Agriculture Reference
In-Depth Information
considered spectral absorption feature parameters
had the potential to detect heavy metals.
Moreover, the image-derived spectral parameters themselves
showed a capacity to screen
areas affected by heavy metals as a
preliminary observation in determining sampling
strategies and
precise analyses for the investigation of environmental pollution (Choe et al.
2008). While weak prediction performance might result from the occurrence of many
overlapping bands and the limited number of molecules that respond spectrally in the
VNIR-SWIR range, a spatial distribution map of heavy metals by EMLR shows a
distribution pattern similar to that on a map of measured values (Choe et al. 2009).
However, the partial trade-off for time saved is reduced accuracy. Thus, spectral assessment
of soil samples cannot replace, but rather complements the classical chemical analysis. The
benefits are the practicable processing of a large number of samples and the savings on
chemicals, lengthy and tedious processes and manpower. However, a calibration using
information obtained from chemical analyses is mandatory (Vohland et al. 2009). In terms of
spatial analysis, an airborne or space borne hyperspectral sensor may be useful for the
screening of large areas and reproduction of the spatial distribution patterns.
3.4 Summary table
The results of the previously mentioned NIRS prediction models of heavy metals are
summarized in Table 1. Different modeling techniques as well as various preprocessing
methods were employed in the development of these models, for an in depth description of
the models please refers to the proper references.
# of
Samples
Modeling
Method
Authors
Contaminant (R
2
)
Malley &
Williams (1997)
Cd(0.63), Cu(0.91), Fe(0.86), Mn(0.93),
Ni(0.88), Pb(0.81), Zn(0.93)
169
MLR / PLS
As(0.84), Cd(0.51), Cu(0.43), Fe(0.72),
Hg(0.96), Pb(0.95), S(0.87), Sb(0.93),
Zn(0.24)
Kemper &
Sommer (2002)
214
MLR / ANN
Kemper &
Sommer (2003)
As(0.73), Pb(0.73)
346
polynomial fit
Siebielec et al.
(2004)
Cd(0.54), Cu(0.61), Fe(0.87), Ni(0.84),
Pb(0.45), Zn(0.67)
70
PLS
Wu et al. (2005)
Hg(0.48)
120
PCR
As(0.72), Cd(0.20), Co(0.80), Cr(0.85),
Cu(0.67), Ni(0.81), Pb(0.55), Zn(0.56)
Wu et al. (2007)
61
PLS
Choe et al. (2008)
As(0.88), Pb(0.61), Zn(0.60)
49
EMLR
Choe et al. (2009)
As(0.60), Cu(0.81)
22
EMLR
Ren et al. (2009)
As(0.62), Cu(0.41), Fe(0.78)
33
PLS
Vohland et al.
(2009)
Cu(0.75), Fe(0.84), Mn(0.71), Pb(0.76),
Zn(0.81)
149
PLS
As(0.30), Cd(0.10), Cr(0.68), Cu(0.46),
Hg(0.15), Pb(0.26), Zn(0.40)
Linear
Regression
Jia et al. (2010)
122
Pandit et al.
(2010)
Cd(0.43), Cu(0.81), Mn(0.81), Pb(0.75),
Zn(0.76)
8
PLS
Table 1. Heavy metals prediction via NIRS.
