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Near infrared spectroscopy for soil bulk density assessment
Author(s) -
Moreira C. S.,
Brunet D.,
Verneyre L.,
Sá S. M. O.,
Galdos M. V.,
Cerri C. C.,
Bernoux M.
Publication year - 2009
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/j.1365-2389.2009.01170.x
Subject(s) - pedotransfer function , bulk density , soil science , environmental science , partial least squares regression , soil carbon , coefficient of determination , soil test , diffuse reflectance infrared fourier transform , soil organic matter , soil water , chemistry , mathematics , statistics , hydraulic conductivity , biochemistry , photocatalysis , catalysis
Soil bulk density values are needed to convert organic carbon content to mass of organic carbon per unit area. However, field sampling and measurement of soil bulk density are labour‐intensive, costly and tedious. Near‐infrared reflectance spectroscopy (NIRS) is a physically non‐destructive, rapid, reproducible and low‐cost method that characterizes materials according to their reflectance in the near‐infrared spectral region. The aim of this paper was to investigate the ability of NIRS to predict soil bulk density and to compare its performance with published pedotransfer functions. The study was carried out on a dataset of 1184 soil samples originating from a reforestation area in the Brazilian Amazon basin, and conventional soil bulk density values were obtained with metallic “core cylinders”. The results indicate that the modified partial least squares regression used on spectral data is an alternative method for soil bulk density predictions to the published pedotransfer functions tested in this study. The NIRS method presented the closest‐to‐zero accuracy error (−0.002 g cm −3 ) and the lowest prediction error (0.13 g cm −3 ) and the coefficient of variation of the validation sets ranged from 8.1 to 8.9% of the mean reference values. Nevertheless, further research is required to assess the limits and specificities of the NIRS method, but it may have advantages for soil bulk density predictions, especially in environments such as the Amazon forest.