Abstract

High-resolution soil property mapping is essential for precision agriculture and sustainable land management, but traditional approaches face limitations in capturing complex spatial relationships and integrating multi-modal data sources. This study presents a novel application of transformer-based deep learning models for predicting soil properties at unprecedented spatial resolution using multi-modal remote sensing data. We developed and evaluated three transformer architectures: Vision Transformer (ViT), Swin Transformer, and a custom Multi-Modal Transformer (MMT) for mapping soil organic carbon (SOC), pH, clay content, and available nitrogen across 15,000 km² of agricultural landscapes in the Midwest USA. The models integrated Sentinel-2 multispectral imagery, Landsat-8 thermal data, ALOS PALSAR-2 synthetic aperture radar, digital elevation models, and 8,247 ground truth soil samples collected from multiple depths (0-15, 15-30, 30-60 cm). Data preprocessing involved advanced augmentation techniques, spatial-temporal feature extraction, and attention-based fusion mechanisms. The Multi-Modal Transformer achieved superior performance with R² values of 0.89 for SOC, 0.84 for pH, 0.81 for clay content, and 0.76 for available nitrogen, outperforming traditional machine learning methods (Random Forest: R² = 0.72-0.78) and convolutional neural networks (CNN: R² = 0.75-0.82). Root mean square errors were reduced by 23-31% compared to conventional approaches. The transformer models demonstrated exceptional capability in capturing long-range spatial dependencies and complex non-linear relationships between soil properties and environmental covariates. Attention mechanism analysis revealed that the models effectively learned to focus on relevant spectral bands, topographic features, and spatial contexts. High-resolution maps (10-meter pixel size) were generated showing detailed spatial variability previously undetectable with traditional methods. Computational efficiency analysis showed 2.3× faster inference compared to equivalent CNN architectures while maintaining superior accuracy. Cross-validation experiments across different agro-ecological zones confirmed model robustness and transferability. The study demonstrates the transformative potential of transformer architectures for digital soil mapping, enabling precision agriculture applications and supporting data-driven decision-making for sustainable soil management.