Abstract:

To overcome the uncertainty in the spectral estimation of soil organic matter, the hyper-spectral estimation model of soil organic matter content is established using grey system theory. Firstly, after introducing the generalized greyness of grey number, the properties of the generalized greyness are analyzed. Secondly, the modeled samples are ranked in the smallest to the largest in terms of soil organic matter content, the moving variance of the ranked estimators is calculated, the greyness of the lower, value and upper domains of the estimators is calculated based on the moving variance, and the new estimators are constructed based on the greyness. The estimation model of soil organic matter content is built and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient. Finally, the model is applied to estimate soil organic matter content in Zhangqiu District of Jinan, Shandong Province. The results show that the generalized greyness of grey number can effectively represent the interval grey number, reduce the random error and grey uncertainty of the estimation factor, and the accuracy of the proposed estimation model and test accuracy are significantly improved, where the determination coefficient R2 = 0.929 and the mean relative error MRE = 6.830% for the 12 test samples. The results further enrich the grey system theory, and provide a new way to modify the estimation factors and improve the spectral estimation accuracy of soil organic matter.