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

• URL: http://arxiv.org/abs/2604.09818v1
• Date: Fri, 10 Apr 2026 18:50:14 GMT
• Title: Below-ground Fungal Biodiversity Can be Monitored Using Self-Supervised Learning Satellite Features
• Abstract summary: We show that self-supervised learning applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness.<n>Our models explain over half the variance in species richness across 12,000 field samples spanning Europe and Asia.<n>These results establish SSL satellite features as a scalable tool for extending sparse field observations to continuous, high-resolution biodiversity maps.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors [69.38367341895632]
  Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and harvesting along the Portuguese Atlantic coast.<n>We develop and evaluate a machine-learning framework to predict harmful algal bloom occurrence using exclusively satellite-derived predictors.
  arXiv  Detail & Related papers  (2026-07-08T18:14:24Z)
• Continental-scale habitat distribution modelling with multimodal earth observation foundation models [0.0]
  Habitats integrate the abiotic conditions, vegetation composition and structure that support biodiversity and sustain nature's contributions to people.<n>Current habitat maps often fall short in thematic or spatial resolution.<n>We evaluated how high-resolution remote sensing (RS) data and Artificial Intelligence (AI) tools can improve habitat mapping.
  arXiv  Detail & Related papers  (2025-07-13T18:11:26Z)
• Multi-scale species richness estimation with deep learning [0.0]
  We combine sampling theory and deep learning to predict local species richness within arbitrarily large sampling areas.<n>We show how our deep SAR model can provide fundamental insights on the multi-scale effects of key biodiversity processes.
  arXiv  Detail & Related papers  (2025-07-08T19:42:33Z)
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning [60.80381372245902]
  We find emergent behaviors in biological vision models via large-scale contrastive vision-language training.<n>We train BioCLIP 2 on TreeOfLife-200M to distinguish different species.<n>We identify emergent properties in the learned embedding space of BioCLIP 2.
  arXiv  Detail & Related papers  (2025-05-29T17:48:20Z)
• Predicting butterfly species presence from satellite imagery using soft contrastive regularisation [1.0923877073891446]
  This paper presents a new data set for predicting butterfly species presence from satellite data in the United Kingdom.<n>We experimentally optimise a Resnet-based model to predict multi-species presence from 4-band satellite images.<n>We develop a soft, supervised contrastive regularisation loss that is tailored to probabilistic labels.
  arXiv  Detail & Related papers  (2025-05-14T11:42:09Z)
• Feedforward Few-shot Species Range Estimation [61.60698161072356]
  Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts.<n> accurate range estimates are only available for a relatively small proportion of all known species.<n>We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data.
  arXiv  Detail & Related papers  (2025-02-20T19:13:29Z)
• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.<n>Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.<n>Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• GeoPlant: Spatial Plant Species Prediction Dataset [4.817737198128259]
  Species Distribution Models (SDMs) predict species across space from spatially explicit features.<n>We have designed and developed a new European-scale dataset for SDMs at high spatial resolution (10--50m)<n>The dataset comprises 5M heterogeneous Presence-Only records and 90k exhaustive Presence-Absence survey records.
  arXiv  Detail & Related papers  (2024-08-25T20:09:46Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• A high-resolution canopy height model of the Earth [22.603549892832753]
  We present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. We have developed a probabilistic deep learning model to retrieve canopy height from Sentinel-2 images anywhere on Earth. Our model enables consistent, uncertainty-informed worldwide mapping and supports an ongoing monitoring to detect change and inform decision making.
  arXiv  Detail & Related papers  (2022-04-13T10:34:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.