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Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression
This paper presents the second part of the mapping of topsoil properties based on the Land Use and Cover Area frame Survey (LUCAS). The first part described the physical properties (Ballabio et al., 2016) while this second part includes the following chemical properties: pH, Cation Exchange Capacity...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Scientific Pub. Co
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743211/ https://www.ncbi.nlm.nih.gov/pubmed/31798185 http://dx.doi.org/10.1016/j.geoderma.2019.113912 |
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author | Ballabio, Cristiano Lugato, Emanuele Fernández-Ugalde, Oihane Orgiazzi, Alberto Jones, Arwyn Borrelli, Pasquale Montanarella, Luca Panagos, Panos |
author_facet | Ballabio, Cristiano Lugato, Emanuele Fernández-Ugalde, Oihane Orgiazzi, Alberto Jones, Arwyn Borrelli, Pasquale Montanarella, Luca Panagos, Panos |
author_sort | Ballabio, Cristiano |
collection | PubMed |
description | This paper presents the second part of the mapping of topsoil properties based on the Land Use and Cover Area frame Survey (LUCAS). The first part described the physical properties (Ballabio et al., 2016) while this second part includes the following chemical properties: pH, Cation Exchange Capacity (CEC), calcium carbonates (CaCO(3)), C:N ratio, nitrogen (N), phosphorus (P) and potassium (K). The LUCAS survey collected harmonised data on changes in land cover and the state of land use for the European Union (EU). Among the 270,000 land use and cover observations selected for field visit, approximately 20,000 soil samples were collected in 24 EU Member States in 2009 together with more than 2000 samples from Bulgaria and Romania in 2012. The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. GPR was selected for its capacity to assess model uncertainty and the possibility of adding prior knowledge in the form of covariance functions to the model. The derived maps will establish baselines that will help monitor soil quality and provide guidance to agro-environmental research and policy developments in the European Union. |
format | Online Article Text |
id | pubmed-6743211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier Scientific Pub. Co |
record_format | MEDLINE/PubMed |
spelling | pubmed-67432112019-12-01 Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression Ballabio, Cristiano Lugato, Emanuele Fernández-Ugalde, Oihane Orgiazzi, Alberto Jones, Arwyn Borrelli, Pasquale Montanarella, Luca Panagos, Panos Geoderma Article This paper presents the second part of the mapping of topsoil properties based on the Land Use and Cover Area frame Survey (LUCAS). The first part described the physical properties (Ballabio et al., 2016) while this second part includes the following chemical properties: pH, Cation Exchange Capacity (CEC), calcium carbonates (CaCO(3)), C:N ratio, nitrogen (N), phosphorus (P) and potassium (K). The LUCAS survey collected harmonised data on changes in land cover and the state of land use for the European Union (EU). Among the 270,000 land use and cover observations selected for field visit, approximately 20,000 soil samples were collected in 24 EU Member States in 2009 together with more than 2000 samples from Bulgaria and Romania in 2012. The chemical properties maps for the European Union were produced using Gaussian process regression (GPR) models. GPR was selected for its capacity to assess model uncertainty and the possibility of adding prior knowledge in the form of covariance functions to the model. The derived maps will establish baselines that will help monitor soil quality and provide guidance to agro-environmental research and policy developments in the European Union. Elsevier Scientific Pub. Co 2019-12-01 /pmc/articles/PMC6743211/ /pubmed/31798185 http://dx.doi.org/10.1016/j.geoderma.2019.113912 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ballabio, Cristiano Lugato, Emanuele Fernández-Ugalde, Oihane Orgiazzi, Alberto Jones, Arwyn Borrelli, Pasquale Montanarella, Luca Panagos, Panos Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression |
title | Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression |
title_full | Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression |
title_fullStr | Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression |
title_full_unstemmed | Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression |
title_short | Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression |
title_sort | mapping lucas topsoil chemical properties at european scale using gaussian process regression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743211/ https://www.ncbi.nlm.nih.gov/pubmed/31798185 http://dx.doi.org/10.1016/j.geoderma.2019.113912 |
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