Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging
Abstract 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 pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2018-09-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-018-31776-w |
| _version_ | 1818347093272559616 |
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| author | Eleanor Hobley Markus Steffens Sara L. Bauke Ingrid Kögel-Knabner |
| author_facet | Eleanor Hobley Markus Steffens Sara L. Bauke Ingrid Kögel-Knabner |
| author_sort | Eleanor Hobley |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-13T17:28:41Z |
| format | Article |
| id | doaj.art-cad5d2614eec4001a62be9e382adf939 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-13T17:28:41Z |
| publishDate | 2018-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-cad5d2614eec4001a62be9e382adf9392022-12-21T23:37:06ZengNature PortfolioScientific Reports2045-23222018-09-018111310.1038/s41598-018-31776-wHotspots of soil organic carbon storage revealed by laboratory hyperspectral imagingEleanor Hobley0Markus Steffens1Sara L. Bauke2Ingrid Kögel-Knabner3Soil Science, Technical University of MunichResearch Institute of Organic Agriculture FibLInstitute of Crop Science and Resource Conservation, Soil Science and Soil Ecology, University of BonnSoil Science, Technical University of MunichAbstract 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.https://doi.org/10.1038/s41598-018-31776-wHyperspectral ImagingSoil Organic Carbon (SOC)Plow HorizonSoil CoresSubsoil |
| spellingShingle | Eleanor Hobley Markus Steffens Sara L. Bauke Ingrid Kögel-Knabner Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging Scientific Reports Hyperspectral Imaging Soil Organic Carbon (SOC) Plow Horizon Soil Cores Subsoil |
| 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 | Hyperspectral Imaging Soil Organic Carbon (SOC) Plow Horizon Soil Cores Subsoil |
| url | https://doi.org/10.1038/s41598-018-31776-w |
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