Soil Classification Model Based on the Characteristics of Soil Reflectance Spectrum
LIU Huan-jun1,2, MENG Xiang-tian1, WANG Xiang1, BAO Yi-lin1, YU Zi-yang1, ZHANG Xin-le1*
1. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
Abstract：The soil reflectance spectrum curve reflects the physical and chemical properties and internal structure of the soil. Hyperspectral remote sensing technology has been used to classify soil based on the soil reflectance spectrum characteristics. The first order differential principal component of soil reflectance spectrum is generally used to construct the spectral classification model, but the principal component data is lack of physical significance, contrast and limited scope of application. Compared with the first-order differential reflectivity data, the extraction of the characteristic parameters based on the de-enveloping line can improve the accuracy of soil classification and find a high-precision soil classification model. In this study, four typical soils (wind-sand soil, meadow soil, calcareous soil) were selected in Nong’an County, Jilin Province. The collected soil samples were dried, ground and treated by 2mm sieve. ASD FiledSpec®3 portable spectrometer was used to measure the visible near infrared spectrum of the treated soil samples, and the spectral data of the soil samples were obtained. The spectral data were smoothed by nine points, the noise was reduced by 10nm resampling, and the processed data were processed by the first order differential principal component and the de-enveloping line respectively. The spectral characteristic parameters were extracted by using the continuum removed line of soil samples. The first order differential principal component data and spectral characteristic parameters were input into Logistic clustering model, artificial neural network clustering model and K-means clustering model respectively. In this paper, the reflectance spectra of different soils, the difference of the envelope, the reflectivity curve of the same soil, and the advantages and disadvantages of the soil classification are determined. And the spectral characteristic parameters which can distinguish different soil types are extracted on the basis of de-enveloping line. Secondly, when the first order differential principal component is compared with the spectral characteristic parameter as input, the accuracy differences of the three spectral classification models are compared and the reasons for the difference in the accuracy of different models are analyzed. The results showed that: (1) The difference of the reflectance spectra of the four soils was small, and the spectral difference between the four soils could be greatly enhanced by the continuum removedline. The spectral characteristic parameters with clear physical meaning are constructed on the basis of the de-enveloping line. (2) The first order differential principal component and spectral characteristic parameters are introduced into the three clustering models respectively. The soil spectral classification model with spectral characteristic parameters as input is more accurate than that of the first order differential principal component model, because the spectral characteristic parameters retain the physical meaning of the original data. More accurately reflects the differences between different soil types, and due to the fact that the first order differential principal component data have a certain degree of fuzziness and are lack of contrast between different ranges, it is more advantageous to use spectral characteristic parameters as input in soil classification. (3) Among the three soil classification models, the Logistic clustering model has the highest classification accuracy of 76.67% kappa coefficient of 0.56; the average classification accuracy of the artificial neural network model is 72.50% and the Kappa coefficient is 0.48 K-mean clustering model has the lowest classification accuracy, only 65.00% . And Kappa coefficient is 0.33. The research results can provide technical support for fine mapping of soil and the development of soil classification instrument.
Key words：Soil classification; Decision tree; Continuum removed; Nong’an County
刘焕军，孟祥添，王 翔，鲍依临，于滋洋，张新乐. 反射光谱特征的土壤分类模型[J]. 光谱学与光谱分析, 2019, 39(08): 2481-2485.
LIU Huan-jun, MENG Xiang-tian, WANG Xiang, BAO Yi-lin, YU Zi-yang, ZHANG Xin-le. Soil Classification Model Based on the Characteristics of Soil Reflectance Spectrum. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2481-2485.
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