Speaker
Description
Cosmic-ray neutron sensing (CRNS) has emerged as a promising non-invasive technique for field-scale soil moisture monitoring in agricultural systems. However, the accuracy of CRNS measurements is significantly influenced by above-ground biomass water equivalent (BWE), which varies dynamically throughout crop growth cycles. This study employs URANOS (Ultra Rapid Adaptable Neutron-Only Simulation) Monte Carlo simulations to investigate the complex relationships between vegetation biomass, soil moisture content, and neutron transport processes in agricultural environments.
We modeled neutron footprints and detection characteristics across different crop growth stages, incorporating varying biomass densities and soil moisture conditions. The simulations utilized layered voxel geometry representations with material codes for different vegetation types, from sparse grass to mature crop canopies. Our results demonstrate that biomass water content can cause neutron count rate variations of independent of soil moisture changes, with the effect being most pronounced during rapid growth phases.
The URANOS simulations reveal that the neutron detection radius decreases under dense crop canopies, while detection depth reduces. These findings provide critical correction factors for BWE effects in CRNS calibration functions, improving soil moisture estimation accuracy. The simulation framework enables real-time correction of CRNS data throughout growing seasons, supporting precision agriculture applications and advancing our understanding of neutron transport physics in vegetated environments.
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