The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
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under the heating conditions, excessive potassium dichromate
and sulphuric acid standard solution was used to oxidize soil
organic matter, and then ferrous salts (ferrous sulfate or ferrous
ammonium sulfate) standard solution was used to titrate in the
presence of appropriate redox indicator. The organic carbons
content can be calculated from the amount of potassium
dichromate consumed by organic matter oxidization,
consequently the soil organic matter content can be worked out.
2.3 Pretreatment of spectral data
2.3.1 Smoothing spectral curves
Because of the different response to energy among spectrometer
bands, the spectral curve always has some noises. In order to
obtain a smooth change, it is necessary to smooth waveform to
remove a small amount of noise included in signals. The
practice has showed that, if noises have high frequency and low
magnitude, the smooth methods could reduce noises to some
extent. The smooth methods in common use include Moving
Average, Static Average, and Fourier Series Approximation and
so on. In this study, the 9-Point Weight Moving Average was
used to smooth spectral curves and eliminate noises. The
spectral curves give sequences of N survey points
({Rj, i 1 ? 2,3 , N} ^ S p ectra j ¿ ata 0 p Ago FieldSpec FR
Spectrometer, the spectral resolution between 350 nm-1000 nm
is 3 nm, the spectral resolution between 1000 nm-2500 nm is 10
nm , and the spectrometer re-samples the data as 1 nm). Here,
the value of point 1 is weighted average of its anterior 4 points
R
and posterior 4 points. That is, the new value of point 1 , ' ,
is replaced by weighted average of 9 points including point 1 ,
which is called smooth value.
/Ç =0.04^ +0.08rç_3 +0.1 ^._ 2 +0.1 +0.20^
+0.1 <^,+0.12^+0.08^3+0.04^
2.3.2 Removing atmospheric water absorption bands
In order to make sure that the ground findings can be finally
applied in the OMIS or Hyperion imaging spectrometer data,
the disposal and analysis of spectral curve in this paper directly
aim at field soil spectral data instead of indoor laboratory data.
Three sects of wavebands with serious water absorption peaks
were removed through concrete data analysis and reference of
conclusions from relative literatures [11] [12]. The removed
three sects of wavebands /including: (1)1350-1416 nm ;
(2)1796-1970 nm ; (3)2470-2500 nm. The eliminated
water-absorption peaks wavebands and spectral curves after
elimination are shown in figure 1, the spectral curves after
elimination are divided into three sections:
R&owi wtter absorb wets
t \ \
2.4 Analytical methods
In addition to direct analysis of soil reflectance, we also perform
14 transforms of soil reflectance to find spectral indicators
sensitive to soil organic matter (SOM) content. The purpose of
the analysis was to relate SOM content to spectral properties.
Fourteen types of transformation were applied to the soil
reflectance R (Table 1).
Description
Formula
Reciprocal of R
Ì/R
Reciprocal of lg R
l/lg R
First derivative of R
R'
First derivative of lg R
(lg*)'
First derivative of ^~R
4r
Second derivative of
(JR)'
Second derivative of
i/ig*
(l/lg*)
Logarithm of R
lg*
Square root of R
Jr
First derivative of 1/R
(JR)
First derivative of 1/ lg R
(i/ig *)'
Second derivative of R
R"
Second derivative of lg R
(lg*)"
Second derivative of ^[R
v/
Table 1 Fourteen transformation types of reflectance
Transforming reflectance is in consideration of two respects. On
one hand, it is a need for removing the noise, for instance, first
derivative of R reduces the impacts of linear or linear-like
background noise on target spectra; Log(R) weakens
multiplication noise caused by the change of illumination
condition. On the other hand, the relationship between
reflectance (independent variable) and SOM content (dependent
variable) was not linear correlation. Reflectance transformation
actually linearizes the correlation between reflectance and soil
physical-chemical properties.
After logarithmic transformation, the spectral data not only tend
to enhance spectral differences of visible light (the original
value of visible spectra is low as a whole), but also intend to
reduce multiplicative factor effects induced by changes in
