Quantitative inversion of soil sodium content and pH by hyperspectral remote sensing

Jia-ge Chen; Tao Jiang; Qin-jun Wang; Yue Zhang; Hai-feng Ding; Zhang Huang

doi:10.1117/12.2068371

26 November 2014 Quantitative inversion of soil sodium content and pH by hyperspectral remote sensing

Jia-ge Chen, Tao Jiang, Qin-jun Wang, Yue Zhang, Hai-feng Ding, Zhang Huang

Proceedings Volume 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V; 92632U (2014) https://doi.org/10.1117/12.2068371
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

Taking debris flow area as an example, this paper studied retrieval of soil Sodium content and pH by hyperspectral remote sensing, which provided a new method for estimating soil dispersion. Based on preprocessing, the authors extracted four spectral indices, including reflectance(R), inverse reflectance(1/R), inverse-log reflectance(log(1/R)) and band depth(BD), to establish the prediction model for Sodium content and pH using stepwise multiple regression method. Results indicated that reflectance spectra and inverse-log reflectance were the optimum parameters for inverting soil sodium ions content and pH, respectively. Determination coefficients R² of prediction samples were 0.690 and 0.641 respectively, and R² of test samples were 0.523 and 0.438, which showed that soil spectra with high spectral resolution had the potential for the rapid prediction of Sodium content and pH, thus, providing reliable detection method for soil dispersion using hyper-spectral technology.

Citation Download Citation

Jia-ge Chen, Tao Jiang, Qin-jun Wang, Yue Zhang, Hai-feng Ding, and Zhang Huang "Quantitative inversion of soil sodium content and pH by hyperspectral remote sensing", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92632U (26 November 2014); https://doi.org/10.1117/12.2068371

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KEYWORDS

Sodium

Ions

Reflectivity

Remote sensing

Soil science

Spectral models

Statistical modeling

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