Near‐Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties

Abstract

A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to predict diverse soil properties. Near-infrared reflectance spectra, obtained from a Perstrop NIR Systems 6500 scanning monochromator (Foss NIRSystems, Silver Spring, MD), and 33 chemical, physical, and biochemical properties were studied for 802 soil samples collected from four Major Land Resource Areas (MLRAs). Calibrations were based on principal component regression (PCR) using the first derivatives of optical density [log(1/R)] for the 1300-to 2500-nm spectral range. Total C, total N, moisture, cation-exchange capacity (CEC), 1.5 MPa water, basal respiration rate, sand, silt, and Mehlich III extractable Ca were successfully predicted by NIRS (r 2 > 0.80). Some Mehlich III extractable metals (Fe, K, Mg, Mn) and exchangeable cations (Ca, Mg, and K), sum of exchangeable bases, exchangeable acidity, clay, potentially mineralizable N, total respiration rate, biomass C, and pH were also estimated by NIRS but with less accuracy (r 2 = 0.80∼0.50). The predicted results for aggregation (wt% > 2, 1, 0.5, 0.25 mm, and macroaggregation) were not reliable (r 2 = 0.46∼0.60). Mehlich III extractable Cu, P, and Zn, and exchangeable Na could not be predicted using the NIRS-PCR technique (r 2 < 0.50). The results indicate that NIRS can be used as a rapid analytical technique to simultaneously estimate several soil properties with acceptable accuracy in a very short time.

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

“…The prediction models were evaluated using the following parameters: the coefficient of determination (R 2 ), root mean square error (RMSE), bias, the ratio of performance to deviation (RPD) and the ratio of performance to interquartile distance (RPIQ) [60,61].…”

Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices

“…To overcome these challenges, some chemometric tools have been used to be applied to the quantitative analysis of the spectroscopic data [38]. These chemometric tools include multiple linear regression (MLR) [39], principal component regression (PCR) [40,41], and partial least squares (PLS) regression [42]. These chemometric tools have been used to characterize soil spectra and build models for estimating the trace metal concentrations in soil or sediments and other matrices [25].…”

“…Principal component regression (PCR) is a chemometric tool that combines principal component analysis and MLR [18,42]. In this method, the independent variables are first decomposed into orthogonal principal components using the nonlinear iterative partial least squares algorithm and full cross validation of the calibration set [18].…”

“…There is very limited information on the application of NIRS-PCR on the analysis of trace metals in different matrices. However, some of the reports are available in the literature [42,43].…”

Determination of water-soluble and total extractable polyphenolics in biomass, necromass and decomposing plant material using near-infrared reflectance spectroscopy (NIRS)

“…If 0.2!SEC-to-SD ratio%0.5, quantitative predictions is possible . The SDto-SECV ratio should be R2 (Chang et al, 2001;Chang and Laird, 2002), SEP-to-SEC%1.2 and the SD-to-SEP ratio should be R2.5 (Mathison et al, 1999). In this study the R 2 value ranged from 0.90 to 0.93, the SEC-to-SD ratio from 0.27 to 0.32, SD-to-SECV ratio from 1.3 to 2.2, the SEP-to-SEC ratio from 0.97 to 1.25 and the SD-to-SEP from 2.69 to 3.85.…”