idmore, 
igeningen, The 
concentration of 
), willow (Salix 
sation technique 
ive power of the 
tion R? between 
or assessing the 
fter returning to 
n additional 24 
ns sampled, an 
Netherlands. 10 
aris L.) and 10 
pling technique 
measurements, 
sported to the 
t 70 degrees. 
) degrees until 
ructed with a 
ozamsky et al., 
neasured with a 
were measured 
mic Absorption 
ed tannin were 
germann(1988). 
g the improved 
lyphenols were 
assay for total 
lyphenol levels 
in solution of 
nd recalculated 
Concentrations 
ral reflectances, 
averaged. This 
cach vegetation 
uum removal 
nuum-removed 
'entres differed 
e to the use of 
each containing 
set served as a 
Using stepwise 
the continuum 
IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring", Hyderabad, India,2002 
  
removed reflectance data to predict the concentration of the 
various chemicals. The number of wavebands used in each 
regression model was limited to a maximum of 3 to avoid over 
fitting of the model (Table 2). The regression model was used 
to predict the chemical composition of the samples in the test 
dataset. To test the effectiveness of the regression model the 
correlation between measured and predicted concentrations 
were calculated for each chemical (Table 2). 
  
  
  
  
  
Training dataset (N=24) Test dataset (N=24) 
Mean Min Max St.Dev Mean Min Max St.Dev 
Component (%) (%) (%) (%) (%) (%) (%) (%) 
Nitrogen 1.43 0.88 2.59 0.44 1.45 0.69 2.53 0.46 
Phosphorous 0.100 0.044 0.198 0.040 0.104 0.049 0.197 0.046 
Sodium 0.014 0.007 0.021 0.004 0.013 0.008 0.022 0.004 
Potassium 0.606 0.37 0.98 0.14 0.620 0.38 0.98 0.17 
Calcium 1.37 0.36 2.30 0.72 1:35 0.37 2.80 0.68 
Magnesium 0.217 0.145 0.310 0.053 0.210 0.124 0.341 0.060 
C. Tannin* 15.85 0.04 54.91 21.63 16.23 0.00 49.02 19.72 
Polyphenol* 8.14 2,33 13:32 3.30 9.10 3:36 17.42 3.73 
  
  
Table 1. Overview of the concentration range of N, P, Na, K, Ca, Mg, Tannin and Polyphenol for the training and the test dataset. 
Tannin and Polyphenols are in 96 quebracho tannin equivalents. 
3. RESULTS 
The concentrations predicted by the regression models for the 
different components showed a strong correlation with the 
measured chemical composition. The best prediction ocurred 
for nitrogen (R? = 0.72), sodium (R? 2 0.79), calcium (R2 = 
0.81) magnesium (R? 2 0.65) and tannin (R? = 0.70). Less 
succesfull was prediction of phosphorous (R* = 0.40), 
potassium (R? = 0.42) and polyphenol (R* = 0.41). Correlations 
between observed and predicted concentrations were significant 
at the 0.01 level, except for the predicted concentration of 
phosphorous (Table 2). 
  
  
Component Waveband 1 Waveband 2 Waveband 3 Pred. R° 
N 741 1665 479 0.72 
P 1022 957 1762 0.40 
Na 1127° 1208 1733 0.79° 
K 2053 821 968 0.42" 
Ca 698 964 510 0.81” 
Mg 482 1172 741 0.65" 
Tannin 694 1013 673 0.70 
Polyphenol 812 804 823 0.41° 
  
  
Table 2. Wavebands used for prediction of chemical composition of a test dataset, based on stepwise regression of a training dataset. 
(*-p «0.05; **: p « 0.001)