The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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nm, ~1900 nm and -2200 nm were used for predicting 
engineering parameters. 
PLS1 analysis (predicting a single engineering parameter at a 
time) method implemented in The Unscrambler software was 
used for the multivariate calibration and validation. Outlier 
detection was performed through analyzing residuals, leverages, 
normal probability plots of residuals both in the X and Y spaces, 
stability plots etc. Sufficient number of PLS factors were 
determined based on significance tests and through examining 
RMSE of residual variances of each PLS factor. Regression 
coefficients that significantly contribute to the models were 
selected based on uncertainty limit tests. Full cross validation 
method was used for calibrating and validating models. 
Measured LL 
Correlation coefficient RMSEP SEP Bias offset 
086 056 Ô57 0.003 L89 
Relevant predictors (absorption feature parameters) 
position, depth, width & area -1900 nm; depth , width & area -1400 
nm; position , depth & width ~2200nm 
Correlation coefficient RMSEP SEP Bias offset 
0.85 0J5 015 0.000 011 
Relevant predictors (absorption feature parameters) 
position, depth, width & area -1900 nm; depth & area -1400 nm; 
position & width ~2200nm 
Figure 6. Results of PLSR modeling for liquid limit and cation 
exchange capacity showing the regression overview (measured 
versus predicted values) in the calibration and validation stages. 
As indicated by the correlation coefficients (Figure 5) a large 
portion of the variation in the engineering parameters could be 
accounted for by the spectral parameters. Estimation errors, 
both RMSEP and SEP are small indicates that expected error 
will be within acceptable limits if prediction is done on new 
samples using these models. Bias, which is average value of the 
difference between predicted and measured values, is also small 
for the given number of PLS factors indicating that effect of 
bias in the modelling is negligible (Martens and Naes, 1989). 
offse 
Correlation coefficient 
RMSEP 
SEP 
Bias 
t 
PL 
0.68 
0.60 
0.60 
0.000 
1.65 
PI 
0.83 
0.47 
0.48 
0.000 
0.65 
FS 
0.64 
0.22 
0.22 
0.001 
0.97 
Relevant predictors (absorption feature parameters) 
PL Position, depth & area -1900 nm; depth & area -1400 nm; 
position, depth & width - 2200 nm. 
PI Position, depth & area -1900 nm; depth & area -1400 nm; 
position, depth, width & area - 2200 nm. 
FS Position & depth -1900 nm; depth -1400 nm; position & width - 
2200 nm. 
Table 1. Summary of model results for engineering parameters 
(PL, PI and FS) 
Position and depth -1900 nm; depth - 1400 nm; position and 
width - 2200 nm are found to be significant predictors for all 
the five engineering parameters: LL, PL, PI, CEC and FS 
(Figure 6 and Table 1). Water bearing clay mineral groups, 
smectites produce absorption features due to the OH stretching 
at -1400 nm; and due to combination of H-O-H bending and 
OH-stretching near -1900 nm. Combination of fundamental OH 
stretching and bending with Al, Mg, or Fe ions are known to 
produce absorption features at -2200 - 2300 nm , and these are 
diagnostic of clay minerals (Clark, 1999). According to Clark, 
(1999) absorption features - 1400 nm is due to OH and H 2 0 
which can be caused by kaolinite/ smectite/ illite clay minerals; 
absorption features - 1900 nm is associated with H 2 0 which 
can be caused by smectite/ illite clay mineral species; and 
absorption features - 2200 nm is related with Al-OH, Mg-OH, 
or Fe-OH bending which can be caused by kaolinite/ smectite/ 
illite clay minerals. 
4. CONCLUSIONS 
In this paper, relationships between expansive soil engineering 
parameters and absorption feature parameters calculated from 
their respective reflectance spectra were examined. We were 
able to quantify engineering parameters from soil reflectance 
spectra employing a multivariate regression analysis, PLSR. 
The results confirm spectroscopy’s potential in assessing 
engineering characteristics of expansive soils both in 
identification of expansive soils and subsequent quantification 
of their engineering parameters (Figure 3 and 5). Apart from 
supplying a great deal of information within a short period of 
time and of being cheaper, accuracy of spectroscopic estimates 
seem also reliable. 
Laboratory spectroscopy can be utilized for measuring soil 
samples from any depth. It require small volume of samples for