Hyperspectral Estimation of Soil Organic Carbon Content Based on Continuous Wavelet Transform and Successive Projection Algorithm in Arid Area of Xinjiang, China

Xiaoyu Huang, Xuemei Wang (), Kawuqiati Baishan and Baisong An
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Xiaoyu Huang: College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
Xuemei Wang: College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
Kawuqiati Baishan: College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
Baisong An: College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China

Sustainability, 2023, vol. 15, issue 3, 1-16

Abstract: Soil organic carbon (SOC), an important indicator to evaluate soil fertility, is essential in agricultural production. The traditional methods of measuring SOC are time-consuming and expensive, and it is difficult for these methods to achieve large area measurements in a short time. Hyperspectral technology has obvious advantages in soil information analysis because of its high efficiency, convenience and non-polluting characteristics, which provides a new way to achieve large-scale and rapid SOC monitoring. The traditional mathematical transformation of spectral data in previous studies does not sufficiently reveal the correlation between the spectral data and SOC. To improve this issue, we combine the traditional method with the continuous wavelet transform (CWT) for spectral data processing. In addition, the feature bands are screened with the successive projection algorithm (SPA), and four machine learning algorithms are used to construct the SOC content estimation model. After the spectral data is processed by CWT, the sensitivity of the spectrum to the SOC content and the correlation between the spectrum and the SOC content can be significantly improved ( p < 0.001). SPA was used to compress the spectral data at multiple decomposition scales, greatly reducing the number of bands containing covariance and enabling faster screening of the characteristic bands. The support vector machine regression (SVMR) model of CWT-R′ gave the best prediction, with the coefficients of determination ( R 2 ) and the root mean square error ( RMSE ) being 0.684 and 1.059 g∙kg −1 , respectively, and relative analysis error ( RPD ) value of 1.797 for its validation set. The combination of CWT and SPA can uncover weak signals in the spectral data and remove redundant bands with covariance in the spectral data, thus realizing the screening of characteristic bands and the fast and stable estimation of the SOC content.

Keywords: soil organic carbon content; visible-near infrared spectroscopy; continuous wavelet transform; successive projections algorithm; machine learning algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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