Articles | Volume 8, issue 1
https://doi.org/10.5194/soil-8-223-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-8-223-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
25 Mar 2022
Original research article |
| 25 Mar 2022
| 25 Mar 2022
Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions
Yuanyuan Yang
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Zefang Shen
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Andrew Bissett
CSIRO Oceans and Atmosphere, GPO BOX 1538, Hobart TAS 7001, Australia
Raphael A. Viscarra Rossel
CORRESPONDING AUTHOR
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Viewed
Total article views: 4,157 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Sep 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,109 | 960 | 88 | 4,157 | 270 | 79 | 105 |
- HTML: 3,109
- PDF: 960
- XML: 88
- Total: 4,157
- Supplement: 270
- BibTeX: 79
- EndNote: 105
Total article views: 2,945 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Mar 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,279 | 599 | 67 | 2,945 | 130 | 72 | 94 |
- HTML: 2,279
- PDF: 599
- XML: 67
- Total: 2,945
- Supplement: 130
- BibTeX: 72
- EndNote: 94
Total article views: 1,212 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Sep 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 830 | 361 | 21 | 1,212 | 140 | 7 | 11 |
- HTML: 830
- PDF: 361
- XML: 21
- Total: 1,212
- Supplement: 140
- BibTeX: 7
- EndNote: 11
Viewed (geographical distribution)
Total article views: 4,157 (including HTML, PDF, and XML)
Thereof 3,883 with geography defined
and 274 with unknown origin.
Total article views: 2,945 (including HTML, PDF, and XML)
Thereof 2,725 with geography defined
and 220 with unknown origin.
Total article views: 1,212 (including HTML, PDF, and XML)
Thereof 1,158 with geography defined
and 54 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
16 citations as recorded by crossref.
- Unraveling the Microbiome–Environmental Change Nexus to Contribute to a More Sustainable World: A Comprehensive Review of Artificial Intelligence Approaches M. Barbosa et al. 10.3390/su17167209
- 矿物际概论及其环境效应研究进展 孟. 冯 et al. 10.1360/SSTe-2024-0103
- Optimised use of data fusion and memory‐based learning with an Austrian soil library for predictions with infrared data B. Ludwig et al. 10.1111/ejss.13394
- Overview of the Mineralosphere and research progress on its environmental effects M. Feng et al. 10.1007/s11430-024-1520-1
- Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density T. Ekundayo et al. 10.1016/j.envpol.2022.120734
- Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: A Meta-Analysis A. Chinilin et al. 10.1134/S1064229323601841
- Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: Meta-Analysis A. Chinilin et al. 10.31857/S0032180X23600695
- Fungal and Bacterial Community Dynamics in the Rhizosphere and Rhizoplane of Diabelia spathulata in Relation to Soil Properties Y. You et al. 10.1080/12298093.2025.2535775
- Soil microbial community construction under revegetation in newly created land S. Feng et al. 10.1016/j.scitotenv.2024.176496
- Characterization of clay and nanoclay extracted from a semi-arid Vertisol and investigation of their carbon sequestration potential N. Sadri et al. 10.1007/s10661-023-12246-x
- Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation Z. Shen et al. 10.5194/soil-8-467-2022
- A progressive knowledge strategy for monitoring soil microbiological activity by microscale spectroscopic detection H. Rodríguez-Albarracín et al. 10.1016/j.soilad.2025.100058
- Deep transfer learning of global spectra for local soil carbon monitoring Z. Shen et al. 10.1016/j.isprsjprs.2022.04.009
- An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. 10.1016/j.earscirev.2024.104797
- A curated soil fungal dataset to advance fungal ecology and conservation research in Australia and Antarctica L. Florence et al. 10.1038/s41597-025-04598-5
- Improving the accuracy of NIR detection of moldy core in apples using different diameter correction methods H. Li et al. 10.1016/j.postharvbio.2024.113279
16 citations as recorded by crossref.
- Unraveling the Microbiome–Environmental Change Nexus to Contribute to a More Sustainable World: A Comprehensive Review of Artificial Intelligence Approaches M. Barbosa et al. 10.3390/su17167209
- 矿物际概论及其环境效应研究进展 孟. 冯 et al. 10.1360/SSTe-2024-0103
- Optimised use of data fusion and memory‐based learning with an Austrian soil library for predictions with infrared data B. Ludwig et al. 10.1111/ejss.13394
- Overview of the Mineralosphere and research progress on its environmental effects M. Feng et al. 10.1007/s11430-024-1520-1
- Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density T. Ekundayo et al. 10.1016/j.envpol.2022.120734
- Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: A Meta-Analysis A. Chinilin et al. 10.1134/S1064229323601841
- Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: Meta-Analysis A. Chinilin et al. 10.31857/S0032180X23600695
- Fungal and Bacterial Community Dynamics in the Rhizosphere and Rhizoplane of Diabelia spathulata in Relation to Soil Properties Y. You et al. 10.1080/12298093.2025.2535775
- Soil microbial community construction under revegetation in newly created land S. Feng et al. 10.1016/j.scitotenv.2024.176496
- Characterization of clay and nanoclay extracted from a semi-arid Vertisol and investigation of their carbon sequestration potential N. Sadri et al. 10.1007/s10661-023-12246-x
- Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation Z. Shen et al. 10.5194/soil-8-467-2022
- A progressive knowledge strategy for monitoring soil microbiological activity by microscale spectroscopic detection H. Rodríguez-Albarracín et al. 10.1016/j.soilad.2025.100058
- Deep transfer learning of global spectra for local soil carbon monitoring Z. Shen et al. 10.1016/j.isprsjprs.2022.04.009
- An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning R. Viscarra Rossel et al. 10.1016/j.earscirev.2024.104797
- A curated soil fungal dataset to advance fungal ecology and conservation research in Australia and Antarctica L. Florence et al. 10.1038/s41597-025-04598-5
- Improving the accuracy of NIR detection of moldy core in apples using different diameter correction methods H. Li et al. 10.1016/j.postharvbio.2024.113279
Latest update: 26 Oct 2025
Short summary
We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
We present a new method to estimate the relative abundance of the dominant phyla and diversity...
