Grey Information Relational Estimation Model of Soil Organic Matter
Content Based on Hyperspectral data
2024, 36(4):
56-68.
摘要
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In order to overcome the uncertainty in hyperspectral estimation of soil organic matter content, this paper aim to establish a grey
information relational estimation model of soil organic matter content based on hyperspectral data and grey information theory. Based
on 76 samples in Zhangqiu District of Jinan City, Shandong province of China, the spectral data are first transformed by the nine
methods such as square root, first order differentiation of the logarithm reciprocal, and so on, the correlation coefficient is calculated,
and the estimation factors are selected by using the principle of great maximum correlation. Then, according to the principle of
increasing information and taking maximum method, the spectral estimation factors of each sample are sorted from small to large, and
the grey information sequence is formed, and the grey relational estimation model of soil organic matter content is constructed based
on the information chain. Finally, the estimation results based on different information chains are fused twice, and compared with the
commonly used estimation methods. The results of the method in this paper show that the average relative error of the 12 test samples
is 5.576%, and the determination coefficient R2 is 0.934, and the estimation accuracy is higher than that of commonly used methods
such as multiple linear regression, BP neural network and support vector machine. The results show that the grey information
relational estimation model using hyperspectral data proposed in this paper is feasible and effective, and it provides a new way for
hyperspectral estimation of soil organic matter and other soil properties.
