Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
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{"title"=>"Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy", "type"=>"journal", "authors"=>[{"first_name"=>"Antoine", "last_name"=>"Stevens", "scopus_author_id"=>"13411569000"}, {"first_name"=>"Marco", "last_name"=>"Nocita", "scopus_author_id"=>"53985246700"}, {"first_name"=>"Gergely", "last_name"=>"Tóth", "scopus_author_id"=>"57194859625"}, {"first_name"=>"Luca", "last_name"=>"Montanarella", "scopus_author_id"=>"6603441055"}, {"first_name"=>"Bas", "last_name"=>"van Wesemael", "scopus_author_id"=>"56888870100"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0066409", "sgr"=>"84879224083", "issn"=>"19326203", "pui"=>"369155297", "isbn"=>"1932-6203", "pmid"=>"23840459", "scopus"=>"2-s2.0-84879224083"}, "id"=>"45495f99-6fae-390a-b0c3-cf1caac0bb0d", "abstract"=>"Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 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.", "link"=>"http://www.mendeley.com/research/prediction-soil-organic-carbon-european-scale-visible-near-infrared-reflectance-spectroscopy-2", "reader_count"=>121, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Researcher"=>28, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>33, "Student > Postgraduate"=>5, "Student > Master"=>19, "Other"=>6, "Student > Bachelor"=>5, "Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>6, "Researcher"=>28, "Student > Doctoral Student"=>12, "Student > Ph. D. Student"=>33, "Student > Postgraduate"=>5, "Student > Master"=>19, "Other"=>6, "Student > Bachelor"=>5, "Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Unspecified"=>14, "Environmental Science"=>30, "Mathematics"=>2, "Agricultural and Biological Sciences"=>34, "Physics and Astronomy"=>1, "Chemistry"=>4, "Computer Science"=>2, "Earth and Planetary Sciences"=>29}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Chemistry"=>{"Chemistry"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>29}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>34}, "Computer Science"=>{"Computer Science"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>14}, "Environmental Science"=>{"Environmental Science"=>30}}, "reader_count_by_country"=>{"Ecuador"=>1, "Czech Republic"=>1, "Norway"=>1, "Poland"=>1, "Brazil"=>1, "Chile"=>1, "Kenya"=>1, "Germany"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1093089"], "description"=>"<p>The principal component analysis has been realized separately for mineral (top panel) and organic (bottom panel) soil materials.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "eigenvalues", "components", "continuum-removed"], "article_id"=>725185, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Eigenvectors_and_eigenvalues_of_the_first_three_principal_components_of_continuum_removed_spectra_/725185", "title"=>"Eigenvectors and eigenvalues of the first three principal components of continuum-removed spectra.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-19 01:26:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093090"], "description"=>"<p>Model predictions are shown for models with (rfe+aux, right panels) and without auxiliary predictors (spc, left panels).</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "soc", "observed"], "article_id"=>725186, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.g003", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_SOC_content_as_a_function_of_observed_SOC_content_in_test_sets_/725186", "title"=>"Predicted SOC content as a function of observed SOC content in test sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-19 01:26:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093093"], "description"=>"<p>The sand classes are 0–25%, 25–50%, 50–75%, 75–100% and the SOC classes are 0–25 g C kg<sup>−1</sup>, 25–50 g C kg<sup>−1</sup>, 50–75 g C kg<sup>−1</sup>, 75–200 g C kg<sup>−1</sup>. Each panel regroups samples of a given SOC interval.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "reflectance", "continuum-removed", "spectra", "lucas", "computed", "arbitrary", "soc"], "article_id"=>725189, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.g004", "stats"=>{"downloads"=>3, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_reflectance_left_scale_and_continuum_removed_reflectance_right_scale_spectra_of_LUCAS_mineral_soil_samples_computed_for_arbitrary_sand_and_SOC_classes_/725189", "title"=>"Mean reflectance (left scale) and continuum-removed reflectance (right scale) spectra of LUCAS mineral soil samples, computed for arbitrary sand and SOC classes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-19 01:26:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093096"], "description"=>"<p>The clay classes are 0–20%, 20–40%, 40–60%, 60–80% and the SOC classes are 0–25 g C kg<sup>−1</sup>, 25–50 g C kg<sup>−1</sup>, 50–75 g C kg<sup>−1</sup>, 75–200 g C kg<sup>−1</sup>. Each panel regroups samples of a given SOC interval.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "reflectance", "continuum-removed", "spectra", "lucas", "computed", "arbitrary", "soc"], "article_id"=>725192, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.g005", "stats"=>{"downloads"=>3, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_reflectance_left_scale_and_continuum_removed_reflectance_right_scale_spectra_of_LUCAS_mineral_soil_samples_computed_for_arbitrary_clay_and_SOC_classes_/725192", "title"=>"Mean reflectance (left scale) and continuum-removed reflectance (right scale) spectra of LUCAS mineral soil samples, computed for arbitrary clay and SOC classes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-19 01:26:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093097"], "description"=>"<p>The sand classes are 0–25%, 25–50%, 50–75%, 75–100% and the SOC classes are 0–25 g C kg<sup>−1</sup>, 25–50 g C kg<sup>−1</sup>, 50–200 g C kg<sup>−1</sup>. The relative <i>RMSEP</i> is the <i>RMSEP</i> divided by the mean of observed SOC values of models developed with (red bars) and without auxiliary predictors (blue bars). Each panel regroups mineral samples of a given SOC interval and land cover type. The number of training samples (n) for each class of SOC content is given in each panel.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "arbitrary", "classes", "soc"], "article_id"=>725193, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.g006", "stats"=>{"downloads"=>3, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_Root_Mean_Square_Error_of_Prediction_RMSEP_per_land_cover_for_arbitrary_classes_of_SOC_and_sand_content_/725193", "title"=>"Relative Root Mean Square Error of Prediction (<i>RMSEP</i>) per land cover, for arbitrary classes of SOC and sand content.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-19 01:26:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093098"], "description"=>"a<p>Number of duplicate samples.