Machine Learning Leads Soil Mapping Race, but No Single Model Wins Everywhere

Soil is one of the most consequential and least understood layers of the planet, and predicting what it contains beneath our feet has become a major computational challenge. A new systematic review published in Discover Soil has, for the first time, put three of the most advanced soil prediction approaches head to head: High-Accuracy Surface Modeling (HASM), machine learning enhanced with Euclidean Distance Fields (ML-EDF), and Bayesian Maximum Entropy (BME). Drawing on 190 studies published between 2014 and 2024, researchers at the University of Abomey-Calavi in Benin found that no single technique dominates. Instead, the winner depends on sample size, data distribution, spatial scale, and how well the underlying data are documented.

The stakes of this comparison are enormous. Accurate maps of soil pH, organic carbon, texture, and nutrients underpin sustainable agriculture, environmental management, and land-use planning. Conventional soil mapping techniques often fail to capture the fine spatial variability of soils and the complex interactions between soil properties and landscape features. The three methods reviewed represent fundamentally different philosophies: HASM is a deterministic approach grounded in differential geometry, BME is a probabilistic geostatistical framework, and machine learning is a data-driven pattern finder. Until now, the literature had never systematically compared all three, leaving researchers without an evidence base for choosing among them.

The mathematics behind each method reveals why they behave so differently. HASM rests on the fundamental theorem of surface theory, which states that a surface is uniquely determined by its first and second fundamental forms. The first form, expressed through coefficients E, F, and G, describes how rapidly a soil property such as nutrient concentration changes in the east-west and north-south directions, enabling calculations of curve lengths, angles, and surface areas. The second form, with coefficients L, M, and N, captures the local curvature of the soil surface, which governs how anomalies such as nutrient hotspots or erosion-prone zones are rendered on the predictive surface. This geometric rigor allows HASM to honor observed data points exactly and produce smooth, physically realistic surfaces.

BME takes an entirely different route. It treats soil properties as a random field and maximizes Shannon entropy, the mathematical measure of uncertainty, subject to the constraints imposed by available data. The method begins with a maximally non-committal prior probability distribution reflecting general knowledge about spatial variability, then updates it with hard, measured soil samples to produce a posterior distribution. The result is a prediction that is probabilistically coherent, explicitly quantifying uncertainty while integrating both exact measurements and softer, less certain information. This makes BME particularly powerful for combining heterogeneous data sources, a persistent headache in soil science where field surveys, legacy maps, and remote sensing products rarely agree.

Machine learning, by contrast, learns patterns directly from data. Random Forest, an ensemble of unpruned decision trees whose predictions are averaged, has become the workhorse of digital soil mapping because of its robustness with high-dimensional inputs. The Euclidean Distance Field enhancement addresses a critical weakness: raw X-Y coordinates tell a model little about spatial structure. EDF transforms coordinates into distance vectors, computing the Euclidean distance between each query point and sample locations, or distances to the corners and center of the sampling rectangle. Studies reviewed show that this transformation substantially improves prediction accuracy by letting algorithms perceive spatial autocorrelation they would otherwise miss.

The review, conducted under PRISMA 2020 guidelines, screened 701 records down to 190 included studies from Scopus, Web of Science, and PubMed. Machine learning appeared in 28 percent of studies, making it by far the most popular choice, while HASM and BME were used in only 3 percent and 2 percent respectively. Bibliometric analysis revealed a strongly upward publication trend and dense collaborative networks, with Chinese institutions such as Wuhan University, Beijing Normal University, and the Chinese Academy of Sciences forming the largest cluster. Notably, Global South institutions in Ethiopia, Benin, Nigeria, and India are increasingly visible, reflecting how soil degradation and food security concerns drive interest well beyond wealthy nations.

Performance analysis showed that HASM, machine learning, and BME consistently outperformed other approaches, but with striking context dependence. BME excels at handling spatial dependency and outliers, making it well suited to regional mapping, yet it is sensitive to skewness and requires ample sample sizes for stable variogram estimation. HASM delivers exceptional fine-scale accuracy and handles skewness well, but it depends on dense sampling and falters in the presence of outliers. Machine learning is robust to skewness and outliers when datasets are large, but its performance becomes irregular under strong spatial dependency unless spatial information is explicitly engineered into the model through EDF features. Reported R² values ranged widely, from 0.45 to 0.95 for machine learning, 0.78 to 0.80 for HASM, and 0.35 to 0.50 for BME, suggesting that study-specific factors often matter more than the algorithm itself.

The review also uncovered troubling reporting gaps that undermine the field’s credibility. Thirty-four percent of studies did not specify their data sources, and roughly 20 percent relied on Google databases of questionable provenance. Thirty-six percent omitted soil nutrients entirely, and a remarkable 72 percent failed to report soil sampling depth, hindering reproducibility. Publication bias analysis using funnel plots, Egger’s regression test, and the trim-and-fill method suggested that machine learning accuracy may be overestimated by around 5 to 7 percent, with an adjusted median R² of 0.73 after accounting for potentially missing weaker studies. Sensitivity analyses, including leave-one-out testing, confirmed that no single study drove the central findings.

The authors argue that the future lies in hybrid modeling that fuses the pattern-finding strength of machine learning, the uncertainty quantification of BME, and the geometric precision of HASM. Such combinations, they contend, can deliver more accurate and robust predictions across diverse data quality conditions than any single paradigm. Emerging deep learning architectures, including convolutional neural networks for spatial imagery and recurrent networks for temporal soil measurements, remain largely untested against these conventional benchmarks and deserve systematic comparison. Proximal soil sensing and remote sensing could supply the high-resolution data needed to feed all three approaches.

For practitioners, the practical message is clear: model choice should follow data characteristics, not fashion. Regional mapping with spatially correlated data favors BME, provided distributions are transformed when skewed. Fine-scale studies with dense sampling suit HASM. Highly variable datasets with large samples favor machine learning, ideally with Euclidean Distance Fields to encode spatial structure. Above all, the review calls for transparent data reporting, standardized validation protocols, and interdisciplinary collaboration, warning that without these foundations even the most sophisticated algorithms will produce maps that look convincing but cannot be trusted.

Subject of Research: Comparative performance of HASM, Euclidean-enhanced machine learning, and Bayesian Maximum Entropy methods for spatial prediction of soil properties

Article Title: A systematic review of high accuracy surface modeling Euclidean enhanced machine learning and Bayesian maximum entropy for soil property prediction

Article References: Kuse, K. A., Agbangba, C. E., & Kakaï, R. G. (2026). A systematic review of high accuracy surface modeling Euclidean enhanced machine learning and Bayesian maximum entropy for soil property prediction. Discover Soil, 3(1), Article 104. https://doi.org/10.1007/s44378-026-00256-3

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00256-3

Keywords: soil prediction, digital soil mapping, machine learning, HASM, Bayesian Maximum Entropy, Euclidean distance fields, geostatistics, random forest, soil nutrients, systematic review, spatial modeling, remote sensing