The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
265 
among input variables, and input variables are not only related 
to outputs, but also relevant to one another. Under this condition, 
an input variable of model possibly shields the effects of the 
other variables on results. In short, stepwise regression, as a 
fixed processing, has risks for users to a certain extent, because 
regressive results are closely related to the initial model as well 
as the selective strategy for variables. 
Considering the shortcomings of stepwise regression analysis, 
this study firstly used single-correlation analysis, then selected 
the wavebands with high correlation and long intervals (weakly 
relevant to one another) as regressive input variables. Thus, on 
one hand, under the premise of remaining sensitive wavebands, 
the amount of input wavebands was reduced and overfitting 
phenomenon was avoided; on the other hand, the selection of 
input wavebands were not entirely based on the magnitude of 
correlation coefficients but selecting 1 extremum (or 2 
extremum for differential transforms with positive and negative 
into three sections after elimination of water-absorption peaks. 
The longer the intervals among selected wavebands are, the 
weaker the correlation among them. Accordingly, high 
correlation among selected variables (wavebands) was 
effectively preventing by this way. 
Table 2 shows the regression equations used for prediction of 
SOM from various transforms of reflectance. And all equations 
were performed F-value test with significance level 0.001. As 
shown in table 2, among all transforms, the first order 
differentiate of logarithm of reflectance ((lg/?)') has the 
strongest ability to predict SOM content, and the validation 
coefficient of its regression equation consisting of three 
wavebands is 0.89, the maximum among all regression 
equations. The effect of SOM content predictions from different 
regression equations are as shown in figure 4, 5, 6, 7. 
Transforms 
Regression Equations 
R 2 
Adjusted 
R 2 
RMSE 
X = R 
f = 1.990-20.949^2137 +22.318^50! 
0.684 
0.679 
0.608 
X = y[R 
Y = 2.386 - 24.938^137 + 24.689* 149 9 
0.802 
0.799 
0.480 
X = l/R 
Y = 0.053 + 0.307^2277 
0.789 
0.787 
0.486 
X-lgR 
y = 1.029 -12.359*2149 + 10.878*1504 
0.851 
0.849 
0.417 
x = (\/r) 
y = 0.581-362.003* 863 +64.680*n45 
0.840 
0.837 
0.432 
* 
II 
\ 
Y = 0.626 + 1308.365*2222 + 2027.007*i 740 -135.885* 672 
0.706 
0.699 
0.586 
x = {igR) 
7 = 1.772 + 1004.071*2187 +2893.272*849 -1682.915*168, 
0.888 
0.885 
0.360 
x={ig r)' 
7 = 2.451 + 21952.91*587 “ 47995.4* 905 
-4577.994*2219 + 13138.89*1726 
0.839 
0.833 
0.431 
X = Jr 
7 = 1.971 + 1399.130*2180 + 4260.033* 846 - 2459.097* 16 85 
0.861 
0.858 
0.403 
* = Væ 
7 = 2.661 + 38552.87*587 - 40731.4* 905 
+ 33733.12*1725 -6072.362*2199 
0.842 
0.837 
0.432 
X = R' 
7 = 1.891 + 5024.556*845 - 941.121* 203 7 
+ 576.462*2180 + 615.901*1521 
0.789 
0.782 
0.500 
X = R 
7 = 2.305 + 13830.69*587 + 52867.66*i 725 - 35305.5* 529 
0.754 
0.748 
0.537 
Table 2 Regression analytical result between different reflectance transforms and SOM Content 
A