AutoRA: an innovative algorithm for automatic delineation of reference areas in support of smart soil sampling and digital soil twins - Rodrigues, Hugo; Ceddia, Marcos Bacis; Vasques, Gustavo Mattos; Grunwald, Sabine; Babaeian, Ebrahim (Frontiers in Soil Science, 2025)
Digital Soil Mapping (DSM) enhances the delivery of soil information but typically requires costly and extensive field data to develop accurate soil prediction models. The Reference Area (RA) approach can reduce soil sampling intensity; however, its subjective delineation may compromise model accuracy when predicting soil properties. In this study, we introduce the autoRA algorithm, an innovative automated soil sampling design method that utilizes Gower’s Dissimilarity Index to delineate RAs automatically. This approach preserves environmental variability while retaining accuracy compared to an exhaustive predictive model (EPM) based on extensive sampling of the entire area of interest. Our objective was to evaluate the sensitivity and efficiency of autoRA by varying target areas (10–50% of the total area) and block size spatial resolutions (5–150 pixels) in regions of Florida, USA, and Rio de Janeiro, Brazil. We modeled a hypothetical soil property derived from a combination of commonly used DSM covariates and user inputs into autoRA. Model performance was assessed using R², root mean square error (RMSE), and Bias, aggregated into a Euclidean Distance (ED) metric. Among all configurations, the optimal RA selection – characterized by the lowest ED – was achieved with a target area of 50% and a block size of 10 pixels, closely matching the accuracy of the EPM. For example, in Rio de Janeiro, the EPM produced an ED of 0.17, while the best RA configuration yielded an ED of 0.15. In Florida, the EPM had an ED of 0.35 compared to 0.38 for the optimal RA. Additionally, the 50%-RA with a block size of 10 significantly reduced total costs by approximately 61% in Rio (from US$258,491 to US$100,611) and 63% in Florida (from US$289,690 to US$106,296). Overall, autoRA systematically identifies cost-effective sampling configurations and reduces the investigation area while maintaining model accuracy. By automating RA delineation, autoRA mitigates the subjectivity inherent in traditional methods, thereby supporting more reproducible, strategic, and efficient DSM workflows.