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Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging
Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141473/ https://www.ncbi.nlm.nih.gov/pubmed/30224656 http://dx.doi.org/10.1038/s41598-018-31776-w |
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author | Hobley, Eleanor Steffens, Markus Bauke, Sara L. Kögel-Knabner, Ingrid |
author_facet | Hobley, Eleanor Steffens, Markus Bauke, Sara L. Kögel-Knabner, Ingrid |
author_sort | Hobley, Eleanor |
collection | PubMed |
description | Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns. |
format | Online Article Text |
id | pubmed-6141473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61414732018-09-20 Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging Hobley, Eleanor Steffens, Markus Bauke, Sara L. Kögel-Knabner, Ingrid Sci Rep Article Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns. Nature Publishing Group UK 2018-09-17 /pmc/articles/PMC6141473/ /pubmed/30224656 http://dx.doi.org/10.1038/s41598-018-31776-w Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hobley, Eleanor Steffens, Markus Bauke, Sara L. Kögel-Knabner, Ingrid Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
title | Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
title_full | Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
title_fullStr | Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
title_full_unstemmed | Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
title_short | Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
title_sort | hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141473/ https://www.ncbi.nlm.nih.gov/pubmed/30224656 http://dx.doi.org/10.1038/s41598-018-31776-w |
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