Field‐Scale Mapping of Soil Carbon Stock with Limited Sampling by Coupling Gamma‐Ray and Vis‐NIR Spectroscopy

Abstract

Core Ideas Proximal sensors have been used for high‐detail mapping of soil C stock at field scale. Laboratory Vis‐NIR spectroscopy allowed an increase in the number of data points. Topsoil spatial variability maps were obtained using field gamma‐ray spectroscopy. Only conventional laboratory analysis was used to calibrate the model (one sample/ha). High‐precision mapping of important soil services, such as soil organic C stocks, is basic for monitoring the effects of different soil management regimes and the effectiveness of agricultural policies. Proximal soil sensing methods have been often used in the last decades to limit costs, field work, and time and to obtain reliable and accurate maps. We tested the combined use of two proximal sensors, visible–near‐infrared (Vis‐NIR) and passive γ‐ray spectrometers, to obtain highly detailed maps of C stocks of the topsoil (CS30, 0–30 cm) of nine pairs of fields in western Sicily using a limited number of sampling sites per field for traditional laboratory analysis (about one sample per hectare). Laboratory Vis‐NIR diffuse reflectance spectroscopy allowed the number of data points per field to be increased, at the same time reducing the costs for laboratory analysis. The predictive model had a coefficient of determination (R2) of 0.77 and an error (RMSE) of 0.67 kg m−2. Data points predicted by Vis‐NIR on the fine earth (<2 mm) and corrected for gravel content (CS30pred) were interpolated within each field using geographically weighted multiple regression and two sets of covariates: (i) digital elevation model derivatives, such as elevation, slope, plan and profile curvature, and topographic wetness index; and (ii) elevation and γ‐ray total counts maps. Validation of 36 independent data points showed that the second method provided greater accuracy than the first. In particular, residual prediction deviation (RPD) showed a mean value of 2.19; however, three pairs of fields showed high error and low RPD. This methodology provides a cost‐effective tool to interpolate C stocks within arable fields, limiting laboratory analysis. The accuracy of the CS30pred maps allows monitoring of the effects of agricultural management and/or soil erosion on the soil C pool.