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Proceedings Paper

Effect of particle size on prediction of soil TN with remote sensing based on NIR spectroscopy
Author(s): Xiaofei An; Minzan Li; Lihua Zheng; Yumeng Liu
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Paper Abstract

It is a feasible method to detect soil total nitrogen (TN) content with remote sensing based on NIR spectroscopy. However, the accuracy of soil TN model was affected seriously by soil particle size. The spectral scanning results showed that at the same soil TN content level, with the decrease of the soil particle size, the reflectance of soil samples was reduced and the trend was not linear relationship. At the short wavelength (760-1100 nm) wave bands, there were a little of differences; while at the long wavelength (1100-2500 nm) wave bands, there were great differences. Two methods were adopted to eliminate the effect of soil particle size. The first method was to establish TN model by the first order differential preconditioning method of the spectral data. The second method was to establish TN model with mixed calibration set of different particle size soil samples after data preprocessing. Through the combination of the two methods, The RC, RV, RMSEC, RMSEP and RPD of the model improved from 0.85, 0.31, 0.046, 0.132, 0.866 to 0.92, 0.86, 0.018, 0.091, 2.700 respectively. The results showed that the effect of soil particle size on prediction of soil TN can be eliminated effectively.

Paper Details

Date Published: 9 November 2012
PDF: 7 pages
Proc. SPIE 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, 852710 (9 November 2012); doi: 10.1117/12.977349
Show Author Affiliations
Xiaofei An, China Agricultural Univ. (China)
Minzan Li, China Agricultural Univ. (China)
Lihua Zheng, China Agricultural Univ. (China)
Yumeng Liu, China Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 8527:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki; Jian-yu Wang, Editor(s)

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