth observation, data science, artificial intelligence (AI), and computational modeling are transforming soil physics by enabling multi-scale monitoring and integrated data–model approaches. The combination of remote sensing, innovative measurement techniques, and process-based models provides new opportunities to estimate soil physical properties, assimilate heterogeneous data sources, quantify uncertainties, and improve the understanding of soil–water–atmosphere interactions.
This session aims to bring together researchers working on measurements, modeling, and data-driven approaches to advance soil physics across scales. It bridges traditional soil physics concepts with emerging technologies, fostering interdisciplinary exchange among soil scientists, hydrologists, and Earth observation researchers.
Topics include, but are not limited to: innovative laboratory and field measurement techniques; infiltration experiments; multi-scale remote sensing of soil moisture and physical properties; preferential flow and macropore processes; coupling AI and machine learning with process-based models; data assimilation, inverse modeling, pedotransfer functions, and data fusion; numerical and analytical models accounting for complex soil processes; uncertainty analysis; and case studies supporting climate impact assessments, sustainable land management, and hydrological prediction. Early-career and interdisciplinary contributions are especially encouraged.
Mehdi Rahmati