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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 concern, 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 tile 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 the on·, and provide a new way to modify the estimation factors and improve the spectral estimation accuracy of soil organic matter. [ABSTRACT FROM AUTHOR]

To improve the estimation accuracy of soil organic matter based on hyper-spectral data when using degree of grey incidence, this paper first proposes the concept of the positive and inverse degree of grey incidence considering the limitation of the degree of grey incidence for estimating problems. Then, two new models of positive and inverse degrees of grey incidence are established. Thereafter, the properties of positive and inverse degree of grey incidence are analyzed. Moreover, the estimation model of soil organic matter using positive and inverse degree of grey incidence is established based on hyper-spectral data, and detailed calculation steps are given. Finally, the validity of the model is verified by taking 76 soil samples collected from Zhangqiu District, Jinan City, Shandong Province. The application results show that using the positive and inverse degree of grey incidence, the hyper-spectral estimation model of soil organic matter has high precision. The mean relative error (MRE) of 16 samples to be estimated is 5.312% and the determination coefficient (R2) is 0.930. The research shows that the positive and inverse degree of grey incidence proposed in this paper effectively expands the application of the degree of grey incidence. It is feasible and effective for hyper-spectral estimation of soil organic matter content, and meanwhile, it provides a new way to solve uncertain prediction problems. [ABSTRACT FROM AUTHOR]

Soil organic matter is one of the important indexes of soil fertility, and using hyper-spectral technology to determine soil organic matter content rapidly is of great significance for developing precision agriculture. To further enhance estimation accuracy through the greyness of the soil organic matter content, the grey relational estimation pattern of soil organic matter content by hyper-spectral technology is established based on the grey relational theoty. Based on the variance information between the identified samples and their corresponding patterns, the modified model of the estimated values of soil organic matter content is constructed. The proposed method in this paper is applied to the hyper-spectral estimation of soil organic matter content in Zhangqiu District of Jinan City, Shandong province. The experimental results show that the estimation accuracy of 16 identified samples is high, the average relative error is only 3.801%, and the determination coefficient R² =0.9782. The results show that the modified model of soil organic matter content grey relation estimation pattern based on hyper-spectra! data is valid. [ABSTRACT FROM AUTHOR]

As to the low prediction accuracy in estimating the content of soil organic matter with hyper-spectral technology, at first, based on the uncertainties of hyper-spectral information and the non-time data series, a new grey weighted relation degree model is put forward and the grey relation estimating model is established. Then, by making full use of recognition error, a modified model is given and the grey relation estimation pattern with the residual modification is proposed. At last, the pattern proposed in this paper is applied to the hyper-spectral estimation of soil organic matter that was collected from Tai'an City in Shandong Province. The results indicate that, the testing samples ' average relative error is 5.563%, while the average relative errors of the classic grey relation pattern and linear regression model are 10.128% and 10.567% respectively, the average relative error of forecasting samples is 6.994%. The application example shows that the pattern proposed in this paper is valid. [ABSTRACT FROM AUTHOR]