Agriculture Reference
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al. 1999). Despite the occurrence of otherwise similar minerals in different samples,
variations in spectral features (e.g., shifts in peak wavelength) may occur according to the
nature of highly enriched cations in the mineral. The surface complexation model of the
binding reactions of heavy metals describes the binding of metal ions to the mineral surface
functional group to form a more stable molecular unit (Christl & Kretzschmar 1999; Zachara
& Westall 1999). The main surface functional groups are inorganic hydroxyl groups that
bind to surface Al, Fe, Mn, or Si on oxides or Al and Si on the edges of clay minerals (Sparks
1995; Zachara & Westall 1999; Choe et al. 2008).
3.2 Reflectance spectroscopy of heavy metals
The first report of quantitative prediction of heavy metals in sediments by NIRS was
published in 1997 by Malley and Williams. They showed the feasibility of predicting six of
the seven metals examined (Cd, Cu, Pb, Zn, Ni, Mn, Fe) with NIRS in a highly variable set of
sediment samples, collected at Precambrian Shield Lake in northwestern Ontario, Canada.
The metals were modeled by both MLR and PLS, and OM was found to be responsible for
the prediction of sediment heavy metal concentrations (Malley & Williams 1997). A study
published in 2002 by Kemper and Sommer investigated the ability to predict heavy metals
following a mining accident that caused an area to be flooded with pyritic sludge
contaminated with high concentrations of heavy metals: 214 soil samples were collected,
and prediction of heavy metals was achieved by MLR and ANN approaches. It was possible
to predict six out of nine elements (As, Fe, Hg, Pb, S, Sb) with high accuracy. Correlation
analysis revealed that most of the wavelengths that were important for the prediction could
be attributed to absorption features of Fe and Fe oxides (Kemper & Sommer 2002). The
study by Kemper and Sommer continued with the collection of an additional 132 soil
samples and acquisition of hyperspectral data with the HyMap airborne sensor (Cocks et al.
1998). A spectral mixture modeling approach was applied to the field and airborne
hyperspectral data. VMESMA was used to estimate the quantities and distribution of the
remaining tailing material. The semi-quantitatively determined abundance of residual
pyritic material in the sludge could be transformed into quantitative information for an
assessment of acidification risk and distribution of residual heavy metal contamination
based on an artificial mixture experiment prepared with three different soils and pure
sludge. Unmixing of the HyMap images allowed identification of secondary minerals of
pyrite as indicators of pyrite oxidation and associated acidification (Kemper & Sommer
2003). The first study on NIRS of heavy metal contamination in agricultural soils was
reported by Wu et al. (2005): 120 soil samples were collected in the Nanjing region, and Hg
concentration was modeled by PCR. Correlation analysis revealed that Hg concentration is
negatively correlated with soil reflectance, while positively correlated with the absorption
depths of goethite at 496 nm and clay minerals at 2,210 nm, suggesting Hg sorption by clay-
size mineral assemblages as the mechanism by which to predict spectrally featureless Hg in
soils (Wu et al. 2005). In later work, Wu et al. (2007) also explored a physicochemical
mechanism that allows estimation of heavy metals with the reflectance spectroscopy
method, and concluded that correlation with total Fe (including active and residual Fe) is
the major mechanism (Wu et al. 2007). Choe et al. (2008) explored the possibility of
extending the use of spectral variations associated with heavy metal parameters to map the
distribution of areas affected by heavy metals on HyMAP data in the Rodalquilar gold-
mining area in southeast Spain. The chosen spectral parameters showed significant
correlations with concentrations of Pb, Zn and As. Later, Choe et al. (2009) examined the
