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

2005

R.A. VISCARRA ROSSEL^1,2, D.J.J. WALVOORT^1,3, A.B. McBRATNEY¹, L.J. JANIK⁴, J.O. SKJEMSTAD⁴

¹Australian Centre for Precision Agriculture, McMillan Building A05, The University of Sydney, NSW 2006, Australia
²INRA Laboratoire de Science du Sol, 35042 Rennes, France
³Wageningen University & Research Centre, Wageningen, The Netherlands
⁴CSIRO Land and Water, Glen Osmond SA 5064, Australia

Geoderma, (available online 3 May 2005).

Abstract

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 – 25000 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 looked at were soil pHCa, pHw, 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 (PCol), exchangeable potassium (K) and electrical conductivity (EC). Our results showed 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 of high intensity spectral bands in this region. Quantitatively, the accuracy of PLSR predictions in each of the spectral regions varied amongst properties, however, predictions using the MIR for pH, LR, OC, CEC, clay, silt and sand contents, P and EC were better. 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.

Keywords: soil analysis, diffuse reflectance spectroscopy, partial least-squares regression