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It is important to clarify the iron-copper interaction pattern to effectively extract the characteristic bands and improve the inversion accuracy of copper content in soil. In this study, based on experimental samples, spectral feature analysis and analysis of variance (ANOVA) were used to deeply uncover the iron-copper interaction pattern. And used natural samples to build a random forest model to analyze the effect of interaction patterns on inversion accuracy. The results of the study showed that the effect of iron content in soil on spectral reflectance varied with copper content in soil, and similarly, the effect of copper content in soil on spectral reflectance varied with iron content in soil. The effect of iron, copper and their interaction on the spectral reflectance of soil varied with the wavelength. In the wavelength from 400 to 2,500 nm, the effect of iron on the spectral features was more than copper, and in the characteristic wavelength of iron (600–700 nm), even more than 5 times that of copper, the effect of iron on the spectral reflectance played a major role, and the iron content in soil must be considered in the inversion of copper content in soil. The Pearson correlation coefficient method was used as the selected characteristic wavelength, the selected wavelength was used as the independent variable, and the copper content in the soil was the dependent variable. Inversion model was built by the random forest algorithm, and the determination coefficient was 0.73. Under the condition of considering the interaction, the coefficient of determination was 0.87. It was shown that the characteristic wavelength was selected by considering the iron-copper interaction, which can better characterize the response signal of copper in soil. This paper provided a new idea for the accurate inversion of copper content in soil, which can provide technical support for the rapid detection of copper content in soil.

An output of the EdTech Hub, https://edtechhub.org

Rapid, accurate, and non-destructive monitoring after land reclamation is the basis of and key to realizing land restoration evaluation, as well as land use direction decision-making and improvement. However, the biggest problem with the filling reclamation method is the poor soil water retention caused by the insufficient thickness of covering soil (TCS), which results in the loss of soil nutrients. In addition, the cost of filling reclamation has also increased due to the excessive TCS. In this study, multi-source remote sensing data acquisition methods were used to assess the impacts of the TCS of the filling reclamation method on soil water retention and crop growth (CG). We found that as the TCS increases, the overall trend of the CG gradually increases, and the overall trend of the soil water content slowly increases. However, considering the huge economic costs and the cost of soil sources, we suggest that the optimal TCS for the filling reclamation method should be controlled within 40-50 cm. The results of this study provide a reference for the use of multi-source remote sensing technology for rapid evaluation of the restoration effects of reclaimed areas and for the rational utilization of coal chemical wastes.