The soil analysis with vis-NIR spectroscopy in the laboratory has relatively higher measurement accuracy, as compared to those for in situ and on-line measurements using fresh (wet) soil conditions. The laboratory analysis requires sample pre-treatment and is subjected to minimum environmental interferes. However, the degree of accuracy depends on soil attribute to be measured, calibration geographical scale, sample statistics and sample size, type of spectrophotometer used and wavelength range, and soil type diversities. Generally, vis-NIR spectroscopy has proven to be a good tool for the prediction of soil MC, OC, SOM, TN and clay content (R2>0.8 and RPD>2.0) and to provide moderate or acceptable prediction accuracy for pH, Ca, CEC, P and Mg (R2>0.6 and RPD>1.5). However, literature suggests that it is quite
However, many of previous and current soil vis-NIR spectroscopic applications are limited to small geographic area e.g. field scale or homogenous soil type area. The larger scale modelling procedures with diverse soil type often results in less accurate model performance, or even complete failure might occurs. There are few successful calibrations using farm, regional or global soil spectral libraries, where spiking might offer potential solutions for enhancing model performance.
In comparison to laboratory application, in situ measurement of soil properties is sensors with several successful applications, there is only one commercially available sensing system from Veris (Veris Technologies, KS, the USA). However, practical applications proved this sensor to be sensitive to breakage due to the sapphire optical window, which is in direct contact with soil and potential presence of solid objects like stones. The vis-NIR on-line sensor designed by Mouazen (2006a) and available at Cranfield University for further development proved to be simple, robust and does not have sapphire window sensitive to breakage.
In comparison with electrical and electromagnetic sensors which are being widely used in PA today, the vis-NIR on-line sensors provide quantitative measurement of many soil attributes simultaneously. This character is essential for site specific application of different input in PA, which needs detailed (quantitative) information about the spatial variations in soil properties collected at low acquisition cost. Development of reliable and efficient methods of on-line measurement of soil properties using available hardware will be the direction of future research to be considered in this thesis.
3 Effect of multivariate calibration technique 3.1 Introduction
The vis and NIR diffuse reflectance spectroscopy has become increasingly attracting to researchers (e.g. Ben-Dor and Banin, 1995; Chang et al., 2001; Reeves and McCarty;
2001; Shepherd and Walsh, 2002; Brown et al., 2006; Mouazen et al., 2007; Zornoza et al., 2008), due to well recognised advantages of this technique as compared to the laboratory reference methods of soil analysis. Although vis-NIR spectroscopy allows for rapid, cost effective and intensive sampling, researchers admit shortcomings associated with instrumentation instability to ambient conditions (e.g. light, temperature, etc), transferability of calibration between different instruments, difficulties associated with model scale (global, continental, regional, country, local and field) vs accuracy and others. Under in situ measurement conditions with non-mobile or mobile instrumentation, additional challenges associated with diverse soil moisture content, texture, colour, harsh field conditions, dust, stones and excessive residues and surface roughness all affect the accuracy of measurement with vis-NIR spectroscopy (Mouazen et al., 2007; Waiser et al., 2007). To compensate or overcome one or more of these difficulties, some solutions were suggested and implemented by researchers. This includes, among other methods, the selection of proper instrumentation e.g. spectrophotometer, optical accessories and optical probe design (Mouazen et al., 2009), improved spectra filtering and pre-processing (Maleki et al., 2008), better control of ambient conditions (Mouazen et al., 2007; Waiser et al., 2007) and the successful selection of the multivariate statistical analysis.
Probably one of the most recommended solutions to enhance the accuracy of vis-NIR measurement of soil properties is the successful development of calibration models. A comprehensive review of literature on calibration methods on diffuse reflectance
common calibration techniques was provided by Viscarra Rossel et al. (2006a). These calibration methods include multiple linear regression analysis (e.g. Dalal and Henry, 1986) and a stepwise multiple linear regression (e.g. Shibusawa et al. 2001), multivariate adaptive regression splines (Shepherd and Walsh, 2002), PCR (e.g. Chang et al., 2001), PLSR (e.g. McCarty et al., 2002) and boosted regression trees (Brown et al., 2006). Five multivariate techniques, namely, SMLR, PCR, PLSR, regression tree and committee trees were compared by Vasques et al., (2008) with the aim of identifying the best combination of multivariate statistics and spectra pre-processing to predict soil carbon. They concluded that PLSR performed the best as compared to other techniques tested. Although the linear PCR and PLSR analyses are the most common techniques for spectral calibration and prediction (Viscarra Rossel et al., 2006a), with PLSR being the most accurate, other nonlinear techniques e.g. ANN have got much less attention and were rarely explored for the spectral analysis in soil sciences. Du and Zhou (2007) and Du et al. (2007; 2008) have successfully implement ANN based on principal components (PCs) obtained from PCA on MIR and photoacoustic MIR soil spectra. Only two examples on using ANN-PCs technique for soil analysis with NIR spectroscopy could be found in literature. Fidêncio et al. (2002) have implemented the RBFN in the NIR region (1000-2500 nm) and Daniel et al. (2003) have used ANN in the vis–NIR region (400–1100 nm). No literature is available about combining PLSR with ANN for the analysis of soil properties with full range vis-NIR spectroscopy (350-2500 nm) has been explored so far.
The scope of this chapter is to compare the performances of linear PCR and PLSR and non-linear BPNN analyses for the prediction accuracy of spectrally active (OC) and inactive (K, Mg, Na and P) soil properties using vis-NIR diffuse reflectance spectroscopy. The BPNN analyses will be based on latent variables (LVs) obtained from PLSR (BPNN-LVs) and PCs obtained from PCA (BPNN-PCs) (Martens and Naes, 1989).