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Estimation and Mapping of Soil Organic Matter using Visible-Near Infrared Spectroscopy  

Choe, Eun-Young (Soil & Fertilizer Management Division, National Academy of Agricultural Science, RDA)
Hong, Suk-Young (Soil & Fertilizer Management Division, National Academy of Agricultural Science, RDA)
Kim, Yi-Hyun (Soil & Fertilizer Management Division, National Academy of Agricultural Science, RDA)
Zhang, Yong-Seon (Soil & Fertilizer Management Division, National Academy of Agricultural Science, RDA)
Publication Information
Korean Journal of Soil Science and Fertilizer / v.43, no.6, 2010 , pp. 968-974 More about this Journal
Abstract
We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.
Keywords
Estimation; Kriging; Partial Least Squares Regression; Soil organic matters; Spectra;
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