Understanding and predicting soil biogeochemical processes is a central challenge in Earth system science due to their spatial heterogeneity and temporal dynamics. Emerging soil sensing technologies, including proximal spectroscopy, in situ electrochemical sensors, remote sensing, and high-frequency gas flux measurements, are transforming our ability to observe soil conditions in real time and predict soil functions across scales.

This session emphasizes moving from descriptive sensing to quantitative prediction of biogeochemical processes, including carbon turnover, nitrogen cycling, and greenhouse gas fluxes. We invite contributions that link sensor signals to process rates and soil functions, integrate sensing approaches with microbial, molecular, or trait-based data, leverage machine learning or data assimilation frameworks to improve prediction, and evaluate sensor performance across environmental gradients, ecosystems, and management regimes.

By uniting biogeochemistry, ecology, soil physics, and data science, this session aims to translate observational innovation into improved representation of soil processes in ecosystem and Earth system models.

• GC - Global Environmental Change
• H - Hydrology
• NS - Near Surface Geophysics