Agriculture Reference
In-Depth Information
VNIR-SWIR region is modeled against constituents determined by traditional chemical
analysis and then used to predict unknown samples. This technology permits rapid and
cost-effective measurements on many samples at a given location and therefore significantly
reduces chemical analysis costs. Despite the heterogeneity of the methodology, it was
demonstrated by many (e.g: Nanni & Demattê 2006; Ben-Dor, et al. 2008; Rossel & Behrens
2010) that soil constituents can be extracted from a simple reflectance measurement
(laboratory and field) if an appropriate calibration model has been previously developed.
Since Ben-Dor et al. (1995)'s pioneering study, many other workers have explored this
promising technology for soils and a significant breakthrough in this area was its
replacement of wet chemistry in many scenarios. The adoption of this approach to evaluate
hydrocarbon contamination in soil is obvious and a few workers have partially
demonstrated this application, as described in the next section. As shown above, VNIR-
SWIR spectroscopy holds great potential for quantitative chemical analysis. If calibration
standards are used, the identification of known substances in a mixture and the
determination of their concentrations are possible. Furthermore, it seems likely that the
identification of substances based on their VNIR-SWIR spectra will become possible for
classification purposes.
4.3 Reflectance spectroscopy of PHCs
The spectral properties of hydrocarbons were identified in the late 1980s, although it has
been argued that these properties were only visible at concentrations of 4% wt (by weight)
and above (Cloutis 1989). In the mid 1990s, a VNIR-SWIR reflectance sensor was developed
as a proof of concept for the detection of OM in soil based on the same spectral properties
identified by Cloutis. The sensor was designed for the detection of benzene in soil at a
minimal concentration of 4.4% wt: several configurations were tested and minimal
information was provided (Schneider et al. 1995). Soon after, the U.S. Department of Energy
contracted a private company to investigate the application of reflectance spectroscopy to
determine motor oil contamination in sandy loam. A schematic design for a field instrument
was suggested, although only one contaminant and one type of soil were tested, using very
few samples with a very limited range of contamination (Stallard et al. 1996). A more
inclusive study was conducted shortly thereafter, using three types of soil contaminated in
the laboratory with diesel and gasoline. A 0.1% wt and 0.5% wt minimum detection limit
was achieved, respectively (Zwanziger & Heidrun 1998). The first study utilizing field-
collected samples was not able to produce robust models, resulting in a very low correlation
coefficient (R
2
= 0.46) and large errors, probably due to very low sample count and problems
with the chemical measurements performed in the laboratory which produced inconsistent
results (Malley et al. 1999). Attempts at mapping hydrocarbons using Landsat and Daedalus
in 1994 and 1995 failed, probably due to the limited spectral resolution of the sensors
(multispectral sensors); on the other hand, the higher spatial and spectral resolution, as well
as the very high signal-to-noise ratio of the airborne hyperspectral sensor used (HyMap)
(Cocks et al. 1998) yielded successful identification of hydrocarbon- and oil-contaminated
soils, but only for high contaminant concentrations (2.5% wt) (Hörig et al. 2001). Based on
the HyMap mission, a Hydrocarbon Index was developed for the mapping of hydrocarbon-
bearing materials. This index is limited to very high signal-to-noise ratio sensors, as well as
by other issues such as problems with land cover, vegetation and high concentration
detection levels (Kühn et al. 2004). The most comprehensive work on reflectance properties
