Data/hora: 06/06/2024 02:49:59

Provedor de dados: 	90	
País: 	Brazil	
Título: 	Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data	
Autores: 	Campbell,Patrícia Morais da Matta; Fernandes Filho,Elpídio Inácio; Francelino,Márcio Rocha; Demattê,José Alexandre Melo; Pereira,Marcos Gervasio; and,Clécia Cristina Barbosa Guimarães; Pinto,Luiz Alberto da Silva Rodrigues.	
Data: 	2018-01-01	
Ano: 	2018	
Palavras-chave: 	Spectral analysis; Reflectance; Chemometrics.	
Resumo: 	ABSTRACT Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly necessary as a viable way to estimate a wide range of soil properties. The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus (P), from the spectral responses of soil samples in the visible and near infrared (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR using different preprocessing methods combined with five prediction models. Soil samples were collected in Iconha, Espírito Santo State, Brazil, in the Ribeirão Inhaúma basin. A total of 184 samples were collected from 92 sites at two depths (0.00-0.10 and 0.10-0.30 m). Physical, chemical, and spectral analyses were performed according to routine soil laboratory methods. Random selection was made of 70 % of total samples for training and 30 % for validation of the models. The coefficient of determination (R2) and root mean square error (RMSE) were calculated in order to assess model performance. The standardized indexes of prediction error RPD and RPIQ were also calculated. For clay and OC, the best R2 was found in the MIR spectrum, at 0.69 and 0.65, respectively, and for P, it was 0.57 in Vis-NIR. The MSC (Multiplicative Scatter Correction), CR (Continuum removal), and SNV (Standard Normal Variate) preprocesses were most efficient for predicting clay, OC, and P, respectively, while the PLSR - Partial Least Squares Regression (OC and P) and SVM - Support Vector Machine (clay) gave the best predictions and are therefore recommended for modeling these properties in the study area. The models identified in this study can be used to discriminate soils according to a critical test value for clay, OC, and P.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100314	
Editor: 	Sociedade Brasileira de Ciência do Solo	
Relação: 	10.1590/18069657rbcs20170413	
Formato: 	text/html	
Fonte: 	Revista Brasileira de Ciência do Solo  v.42 2018	
Fonte: 	info:eu-repo/semantics/openAccess