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Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO(2) with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack...

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Autores principales: Stevens, Antoine, Nocita, Marco, Tóth, Gergely, Montanarella, Luca, van Wesemael, Bas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3686688/
https://www.ncbi.nlm.nih.gov/pubmed/23840459
http://dx.doi.org/10.1371/journal.pone.0066409
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author Stevens, Antoine
Nocita, Marco
Tóth, Gergely
Montanarella, Luca
van Wesemael, Bas
author_facet Stevens, Antoine
Nocita, Marco
Tóth, Gergely
Montanarella, Luca
van Wesemael, Bas
author_sort Stevens, Antoine
collection PubMed
description Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO(2) with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(−1) for mineral soils and a root mean square error of 50 g C kg(−1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.
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spelling pubmed-36866882013-07-09 Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy Stevens, Antoine Nocita, Marco Tóth, Gergely Montanarella, Luca van Wesemael, Bas PLoS One Research Article Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO(2) with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(−1) for mineral soils and a root mean square error of 50 g C kg(−1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation. Public Library of Science 2013-06-19 /pmc/articles/PMC3686688/ /pubmed/23840459 http://dx.doi.org/10.1371/journal.pone.0066409 Text en © 2013 Stevens et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Stevens, Antoine
Nocita, Marco
Tóth, Gergely
Montanarella, Luca
van Wesemael, Bas
Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
title Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
title_full Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
title_fullStr Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
title_full_unstemmed Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
title_short Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
title_sort prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3686688/
https://www.ncbi.nlm.nih.gov/pubmed/23840459
http://dx.doi.org/10.1371/journal.pone.0066409
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