Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features
TL;DR
It is shown that self-supervised learning applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness across diverse environments, and SSL-derived features are the most informative tested predictor group, and outperform each of the established climate, soil, and land cover baselines.
Abstract
Mycorrhizal fungi are vital to terrestrial ecosystem functioning. Yet monitoring their biodiversity at landscape scales is often unfeasible due to time and cost constraints. Current predictions suggest that 90% of mycorrhizal diversity hotspots remain unprotected, opening questions of how to broadly and effectively map underground fungal communities. We show that self-supervised learning (SSL) applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness across diverse environments. Our models explain over half the variance in species richness across ~12,000 field samples spanning Europe and Asia. SSL-derived features are the most informative tested predictor group, and outperform each of the established climate, soil, and land cover baselines. We achieve a 10,000-fold increase in spatial resolution over existing techniques, moving from 1km landscape averages to 10m habitat-scale observations. As satellite observations are dynamic rather than static, this enables temporal monitoring of below-ground biodiversity at landscape scales for the first time. We apply this approach across two UK National Park woodlands, where ancient forests are predicted to have high but declining ectomycorrhizal diversity, marking key areas for further field verification. These results establish SSL satellite features as a scalable tool for extending sparse field observations to continuous, high-resolution biodiversity maps for monitoring the invisible half of terrestrial ecosystems.
