Operationalizing fine-scale soil property mapping with spectroscopy and spatial machine learning

Abstract. One challenge in soil mapping is the transfer of new techniques and methods into operational practice, integrating them with traditional field surveys, reducing costs, and increasing the quality of the soil maps. The latter is paramount, as they form the basis for many thematic maps. As part of a novel approach to soil mapping, we integrate various technologies and pedometric methodologies to create soil property maps for soil surveyors, which they can utilize as a reference before beginning their pedological fieldwork. This gives the surveyors considerably more detailed and accurate prior information, reducing the subjectivity inherent in soil mapping. Our approach comprises a novel soil sampling design that effectively captures spatial and feature spaces, mid-infrared spectroscopy, and spatial machine learning based on a comprehensive set of covariates generated through various feature engineering approaches. We employ multi-scale terrain attributes, temporal multi-scale remote sensing, and Euclidean distance fields to account for environmental correlation, spatial non-stationarity, and spatial autocorrelation in machine learning. Methods to reduce the uncertainties inherent to the spectral and spatial data were integrated. The new sampling design is based on a geographical stratification and focuses on the local soil variability. The method identifies spatially local minima and maxima of the feature space, which is fundamental to soil surveys at the specified scale. The k-means and Kennard-Stone algorithms were applied in a sequential manner within each cell of a hexagonal grid overlaying the study area. This approach permits a systematic sub-sampling from each cell to analyze predictive accuracy for varying sampling densities. We tested one to three samples per hectare. Our findings indicate that a sample size of two samples per hectare was sufficient for accurately mapping soil properties across 300 hectares. This markedly reduces the financial burden associated with subsequent projects, given the significant reduction in the time and resources required for surveying. The spectroscopic and spatial models were unbiased and yielded average R² values of 0.91 and 0.68–0.86, depending on mapping with or without pedotransfer models. Our study highlights the value of integrating robust pedometric technologies in soil surveys.