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "soc", "estimates", "spectroscopic", "auxiliary"], "article_id"=>725194, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.t005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reproducibility_of_SOC_estimates_g_C_kg_8722_1_Eq_6_of_the_reference_method_and_the_spectroscopic_models_with_rfe_aux_and_without_spc_the_use_of_auxiliary_predictors_/725194", "title"=>"Reproducibility of SOC estimates (g C kg<sup>−1</sup>; Eq. 6) of the reference method and the spectroscopic models with (rfe+aux) and without (spc) the use of auxiliary predictors.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-19 01:26:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093099"], "description"=>"a<p>Spectral transformation (SG0 =  Savitzky-Golay smoothing; SG1 =  Savitzky-Golay first derivative; SNV = standard normal variate);</p>b<p>Multivariate Calibration Model (svm = support vector machine regression; cubist = Cubist);</p>c<p>Predictor used in the models (spc = spectral matrix; rfe = spectral matrix with bands selected by recursive feature elimination);</p>d<p>Standard Deviation of the observations (g C kg<sup>−1</sup>);</p>e<p>Root Mean Square Error of Prediction (g C kg<sup>−1</sup>; Eq. 4);</p>f<p>Bias (g C kg<sup>−1</sup>; Eq. 2);</p>g<p>Standard Error of Prediction (g C kg<sup>−1</sup>; Eq. 3);</p>h<p>Ratio of Performance to Deviation (Eq. 5);</p>i<p>Number of validation samples.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "spectroscopic"], "article_id"=>725195, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.t004", "stats"=>{"downloads"=>7, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_best_spectroscopic_models_as_measured_against_the_test_set_/725195", "title"=>"Performance of the best spectroscopic models as measured against the test set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-19 01:26:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093100"], "description"=>"a<p>Standard Deviation;</p>b<p>lower quartile;</p>c<p>median;</p>d<p>upper quartile,</p>e<p>correlation of PC1-3 scores with the soil properties;</p>f<p>number of samples.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "lucas"], "article_id"=>725196, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.t003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_of_soil_properties_available_in_the_LUCAS_database_for_mineral_and_organic_soil_materials_/725196", "title"=>"Summary statistics of soil properties available in the LUCAS database, for mineral and organic soil materials.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-19 01:26:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093101"], "description"=>"a<p>Percentage of the total surface occupied WRB major soil groups. Data should be considered approximate: surfaces have been computed using the dominant value of the soil typological units of the European Soil Database <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066409#pone.0066409-European1\" target=\"_blank\">[58]</a>. The total land surface considered is the European continent and islands (United Kingdom, Ireland, Iceland, Malta, Sicily, Sardinia, Corsica,…), excluding Russia and Turkey.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "samples", "occupied", "groups"], "article_id"=>725197, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.t002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_samples_n_frequency_in_of_the_total_number_of_samples_and_surface_in_of_the_total_surface_occupied_by_World_Reference_Base_WRB_major_soil_groups_30_/725197", "title"=>"Number of samples (n), frequency (in% of the total number of samples) and surface (in % of the total surface) occupied by World Reference Base (WRB) major soil groups [30].", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-19 01:26:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093102"], "description"=>"a<p>Percentage of the total surface occupied by land cover type in the 23 EU countries of the LUCAS survey <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066409#pone.0066409-Eurostat1\" target=\"_blank\">[25]</a>.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "samples", "occupied", "defined", "lucas"], "article_id"=>725198, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_samples_n_frequency_in_of_the_total_number_of_samples_and_surface_in_of_the_total_surface_occupied_by_land_cover_type_as_defined_in_the_LUCAS_survey_31_/725198", "title"=>"Number of samples (n), frequency (in% of the total number of samples) and surface (in % of the total surface) occupied by land cover type as defined in the LUCAS survey [31].", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-19 01:26:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093104", "https://ndownloader.figshare.com/files/1093105", "https://ndownloader.figshare.com/files/1093106"], "description"=>"<div><p>Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO<sub>2</sub> 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<sup>−1</sup> for mineral soils and a root mean square error of 50 g C kg<sup>−1</sup> 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.</p></div>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "carbon", "european", "infrared", "reflectance"], "article_id"=>725200, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0066409.s001", "https://dx.doi.org/10.1371/journal.pone.0066409.s002", "https://dx.doi.org/10.1371/journal.pone.0066409.s003"], "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_Soil_Organic_Carbon_at_the_European_Scale_by_Visible_and_Near_InfraRed_Reflectance_Spectroscopy_/725200", "title"=>"Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-06-19 01:26:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1093087"], "description"=>"<p>Map labels give the total number of samples per country.</p>", "links"=>[], "tags"=>["Soil science", "geoinformatics", "Environmental systems modeling", "Remote sensing imagery", "geochemistry", "Carbon cycle", "geography", "lucas", "european", "territorial"], "article_id"=>725183, "categories"=>["Information And Computing Sciences", "Mathematics", "Medicine", "Sociology", "Earth and Environmental Sciences"], "users"=>["Antoine Stevens", "Marco Nocita", "Gergely Tóth", "Luca Montanarella", "Bas van Wesemael"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0066409.g001", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sampling_density_of_the_LUCAS_SOIL_database_per_European_territorial_units_level_1_NUTS_1_/725183", "title"=>"Sampling density of the LUCAS SOIL database per European territorial units, level 1 (NUTS 1).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-19 01:26:23"}

PMC Usage Stats | Further Information

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Relative Metric

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