Evaluating sampling efforts of standard laboratory analysis and mid-infrared spectroscopy for cost effective digital soil mapping at field scale

Abstract

The performance of digital soil mapping (DSM) model is highly reliant on the intensity and spatial distribution of the input soil data points. Increasing the number of soil data points (i.e. samples) improves the accuracy of the prediction, but it also raises the sampling effort, including the time, money and labor required for field and laboratory analysis. Thus, optimizing the production of DSMs requires maximizing accuracy while minimizing cost. In this study, we evaluated a range of strategies for DSM of a farm field using high spatial resolution ancillary environmental data (e.g. unmanned aerial vehicle-UAV imagery) and compared sampling efforts of soil data generated from standard laboratory analysis (SLA) and mid-infrared spectroscopy (MIRS) at equivalent costs. We produced DSMs of a number of soil properties including sand, silt, clay, pH, salinity, organic matter, and total nitrogen. We employed Conditioned Latin Hypercube Sampling (cLHS) to generate a range of sampling efforts from the full SLA (n = 62) and MIRS (n = 308) datasets and contrasted the DSM outcomes modeled using kriging with external drift (KED). We found that the DSM outputs were most effective, in terms of accuracy and cost, at 50–60% of the full sampling effort. Although MIRS predictions of soil properties introduced a sizable amount of error, DSMs produced using the MIRS dataset were more accurate as compared to the outcomes of SLA datasets at equivalent sampling efforts. The prediction accuracy for DSMs varied across the soil properties with R2 ranging from 0.82 (for sand) to 0.45 (for total nitrogen) at the optimum sampling effort. The outcomes of the study highlight that spatially optimized sampling efforts and the use of the MIRS technique substantially improve the cost efficiency and accuracy of kriging-based DSM models for predicting a range of field scale soil properties.