Soil sampling by automatically identifying optimal reference areas reduces costs while maintaining model accuracy.
Automatically identifies optimal reference areas using Gower's Dissimilarity Index, eliminating subjective manual delineation.
Reduces soil sampling costs by 61-63% while maintaining or improving model accuracy and predictive power.
Provides objective, data-driven methodology that enhances reproducibility and scalability across diverse soil landscapes.
Captures the full spectrum of environmental variability through the integration of multiple SCORPAN covariates.
Successfully validated in contrasting environments: Florida (USA) and Rio de Janeiro (Brazil).
Innovative algorithm registered as Brazilian patent BR102024020867 and required US patent registration.
Area: 170,304 km²
Predominantly flat terrain with elevations from sea level to 110m
Spodosols, Entisols, Inceptisols, Ultisols
Cost Reduction: 63%
Accuracy: 0.38 (EPM) vs 0.35 (autoRA)
Sampling: 50% target area with 10-pixel blocks
Area: 900 km²
Terrain
Semi-arid landscape in northeastern Brazil with diverse geomorphology, pronounced elevation gradients, and heterogeneous parent materials.
Predominantly Oxisols and associated transitional soils, with marked variation in texture, mineralogy, and soil carbon content driven by topographic and climatic contrasts.
Cost Reduction: ≈60% compared to full-area mapping
Accuracy: 0.85–0.96 (autoRA) vs 0.75 (manual RA)
Sampling: Optimal at 40% coverage, balancing representativeness and redundancy
Performance: Automated delineation captured key environmental gradients, achieving near-total-area accuracy with significantly fewer samples.

Multi-scale overview of the Coari region, Amazonas, Brazil. The top panels display the regional context within Brazil and Amazonas State; the bottom panel details the manual reference area, training sites (represented by black points), and validation sites (denoted by green diamonds) over satellite imagery.

Exploratory maps showing spatial variability of key environmental covariates (soil, geomorphology, geology, elevation, temperature, and precipitation). Training and validation points are overlaid on the manual reference area to illustrate environmental representativeness across gradients.

Map showing the automated Reference Area (autoRA) delineation at 50% coverage across the study region. Black circles represent training points and green diamonds represent validation points, illustrating the spatial distribution of sampling locations within and outside the autoRA coverage.
autoRA is developed through academic–industry collaboration led by UFRRJ researchers and international partners.
We welcome collaborations, research partnerships, and commercial applications across agriculture, mining, and environmental management sectors.
📩 Contact us to discuss integration, data analysis services, or pilot testing opportunities
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Co-developed with UFRRJ.
Brazilian Patent BR102024020867 | U.S. patent filed.