Field-scale soil carbon analysis: Correcting for moisture and bulk density effects in Inelastic Neutron Scattering

Traditional point-sampling methods for soil elemental analysis often struggle to capture field-scale spatial variations and rely on destructive, labor-intensive bulk density measurements. Inelastic Neutron Scattering (INS) offers rapid, non-destructive, in situ measurements of soil elemental composition over large volumes. A unique advantage of this neutron-based method is the potential to measure all major soil components simultaneously, allowing for the simultaneous estimation of bulk density and water content alongside elemental carbon. While standard INS yields bulk concentrations over a given volume, spatially resolved measurements require techniques such as Associated Particle Imaging (API). INS-API techniques provide non-destructive access to depth-resolved information, but the quantitative accuracy of this technique depends heavily on correcting for signal attenuation. While gamma-ray attenuation is readily predicted from soil density, neutron attenuation is complex and highly sensitive to hydrogen content, confounding carbon measurements in soils. In this study, we use Monte Carlo simulations of soils with varied compositions, bulk densities, and water contents to model neutron attenuation and develop a simple predictive model requiring only dry bulk density and volumetric water content. We find that such a simplification achieves accuracy within 10% at 30 cm depth for simulated soils, and validate the model experimentally to 18 cm depth using an INS–API system with controlled soil columns. This approach enables practical correction of INS–API measurements, laying the groundwork for a self-consistent framework to monitor soil carbon stocks independent of moisture fluctuations.