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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
29/06/2020 |
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Data da última atualização: |
02/07/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MOURA-BUENO, J. M.; DALMOLIN, R. S. D.; HORST-HEINEN, T. Z.; CATEN, A. ten; VASQUES, G. de M.; DOTTO, A. C.; GRUNWALD, S. |
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Afiliação: |
JEAN MICHEL MOURA-BUENO, UFSM; RICARDO SIMÃO DINIZ DALMOLIN, UFSM; TACIARA ZBOROWSKI HORST-HEINEN, UFSM; ALEXANDRE TEN CATEN, UFSC; GUSTAVO DE MATTOS VASQUES, CNPS; ANDRÉ CARNIELETTO DOTTO, ESALQ; SABINE GRUNWALD, UNIVERSITY OF FLORIDA. |
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Título: |
When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content? |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Science of The Total Environment, v. 737, 139895, Oct. 2020. |
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DOI: |
https://doi.org/10.1016/j.scitotenv.2020.139895 |
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Idioma: |
Inglês |
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Conteúdo: |
More accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions. MenosMore accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homog... Mostrar Tudo |
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Palavras-Chave: |
Aprendizagem ambiental; Carbono orgânico do solo; Environmental-based learning; Modelos espectrais; SOC variance; Spectral models; Spectral variation; Variação espectral; Vis-NIR data mining. |
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Thesaurus Nal: |
Soil organic carbon. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03175naa a2200325 a 4500
001 2123501
005 2020-07-02
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.scitotenv.2020.139895$2DOI
100 1 $aMOURA-BUENO, J. M.
245 $aWhen does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content?$h[electronic resource]
260 $c2020
520 $aMore accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions.
650 $aSoil organic carbon
653 $aAprendizagem ambiental
653 $aCarbono orgânico do solo
653 $aEnvironmental-based learning
653 $aModelos espectrais
653 $aSOC variance
653 $aSpectral models
653 $aSpectral variation
653 $aVariação espectral
653 $aVis-NIR data mining
700 1 $aDALMOLIN, R. S. D.
700 1 $aHORST-HEINEN, T. Z.
700 1 $aCATEN, A. ten
700 1 $aVASQUES, G. de M.
700 1 $aDOTTO, A. C.
700 1 $aGRUNWALD, S.
773 $tScience of The Total Environment$gv. 737, 139895, Oct. 2020.
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Embrapa Solos (CNPS) |
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