Digital Soil Mapping of Soil Properties in the "Mar de Morros" Environment Using Spectral Data

被引:10
作者
da Matta Campbell, Patricia Morals [1 ]
Fernandes Filho, Elpidio Inacio [2 ]
Francelino, Marcio Rocha [2 ]
Melo Dematte, Jose Alexandre [3 ]
Pereira, Marcos Gervasio [4 ]
Barbosa Guimaraes, Clecia Cristina [5 ]
da Silva Rodrigues Pinte, Luiz Alberto [6 ]
机构
[1] Univ Fed Rural Rio de Janeiro, Inst Florestas, Dept Silvicultura, Programa Posgrad Ciencias Ambientais & Florestais, Rio De Janeiro, Brazil
[2] Univ Fed Vicosa, Dept Solos, Vicosa, MG, Brazil
[3] Univ Sao Paulo, Escola Super Agr Luiz de Queiroz, Dept Ciencia Solo, Sao Paulo, Brazil
[4] Univ Fed Rural Rio de Janeiro, Inst Agron, Dept Solos, Rio De Janeiro, Brazil
[5] Univ Sao Paulo, Escola Super Agr Luiz de Queiroz, Dept Ciencia Solo, Programa Posgrad Solos & Nutr Plantas, Sao Paulo, Brazil
[6] Univ Fed Rural Rio de Janeiro, Curso Agron, Rio De Janeiro, Brazil
来源
REVISTA BRASILEIRA DE CIENCIA DO SOLO | 2018年 / 42卷
关键词
spectral analysis; reflectance; chemometrics; INFRARED REFLECTANCE SPECTROSCOPY; DIFFUSE-REFLECTANCE; ORGANIC-MATTER; PREDICTING SOIL; MIDINFRARED SPECTROSCOPY; CARBON FRACTIONS; LEAST-SQUARES; CLAY CONTENT; NIR; QUANTIFICATION;
D O I
10.1590/18069657rbcs20170413
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
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, Espirito Santo State, Brazil, in the Ribeirao Inhauma 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 (R-2 ) 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 R-2 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.
引用
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页数:19
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