Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties

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• 作者：R.A. Viscarra Rossel ; D.J.J. Walvoort ; A.B. McBratney ; L.J. Janik and J.O. Skjemstad
• 关键词：Soil analysis ; Diffuse infrared reflectance spectroscopy ; Visible spectroscopy ; NIR ; MIR partial least-squares regression
• 刊名：Geoderma
• 出版年：2006
• 出版时间：March 2006
• 年：2006
• 卷：131
• 期：1-2
• 页码：59-75
• 全文大小：373 K

文摘

Historically, our understanding of the soil and assessment of its quality and function has been gained through routine soil chemical and physical laboratory analysis. There is a global thrust towards the development of more time- and cost-efficient methodologies for soil analysis as there is a great demand for larger amounts of good quality, inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Diffuse reflectance spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it overcomes some of their limitations. Spectroscopy is rapid, timely, less expensive, non-destructive, straightforward and sometimes more accurate than conventional analysis. Furthermore, a single spectrum allows for simultaneous characterisation of various soil properties and the techniques are adaptable for ‘on-the-go’ field use. The aims of this paper are threefold: (i) determine the value of qualitative analysis in the visible (VIS) (400–700 nm), near infrared (NIR) (700–2500 nm) and mid infrared (MIR) (2500–25,000 nm); (ii) compare the simultaneous predictions of a number of different soil properties in each of these regions and the combined VIS–NIR–MIR to determine whether the combined information produces better predictions of soil properties than each of the individual regions; and (iii) deduce which of these regions may be best suited for simultaneous analysis of various soil properties. In this instance we implemented partial least-squares regression (PLSR) to construct calibration models, which were independently validated for the prediction of various soil properties from the soil spectra. The soil properties examined were soil pH_Ca, pH_w, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity (CEC), exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate–nitrogen (NO₃–N), available phosphorus (P_Col), exchangeable potassium (K) and electrical conductivity (EC). Our results demonstrated the value of qualitative soil interpretations using the loading weight vectors from the PLSR decomposition. The MIR was more suitable than the VIS or NIR for this type of analysis due to the higher incidence spectral bands in this region as well as the higher intensity and specificity of the signal. Quantitatively, the accuracy of PLSR predictions in each of the VIS, NIR, MIR and VIS–NIR–MIR spectral regions varied considerably amongst properties. However, more accurate predictions were obtained using the MIR for pH, LR, OC, CEC, clay, silt and sand contents, P and EC. The NIR produced more accurate predictions for exchangeable Al and K than any of the ranges. There were only minor improvements in predictions of clay, silt and sand content using the combined VIS–NIR–MIR range. This work demonstrates the potential of diffuse reflectance spectroscopy using the VIS, NIR and MIR for more efficient soil analysis and the acquisition of soil information.