263 
illumination conditions. Differential spectra will help to limit 
the effects of low frequency noises on objective spectra. In the 
study, not only the reflectance differentiate was calculated, but 
also the first order and second order differentiate of four 
transforms of reflectance (reciprocal, logarithm, logarithmic 
reciprocal and square root) were calculated. And statistical 
analytical technique was used to evaluate and compare their 
sensitivity as indicators of SOM.Figured Removal of Water 
Absorption Sects 
2.4.1 Derivative spectroscopy technique 
Among the developed methods of spectrometry, derivative 
spectroscopy technique has a promising application in remote 
sensing data processing. Differential (difference) values with 
different orders can help people to quickly determine the 
wavelength location of spectral curve inflection point and 
extremum reflectance. Clouds’s study showed that the 
sensitivity to noise of spectra data decreased by low-order 
differential processing. Therefore it is comparatively effective 
in practical application [13]. Spectral difference is practically 
used as a limited approximation of differentiate. The calculative 
formula is as follows: 
«'U,.) = [«U)-Æ(/t w )]/2A/L ( 2) 
(3) 
x. 
In above formula, 1 represent wavelength of each band; 
r'U.) A r”(;l) 
v 1 ' and v 1 7 represent first order and second order 
X a 3 
differential spectra for wavelength ' , respectively; is 
X_, X- 
interval between wavelength ' 1 and ' . With the increase 
of A/l ^ ^e: spectral differential curve inclined to becoming 
smoother, leading possibly to elimination of many subtle 
spectral characteristics (as shown in figure 2). In this study, 
=10 nm is selected« 
In the above formula, 1 is single correlation coefficient 
between soil organic matter content OM an( j S p ec t r al 
reflectance or its transforms (all denote as R ), 1 is the serial 
TO 
number of waveband, m is the spectral reflectance (or its 
transforms) value at the ith waveband of the nth soil sample, 
1 is the mean value of spectral reflectance (or its transforms) 
i s the SOM 
of the N so ii samples at waveband *, 
content of the nth soil sample, 
mean for SOM content of ^ 
total number of soil samples. 
OM i s t he actual measured 
mean for SOM content of ^ soil samples, ^ equal to 174, 
Figure.2 Order 2 Derivative of Albedo with Different Band 
Interval 
2.4.2 Correlation analysis 
The SOM contents of 174 soil samples measured by volumetry 
assay method and soil reflectance as well as its 14 types of 
2.4.3 Stepwise regression 
According to single correlation analytical results, several 
optimal wavebands with comparatively high correlation 
coefficients in each transforms were selected for stepwise 
regression analysis, and then used to compose predictive 
equation. The total 174 samples were randomly divided into two 
groups, one was used for establishing regression predictive 
model (called modeling sample collection, total is 134, 
possessing 77% of total number), another group was used for 
testing established regression model (called testing sample 
collection, total is 40, possessing 23% of total number). 
Stepwise regression analysis is a typical mathematic method 
used for selecting regression variable in multiple linear 
regression models. Its basic idea is described as follows: 
regression variables are selected one by one, and the selective 
qualification is their sum of partial regression square is 
remarkable; the selected variables are performed significance 
test one by one after selection of each new variable, and the 
non-significant variables are removed. Repeat the process of 
selection, test and elimination until there is no variable can be 
selected or eliminated. When using stepwise regression analysis 
to determine waveband combwûifcm related to organic matter, 
the input variables are organicH(fcter content measured and the 
value of spectral reflectance or its transforms at the optimal 
wavebands with comparatively high correlation coefficients in 
single correlation analysis. The output result are a series of 
multiple linear equations containing different wave bands and 
corresponding validation coefficient R (formula 5), and the 
SOM content is calculated by multivariate regression model 
finally. The validation coefficient R is also called multiple 
correlation coefficient or fitting degree of curve, which is a 
good measurement for regression effectiveness. When 
R 2 
regression effectiveness is rather bad, 
equal to 0 
approximately, which manifests the fitting value 
Y: 
irrelevant to the observed value 
at all.