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A NEW PERSPECTIVE TO VISIBLE NEAR INFRARED REFLECTANCE SPECTROSCOPY: A WAVELET APPROACH
Yufeng Ge, Cristine L.S. Morgan, J. Alex Thomasson and Travis WaiserTexas A&M University, College Station, TX
INTRODUCTION
In soil science, visible and near-infrared diffuse reflectance spectroscopy is being used in an effort to develop proximal sensors to quantify soil constituents. Common analysis techniques in soil spectroscopy include principal component analysis, partial least squares regression (PLS), and boosted regression trees. These techniques limited one’s ability to assess wavebands important to prediction models. A new algorithm to incorporate wavelet analysis into VNIR spectroscopy as a preprocessing tool is proposed in this study. The technique uses a discrete wavelet transform (DWT) to analyze soil reflectance at multiple spectral resolutions.
Justification
1. Compare the results of using DWT regression to the results of PLS regression in predicting clay content using VNIR scans of in-situ soil cores.
2. Examine the effectiveness using the results of the two models for physical interpretation of results.
Objectives
CONCLUSIONS
REFERENCES
RESULTS
Ge, Y., C.L.S. Morgan, J.A. Thomasson, and T.H. Waiser. 2006. A new perspective to near infrared reflectance spectroscopy: A wavelet approach. Trans. ASAE. Submitted.
Waiser, T., C.L.S. Morgan, D.J. Brown and C.T. Hallmark. In situ characterization of soil clay content with visible near-infrared diffuse reflectance spectroscopy. SSSAJ. Submitted.
Soil Scanning and Clay Analysis
Spectra were averaged every 10 nm; Waiser et al., 200X The 1st derivative was used for model building; Waiser et al., 200X Partial least squares regression was performed using Unscrambler 9.0 (CAMO Tech, Woodbridge, NJ)
Partial Least Squares (PLS) Analysis
Discrete Wavelet Transform (DWT) Analysis
A dyadic discrete wavelet transform, the “Haar” wavelet, was used for its simplicity; Ge et al., 200X
Spectra were truncated to 2048 data points with the spectral range from 351 – 2398 nm
Each soil spectra was subject to seven levels of dyadic filter band decomposition at scale 3, 4, 5, and 6 (or spectral band ranges of 256, 128, 64, and 32 nm, respectively)
Stepwise multiple linear regression was used with the wavelet variables to develop prediction models for soil clay. The p-value was set at 0.05 for a regressor to be added, and at 0.1 for a regressor to be removed
The DWT and stepwise multiple linear regression was performed with Wavelet Toolbox and Statistics Toolbox, respectively, in MATLAB Release 13
MATERIALS AND METHODS
72 soil cores were collected from 21 soil series in Central Texas. Soil parent material varied from residuum to alluvial materials
Soil cores were cut open and scanned using an ASD “FieldSpec® Pro FR” VNIR spectroradiometer (Analytical Spectral Devices, Boulder, CO), with a spectral range of 350-2500 nm
The pipette method was used to measure particle size distribution of the soil samples
70% of the cores were used for model calibration, 30% of the cores were used for model validation
Calibration samples
Mean: 26% clay
SD: 14% clay
Validation samples
Mean: 27% clay
SD: 15% clay 1 0 0 8 0 6 0 4 0 2 0 0S a n d ( % )
0
2 0
4 0
6 0
8 0
1 0 00
2 0
4 0
6 0
8 0
1 0 0+ calibration○ validation
Partial Least Square Regr.
500 1000 1500 2000 2500
-1000
-500
0
500
1000
wavelength (nm)
regr
essi
on c
oeffi
cien
ts
significant coefficients in blue
iron oxides mica, smectite kaolonite
Wavelet w/ Multiple Regr.
waveband (nm)
wav
eban
d sc
ale
351 23981374862 1886
iron oxides
mica smectitekaolonite
MATERIALS AND METHODS CONT…
pred
icte
d cl
ay, %
RMSD 6 %bias 0.3 %
0 2 0 4 0 6 0
0
2 0
4 0
6 0
measured clay , %
regression line1:1 line
Partial Least Squares
0 2 0 4 0 6 0
0
2 0
4 0
6 0RMSD 7 %bias 0.7 %
measured clay , %
regression line1:1 line
pred
icte
d cl
ay, %
DWT w/ Multiple Regr.
DWT and PLS regression predicted clay content with similar accuracies, the PLS prediction was slightly better
The DWT model creates a simple diagram for visualizing central wavelengths and scales of wavelet regressors, facilitating physical interpretation of the prediction model.
Spectral “maps” of significant regressors in both methods indicate that iron oxides, clay minerals and soil color are used in the clay content prediction models
The wavelet method is more suitable for inexpensive sensor development because it would allow for a simpler sensor design.