1. A NEW PERSPECTIVE TO VISIBLE NEAR INFRARED REFLECTANCE SPECTROSCOPY: A WAVELET APPROACH Yufeng Ge, Cristine L.S. Morgan, J. Alex Thomasson and Travis Waiser Texas 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 1 st 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 + calibration ○ validation Partial Least Square Regr. wavelength (nm) regression coefficients significant coefficients in blue iron oxides mica, smectite kaolonite Wavelet w/ Multiple Regr. waveband (nm) waveband scale 351239813748621886 iron oxides mica smectite kaolonite MATERIALS AND METHODS CONT… predicted clay, % RMSD 6 % bias 0.3 % measured clay, % regression line 1:1 line Partial Least Squares RMSD 7 % bias 0.7 % measured clay, % regression line 1:1 line predicted clay, % 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.