From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy

2026-08-01

AI Summary

This study addresses the challenge of efficiently and accurately predicting soil properties from high-dimensional, highly collinear spectral data across multiple spatial scales—from field to global. We systematically evaluate the performance of various regression models and dimensionality reduction techniques across 85 prediction tasks. Notably, we introduce TabPFN, a tabular foundation model, into soil spectroscopy for the first time, demonstrating its ability to outperform conventional approaches without explicit dimensionality reduction. Further gains in predictive accuracy are achieved by integrating partial least squares (PLS) latent variables with TabPFN. Empirical results show that TabPFN consistently achieves state-of-the-art performance across all scales, substantially surpassing classical baselines—particularly on large-scale global spectral libraries comprising tens of thousands of samples—thereby highlighting its potential as a general-purpose modeling paradigm for soil property prediction.

Abstract

Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties. Yet, translating high-dimensional, highly collinear spectra into accurate soil property predictions remains challenging, particularly when employing machine learning. We systematically investigated regression models and dimensionality reduction approaches for spectroscopic modeling across 85 regression tasks from open benchmark datasets in pedometrics spanning field-scale digital soil mapping and a global soil spectral library. We compared an in-context learning tabular foundation model (TabPFN), a convolutional neural network (CNN), rule-based regression (Cubist), Random Forest, and partial least squares regression (PLSR) using full spectra as well as features derived from principal component analysis (PCA) and partial least squares (PLS) latent variables. TabPFN consistently delivered the best overall performance across scales, including large spectral library tasks with tens of thousands of soil samples. Notably, TabPFN applied directly to full spectra already surpassed all classical baselines, showing that explicit dimensionality reduction is not strictly required for strong performance. Further improvements were achieved through PLS, which proved to be an effective dimensionality reduction strategy for all models. Combining PLS latent variables with TabPFN yielded the best predictions overall. Our findings provide evidence-based guidance for spectroscopic calibration model selection across operational scales, demonstrating that the long-standing advantages of PLSR and modern tabular foundation models complement each other in chemometrics.

Problem

• soil spectroscopy
• high-dimensional data
• soil property prediction
• machine learning
• spectral libraries

Innovation

• Tabular foundation model
• Soil spectroscopy
• In-context learning
• Partial least squares
• High-dimensional regression