autoRA: An Algorithm to Automatically Delineate Reference Areas—A Case Study to Map Soil Classes in Bahia, Brazil

Hugo Rodrigues (), Marcos Bacis Ceddia, Gustavo Mattos Vasques, Sabine Grunwald, Ebrahim Babaeian and André Luis Oliveira Villela
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Hugo Rodrigues: Laboratory of Soil and Water in Agroecosystems (LASA), Postgraduation in Soil Science, Agronomy Institute, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Marcos Bacis Ceddia: Laboratory of Soil and Water in Agroecosystems (LASA), Postgraduation in Soil Science, Agronomy Institute, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Gustavo Mattos Vasques: Brazilian Research Brazilian Agricultural Research Corporation—Soils, Rio de Janeiro 22460-000, Brazil
Sabine Grunwald: Pedometrics, Landscape Analysis & GIS Laboratory, Soil, Water, and Ecosystem Sciences Department, University of Florida, Gainesville, FL 32611, USA
Ebrahim Babaeian: Environmental Soil Physics Lab, Soil, Water, and Ecosystem Sciences Department, University of Florida, Gainesville, FL 32611, USA
André Luis Oliveira Villela: Technical College of the Federal Rural University of Rio de Janeiro, Rio de Janeiro 23897-000, Brazil

Land, 2025, vol. 14, issue 3, 1-36

Abstract: The reference area (RA) approach has been frequently used in soil surveying and mapping projects, since it allows for reduced costs. However, a crucial point in using this approach is the choice or delineation of an RA, which can compromise the accuracy of prediction models. In this study, an innovative algorithm that delineates RA (autoRA—automatic reference areas) is presented, and its efficiency is evaluated in Sátiro Dias, Bahia, Brazil. autoRA integrates multiple environmental covariates (e.g., geomorphology, geology, digital elevation models, temperature, precipitation, etc.) using the Gower’s Dissimilarity Index to capture landscape variability more comprehensively. One hundred and two soil profiles were collected under a specialist’s manual delineation to establish baseline mapping soil taxonomy. We tested autoRA coverages ranging from 10% to 50%, comparing them to RA manual delineation and a conventional “Total Area” (TA) approach. Environmental heterogeneity was insufficiently sampled at lower coverages (autoRA at 10–20%), resulting in poor classification accuracy (0.11–0.14). In contrast, larger coverages significantly improved performance: 30% yielded an accuracy of 0.85, while 40% and 50% reached 0.96. Notably, 40% struck the best balance between high accuracy (kappa = 0.65) and minimal redundancy, outperforming RA manual delineation (accuracy = 0.75) and closely matching the best TA outcomes. These findings underscore the advantage of applying an automated, diversity-driven strategy like autoRA before field campaigns, ensuring the representative sampling of critical environmental gradients to improve DSM workflows.

Keywords: soil class mapping; digital soil mapping; previously mapped areas (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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