Soil Science

Skip Navigation LinksHome > November 2012 - Volume 177 - Issue 11 > Using Wavelet Transform of Hyperspectral Reflectance Data fo...
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Soil Science:
doi: 10.1097/SS.0b013e3182792bcc
Technical Article

Using Wavelet Transform of Hyperspectral Reflectance Data for Extracting Spectral Features of Soil Organic Carbon and Nitrogen

Yang, Hongfei1; Qian, Yurong1,2; Yang, Feng1,3; Li, Jianlong1; Ju, Weimin4

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Abstract: Visible and near-infrared spectroscopy can be a powerful tool to rapidly quantify various soil characteristics. Recently, wavelet analysis has been proven to be efficient in many fields, including signal processing and digital image analysis. It can be used to smooth signals and to reduce large data sets to parsimonious representations. The objective of this study was to capture the absorption features of soil organic carbon (SOC) and total nitrogen (TN) by applying wavelet analysis to reflectance spectra. In addition, the correlation between SOC, TN contents, and soil spectra reflectance and wavelet coefficient were analyzed and compared. The results show that the maximum correlation coefficient between SOC, TN, and wavelet coefficient was more than 0.96 compared with the relationship between SOC, TN, and spectral reflectance (r = −0.79 for SOC, r = −0.40 for TN), especially for TN (the maximum negative correlation coefficient, r = −0.964). For SOC + TN and SOC/TN, because SOC contents accounted for a larger proportion of soil composition than TN, their spectral features were mainly affected by SOC in soil samples. In addition, wavelet analysis also enhanced the features of SOC + TN and SOC/TN obviously. These results suggest that wavelet analysis is a better method for capturing the absorption features of soil composition from hyperspectral data. Continuous wavelet transform was confirmed to be an essential and efficient tool used to locate the wavenumber where SOC and TN could be predicted with minimal interference from other components. The results also implied that continuous wavelet transform can be used in discriminating soil composition.

© 2012 Lippincott Williams & Wilkins, Inc.




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