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Remote Sens. 2018, 10(6), 867; https://doi.org/10.3390/rs10060867

The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of Soil Properties with Visible and Near-Infrared Spectral Data

1
College of Water Sciences, Beijing Normal University, Beijing 100875, China
2
Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China
3
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
*
Authors to whom correspondence should be addressed.
Received: 4 May 2018 / Revised: 29 May 2018 / Accepted: 30 May 2018 / Published: 2 June 2018
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Abstract

This study evaluated whether wavelet functions (Bior1.3, Bior2.4, Db4, Db8, Haar, Sym4, and Sym8) and decomposition levels (Levels 3–8) can estimate soil properties. The analysis is based on the discrete wavelet transform with partial least-squares (DWT–PLS) method, incorporated into a visible and near-infrared reflectance analysis. The improved DWT–PLS method (called DWT–Stepwise-PLS) enhances the accuracy of the quantitative analysis model with DWT–PLS. The cation exchange capacity (CEC) was best estimated by the DWT–PLS model using the Haar wavelet function. This model yielded the highest coefficient of determination (Rv2 = 0.787, p < 0.001), with the highest relative percentage deviation (RPD = 2.047) and lowest root mean square error (RMSE = 4.16) for the validation data set of the CEC. The RPD of the SOM predictions by DWT–PLS using the Bior1.3 wavelet function was maximized at 1.441 (Rv2 = 0.642, RMSE = 5.96), highlighting the poor overall predictive ability of soil organic matter (SOM) by DWT–PLS. Furthermore, the best performing decomposition levels of the wavelet function were distributed in the fifth, sixth, and seventh levels. For various wavelet functions and decomposition levels, the DWT–Stepwise-PLS method more accurately predicted the quantified soil properties than the DWT–PLS model. DWT–Stepwise-PLS using the Haar wavelet function remained the best choice for quantifying the CEC (Rv2 = 0.92, p < 0.001, RMSE = 4.91, and RPD = 3.57), but the SOM was better predicted by DWT–Stepwise-PLS using the Bior2.4 wavelet function (Rv2 = 0.8, RMSE = 5.34, and RPD = 2.24) instead of the Bior1.3 wavelet function. However, the performance of the DWT–Stepwise-PLS method tended to degrade at high and low decomposition levels of the DWT. These degradations were attributed to a lack of sufficient information and noise, respectively. View Full-Text
Keywords: Vis-NIR hyper-spectral; DWT; wavelet function; decomposition levels; PLS; Stepwise-PLS Vis-NIR hyper-spectral; DWT; wavelet function; decomposition levels; PLS; Stepwise-PLS
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Wang, G.; Wang, W.; Fang, Q.; Jiang, H.; Xin, Q.; Xue, B. The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of Soil Properties with Visible and Near-Infrared Spectral Data. Remote Sens. 2018, 10, 867.

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