Author

M. V. van Nuland

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#machine learning Apr 2026

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

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.

Robin Young, M. V. van Nuland, E. T. Kiers et al. · arXiv.org · 0 citations