Comparison of two gamma-ray datasets measured with different methods and assessment of their performance to predict soil properties

Abstract

ABSTRACT Terrain mobility is directly dependent on soil properties, i.e. stoniness, soil particle size and peat depth. Gamma-ray spectrometry has been widely used in soil mapping as gamma-rays can penetrate up to 50 cm of soil. The method has proven to be effective in gathering information on various soil properties, such as clay and silt content, organic carbon content, soil pH, and soil stoniness. The intensity of gamma radiation decreases with increasing soil moisture content, which is important when assessing the load-bearing capacity of forest terrain, which affects the quality and planning of mechanized wood harvesting. In this study, we compared two gamma-ray datasets that were acquired from the same plots but measured with different methods: airborne gamma-ray spectrometry and a handheld gamma-ray spectrometer. Regression analyses were used to examine whether the gamma-ray datasets have significant similarities. Linear discriminant analysis was then used to study the performance of the two gamma-ray datasets when predicting different soil properties (stoniness index classes (SIC), soil depth, and peat depth). Our correlation analysis produced R2 values of 0.18–0.29 and root mean square error (RMSE%) values of 0.48–0.55. Prediction of SIC was 67.1% accurate with ground gamma and 45.9% accurate with air gamma. For soil depth predictions, the accuracies were 64.7–70.0% for ground gamma and 61.7–63.5% for air gamma. Our findings showed that ground gamma-ray spectrometry is better at predicting soil properties than air gamma-ray spectrometry.