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Spectroscopy-Based Soil Organic Matter Estimation in Brown Forest Soil Areas of the Shandong Peninsula, China 被引量:3
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作者 GAO Lulu ZHU Xicun +3 位作者 HAN Zhaoying WANG Ling ZHAO Gengxing JIANG Yuanmao 《Pedosphere》 SCIE CAS CSCD 2019年第6期810-818,共9页
Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In thi... Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, the Shandong Peninsula, China, were measured with an ASD FieldSpec 3 portable object spectrometer (Analytical Spectral Devices Inc., Boulder, USA). Raw spectral reflectance data were transformed using four methods: nine points weighted moving average (NWMA), NWMA with first derivative (NWMA + FD), NWMA with standard normal variate (NWMA + SNV), and NWMA with min-max standardization (NWMA + MS). These data were analyzed and correlated with SOM content. The evaluation model was established using support vector machine regression (SVM) with sensitive wavelengths. The results showed that NWMA + FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA + FD were 917, 991, 1 007, 1 996, and 2 267 nm. The SVM model established with the above-mentioned five sensitive wavelengths was significant ( R 2 = 0.875, root mean square error (RMSE) = 0.107 g kg −1 for calibration set;R 2 = 0.853, RMSE = 0.097 g kg −1 for validation set). The results indicate that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients. 展开更多
关键词 Brown forest soil Hyperspectral remote sensing Nine points weighted moving average Standard normal variate Sensitive wavelength Spectral reflectance Support vector machine regression
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Mathematical modeling of building structures and nonlinear differential equations
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作者 Victor Orlov Yulia Zheglova 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2020年第3期154-160,共7页
Nonlinear differential equations with moving singular points require emergence and development of new approximate methods of solution.In this paper,we give a solution to one of the problems of the analytical approxima... Nonlinear differential equations with moving singular points require emergence and development of new approximate methods of solution.In this paper,we give a solution to one of the problems of the analytical approximate method for solving nonlinear differential equations with moving singular points,and study the influence of the perturbation of the initial conditions on the analytical approximate solution in the analytic domain.Theoretical material was tested using a numerical experiment confirming its reliability.The theoretical material presented in this paper allows researchers to use nonlinear differential equations with moving singular points when designing mathematical models of building structures. 展开更多
关键词 Mathematical modeling moving singular points a priori estimate approximate solution a posteriori error estimate
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