Environmental Controls on Portable Gamma Spectrometry for Soil Property Assessment: Influence of Land Use and Soil Type on a Farm Level

Proximal sensing techniques play an increasingly important role in pedometrics and digital soil mapping, yet methodological challenges remain in achieving pedologically consistent, transferable predictions. Portable gamma-ray spectrometry (pGRS) offers a physically grounded sensing approach, but its sensitivity to environmental conditions and soil type complicates the development of robust soil property conversion algorithms. This study addresses key session themes by evaluating sampling design, depth consistency, and physical interpretability in pGRS-based soil prediction.

Using a structured grid-based sampling framework, we combined in-situ pGRS measurements with laboratory analyses across contrasting arable and grassland systems at the North Wyke Farm Platform (UK), an experimental research farm, and a geologically distinct external site. Relationships between gamma-derived variables (⁴⁰K, ²³⁸U, ²³²Th) and soil texture, SOC, and soil moisture were examined alongside depth-profile data to assess vertical consistency.

Results show that ⁴⁰K provides a pedologically meaningful predictor of texture and SOC, while soil moisture exerts a physically interpretable attenuation effect on gamma signals. Depth analyses demonstrate that pGRS sensitivity is heavily influenced by the near-surface (0-10 cm), with soil property gradients, rather than radionuclide redistribution, controlling signal response. These findings demonstrate the potential for pGRS-derived predictions and inform the development of depth-aware pedo-transfer functions.

Building on this work, the data collected will be used to develop predictive models for soil properties from pGRS measurements through the integration of machine learning approaches informed by soil process knowledge. This includes the development of an open-source conversion algorithm to translate pGRS signals into soil property estimates with quantified uncertainty, supporting reproducible, explainable, and transferable soil mapping frameworks for soil health assessment.