The estimation of soil calcium carbonate content based on Hyperspectral data

doi:10.6046/gtzyyg.2020095

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Abstract

Carbonate content in soil is an important basis for soil classification and fertility evaluation. Based on an analysis of calcium carbonate content, the authors chose 78 soil samples from Loess Plateau of Shaanxi Province as the research objects. The visible near infrared hyperspectral reflectance (350~2 500 nm) data of soil samples were obtained by hyperspectral imager. Three mathematical transformations, i.e., first-order differentiation, second-order differentiation and continuum removal, were carried out on the original spectral curve, and correlation analysis was used. The method and the continuous projection algorithm were used to select the sensitive band respectively, and the Stochastic Forest regression was used to establish the estimation model of soil calcium carbonate. According to the results obtained, the spectral curve characteristics of Huangmian soil are almost the same, there are obvious absorption characteristics at 1 440 nm, 1 900 nm, 2 200 nm and so on, and the calcium carbonate content and spectral reflectance show a positive correlation trend; the accuracy of random forest estimation model based on the second-order differential and continuous projection algorithm is the highest, the validation set R ² is 0.82, and the PRD value is 2.37.

Keywords calcium carbonate Huangmian soil hyperspectral continuous projection algorithm random forest regression

ZTFLH:

TP79

Corresponding Authors: JIANG Qigang E-mail: 1148545835@qq.com;jiangqigang@jlu.edu.cn

Issue Date: 18 March 2021

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	Qian WU
	Qigang JIANG
	Pengfei SHI
	Lili ZHANG

Cite this article:

Qian WU,Qigang JIANG,Pengfei SHI, et al. The estimation of soil calcium carbonate content based on Hyperspectral data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 138-144.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020095 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/138

Fig.1 Location of the study area and the distribution of sampling sites

Tab.1 Statistics of calcium carbonate in soil samples

Fig.2 Reflectance maps of calcium carbonate content of samples

Fig.3 Sensitive band screening graph based on correlation analysis

Fig.4 Sensitive band selection based on successive projections algorithm

Tab.2 RSR models for soil calcium carbonate content based on sensitive bands

Fig.5 Fitting graph of measured value and predicted value of four mathematical transformations RFR model based on SPA

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