Horizon Classification in Soil Profile Using Imaging Spectroscopy
ZHENG Guang-hui1,2, JIAO Cai-xia2, SHANGGUAN Chen-xi2, WU Wen-qian2, LIU Yi3, HONG Chang-qiao2,4
1. Collaborative Innovation Center on Forecast and Evaluation of Meteologicla Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
2. School of Geographic Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
4. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023，China
Abstract：The soil profile is one of the core topics in pedogenesis research, but traditional pedological observations of soil profiles rely on the use of visible light and a toolbox that has not changed in the past decades. The imaging spectroscopy can provide high-resolution spatial and spectral soil profile information, which gives continuous depth functions of soil properties and compensates for the large gap between the sampling depths of reflectance spectroscopy. The objective of this study is to analyze the classification of soil horizon in a profile by investigating the spectral data of imaging spectroscopy collected in the laboratory. The support vector machine (SVM) method was used to classify the spectral data, and the feasibility and influence factors of the imaging spectroscopy for classification were studied. Firstly, the morphological characteristics of the average spectral curves of each horizon in sample profile were analyzed qualitatively. Secondly, depth dynamic and scatter plot of principal components were qualitatively analyzed to explain the feasibility of horizon classification using profile imaging spectroscopy. Finally, One thousand times computations were carried out to reduce the classification error by partitioning random dataset and building prediction model. The prediction results can quantificationally testify the feasibility and the influence factors were discussed by the percentage of wrong classification in prediction results. The results indicated that the four average spectral curves in sample profile differed and reflected the variation in depth derived from pedogenic processes. The principal components of the imaging spectral data showed the continuous change in the depth direction of the soil profile and the grouping feature in scatter plot, which proved that imaging spectroscopy reflected the difference between the genetic horizons and can be used for the horizon classification. The average accuracy of classification prediction reached 93.08%. Moreover, it was found that the sample with similar scattering distribution and locating transition region were classified in wrong classes easily. This study provides a theoretical basis for horizon classification, and proves that imaging spectroscopy is a potential technology for mapping soil profile.
Key words：Soil profile; Horizon; Imaging spectroscopy; Support vector machine (SVM)
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