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.

翻译：菌根真菌对陆地生态系统功能至关重要。然而，由于时间和成本限制，在景观尺度上监测其生物多样性通常难以实现。现有预测表明，90%的菌根多样性热点区域尚未得到保护，这引发了一个问题：如何广泛而有效地绘制地下真菌群落分布图。在此，我们表明，将自监督学习（SSL）应用于卫星图像，可以预测不同环境下地下外生菌根真菌的丰富度。我们的模型解释了横跨欧亚大陆约12,000个现场样本中超过一半的物种丰富度方差。SSL推导出的特征被证明是信息量最大的单一预测因子，涵盖了气候、土壤和土地覆盖数据集中的大部分信息。通过这种方法，我们实现了比现有技术高10,000倍的空间分辨率提升，从1公里的景观平均值推进到10米的栖息地尺度观测，且几乎没有系统性偏差。由于卫星观测是动态而非静态的，这首次实现了在景观尺度上对地下生物多样性进行时间监测。我们分析了英国国家公园林地中预测真菌丰富度的多年趋势，发现古老森林中的外生菌根多样性可能正在以不成比例的速度丧失。这些结果表明，SSL卫星特征作为一种可扩展的工具，能够将稀疏的现场观测扩展为连续的高分辨率生物多样性地图，用于监测陆地生态系统中不可见的一半。