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Investigating the potentialities of Monte Carlo simulation for assessing soil water content via proximal gamma-ray spectroscopy

Baldoncini, Marica, Albéri, Matteo, Bottardi, Carlo, Chiarelli, Enrico, Raptis, Kassandra Giulia Cristina, Strati, Virginia, Mantovani, Fabio
Journal of environmental radioactivity 2018 v.192 pp. 105-116
Monte Carlo method, gamma radiation, monitoring, potassium, precision agriculture, radioactivity, radionuclides, soil composition, soil water, soil water content, spectroscopy, thorium, uncertainty, uranium
Proximal gamma-ray spectroscopy recently emerged as a promising technique for non-stop monitoring of soil water content with possible applications in the field of precision farming. The potentialities of the method are investigated by means of Monte Carlo simulations applied to the reconstruction of gamma-ray spectra collected by a NaI scintillation detector permanently installed at an agricultural experimental site. A two steps simulation strategy based on a geometrical translational invariance is developed. The strengths of this approach are the reduction of computational time with respect to a direct source-detector simulation, the reconstruction of ⁴⁰K, ²³²Th and ²³⁸U fundamental spectra, the customization in relation to different experimental scenarios and the investigation of effects due to individual variables for sensitivity studies. The reliability of the simulation is effectively validated against an experimental measurement with known soil water content and radionuclides abundances. The relation between soil water content and gamma signal is theoretically derived and applied to a Monte Carlo synthetic calibration performed with the specific soil composition of the experimental site. Ready to use general formulae and simulated coefficients for the estimation of soil water content are also provided adopting standard soil compositions. Linear regressions between input and output soil water contents, inferred from simulated ⁴⁰K and ²⁰⁸Tl gamma signals, provide excellent results demonstrating the capability of the proposed method in estimating soil water content with an average uncertainty <1%.