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Chinese Agricultural Science Bulletin ›› 2015, Vol. 31 ›› Issue (2): 209-214.doi: 10.11924/j.issn.1000-6850.2014-0934

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Hyperspectral Characteristic and Estimation Modeling of Fluvo-aquic Soil Alkali Hydrolysable Nitrogen Content Based on Genetic Algorithm in Combination with Partial Least Squares

Chen Hongyan1,2, Zhao Gengxing1, Zhang Xiaohui2, Chen Jingchun3, Zhou Congliang1   

  1. (1National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Research Center of Agricultural Big Data, College of Resources and Environment, Shandong Agricultural University, Taian Shandong 271018;2College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian Shandong 271018;3Hydrological Bureau of Juye County, Juye Shandong 274900)Abstract: Extracting the characteristic spectra is the key of estimating soil alkali hydrolysable nitrogen content based on hyperspectra. This article was carried out to detect and transform the hyperspectra reflectance of the soil samples collected from the fluvo-aquic soil areas in Shandong Province. The characteristic wave bands of fluvo-aquic soil alkali hydrolysable nitrogen were filtered using genetic algorithms and partial least squares method. Then, the partial least squares regression estimation models of fluvo-aquic soil alkali hydrolysable nitrogen content were constructed. The best model was selected and compared with the models based on correlation analysis, stepwise linear and partial least squares regression directly. The results showed that the characteristic wave bands of fluvo-aquic soil alkali hydrolysable nitrogen were 449-469 nm, 988-1001 nm, 1065-1078 nm, 1716-1736 nm, 1912-1925 nm, 2213-2233 nm and 2262-2275 nm, the modeling coefficient of determination(R2) based on characteristic wave bands of different input spectra was generally high, the model based on the characteristic wave bands of the first derivative of reflectance had the highest precise, with the 147 data points as 7.17% of the original spectra, the model building R2 to 0.97, RMSE as 4.78 mg/kg, the validated R2 to 0.95, RMSE as 5.49 mg/kg, the model had good prediction accuracy of fluvo-aquic soil alkali hydrolysable nitrogen content. The comparison of methods showed that the genetic algorithms in combination with partial least squares regression could not only obtain higher prediction accuracy, but also simplify models. Therefore, the genetic algorithms in combination with partial least squares regression method may effectively select the characteristic wave bands of soil alkali hydrolysable nitrogen, reduce the model variables and improve the estimation accuracy.
  • Received:2014-04-02 Revised:2015-01-15 Accepted:2014-08-06 Online:2015-03-19 Published:2015-03-19

Abstract: Extracting the characteristic spectra is the key of estimating soil alkali hydrolysable nitrogen content based on hyperspectra. This article was carried out to detect and transform the hyperspectra reflectance of the soil samples collected from the fluvo-aquic soil areas in Shandong Province. The characteristic wave bands of fluvo-aquic soil alkali hydrolysable nitrogen were filtered using genetic algorithms and partial least squares method. Then, the partial least squares regression estimation models of fluvo-aquic soil alkali hydrolysable nitrogen content were constructed. The best model was selected and compared with the models based on correlation analysis, stepwise linear and partial least squares regression directly. The results showed that the characteristic wave bands of fluvo-aquic soil alkali hydrolysable nitrogen were 449-469 nm, 988-1001 nm, 1065-1078 nm,1716-1736 nm, 1912-1925 nm, 2213-2233 nm and 2262-2275 nm, the modeling coefficient of determination(R2) based on characteristic wave bands of different input spectra was generally high, the model based on the characteristic wave bands of the first derivative of reflectance had the highest precise, with the 147 data points as 7.17% of the original spectrum, the model building R2 to 0.97, RMSE as 4.78 mg/kg, the validated R2 to 0.95, RMSE as 5.49 mg/kg, the model had good prediction accuracy of fluvo-aquic soil alkali hydrolysable nitrogen content. The comparison of methods showed that the genetic algorithms combined with partial least squares regression could not only obtain higher prediction accuracy, but also simplify models. Therefore, the genetic algorithms combined with partial least squares regression method may effectively select the characteristic wave bands of soil alkali hydrolysable nitrogen, reduce the model variables and improve the estimation accuracy.