A new digital soil mapping approach based on the adjacency effect

Accurate soil mapping is crucial for agriculture, land, ecosystem and environmental management. Digital Soil Mapping (DSM) is one of the most conventional and widely used methods for mapping soil. This study introduces a novel strategy for DSM by incorporating the neighborhood effect of environmental covariates (ECs), aiming to enhance mapping accuracy of soil properties. The research focused on modeling organic carbon, cation exchange capacity, bulk density, and pH in southern Canada using 18 ECs derived from the Soil Landscapes of Canada dataset and satellite imagery. Two strategies were compared: a conventional approach using standard ECs, and a proposed method incorporating neighboring ECs through Inverse Distance Weighting. Both strategies employed Gaussian Process Regression for modeling. Results demonstrated significant improvements in accuracy using the proposed strategy. Mean absolute errors were reduced by 32%, 36%, 28%, and 14% for organic carbon, cation exchange capacity, bulk density, and pH, respectively. The proposed method also decreased the coverage of high-error areas and improved R² values across all soil properties. Moreover, mean uncertainty in soil property modeling decreased by 3.4% to 3.9% using the proposed strategy. This study highlights the importance of considering spatial context in DSM through neighborhood effects. The proposed strategy offers a more nuanced and accurate approach to soil property modeling, with potential applications extending beyond soil science to other environmental mapping domains. These improvements in soil mapping accuracy have significant implications for sustainable land management, precision agriculture, and environmental conservation.