Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features

2026-04-10

AI Summary

Traditional approaches struggle to monitor belowground ectomycorrhizal fungal diversity at large scales efficiently and cost-effectively, hindering conservation efforts in biodiversity hotspots. This study addresses this challenge by leveraging self-supervised learning to extract features from dynamic satellite imagery and integrating them with climate, soil, and land cover data to build a high-accuracy species richness prediction model. Evaluated across approximately 12,000 Eurasian sampling sites, the model explains over 50% of the observed variation, with self-supervised features emerging as the strongest individual predictor. The approach enables spatially continuous, 10-meter-resolution mapping of belowground fungal diversity through time, revealing an accelerating loss of ectomycorrhizal diversity in ancient forests.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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. Here, 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 prove to be the single most informative predictor, subsuming the majority of information contained in climate, soil, and land cover datasets. Using this approach, we achieve a 10,000-fold increase in spatial resolution over existing techniques, moving from 1km landscape averages to 10m habitat-scale observations with nearly no systematic bias. As satellite observations are dynamic rather than static, this enables temporal monitoring of below-ground biodiversity at landscape scales for the first time. We analyze multi-year trends in predicted fungal richness across UK National Park woodlands, finding that ancient forests may be losing ectomycorrhizal diversity at disproportionate rates. 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.

Problem

• below-ground fungal biodiversity
• mycorrhizal fungi
• landscape-scale monitoring
• biodiversity mapping
• ectomycorrhizal richness

Innovation

• self-supervised learning
• satellite remote sensing
• below-ground biodiversity
• ectomycorrhizal fungi
• high-resolution mapping