AI RESEARCH
Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features
arXiv CS.LG
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ArXi:2604.09818v1 Announce Type: new 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.