Spectral Response and Inversion Models for Prediction of Total Copper Content in Soil of Xifanping Mining Area
TENG Jing1, 2, 3, HE Zheng-wei1, 2, 3*, NI Zhong-yun2, 4, ZHAO Yin-quan2, 4, ZHANG Zhi1, 2, 3
1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology)，Chengdu 610059，China 2. Key Laboratory of Geoscience Spatial Information Technology，Ministry of Land and Resources，Chengdu 610059，China 3. College of Earth Sciences，Chengdu University of Technology，Chengdu 610059，China 4. College of Tourism and Urban-Rural Planning，Chengdu University of Technology，Chengdu 610059，China
Abstract：In order to solve the problem of high cost and low efficiency by using the traditional soil geochemical survey methods, this paper studied the simple detection method of soil heavy metal content with visible and near-infrared reflectance spectroscopy. The study collected visible and near-infrared reflectance spectroscopy of soil samples in Xifanping mining area; then treated the reflectance spectroscopy with six mathematic changes such as differentials and continuum removal in advance; the next step was to select characteristic wavelengths that were sensitive to soil copper content by using stepwise regression method and Pearson correlation coefficient as set of comprehensive characteristic variables; finally, utilized different methods and parameters of characteristic variable selection to build the soil total copper content models and tested them. Results showed that: to extract the information of copper content in soil, the performance of different spectral transform methods varied, and each spectrum transform method corresponded to its certain sensitive spectral ranges; the inversion models based on the integrated spectrum transform information were better than that based on only one kind of spectrum transform information; as for establishing the prediction model of soil copper content by using the integrated spectrum transform information, backward elimination was better than forward selection and stepwise selection, and when the Removal is 0.20, the optimum model was obtained, its coefficients of determination(R2)and determination coefficients of prediction(R2pre)reached 0.851 and 0.830, root mean square error of calibration(RMSEC)and root mean square error of prediction(RMSEP)were 0.349 and 0.468 mg·kg-1. The model has a good precision, and it provides a train of thought for the detection of other soil heavy metal elements with visible and near-infrared reflectance spectroscopy.
Key words：Soil geochemistry;Spectral transformation;Characteristic variable selection;Hyperspectral inversion model;Xifanping mining area
[1] YANG Zhen, HU Guang-dao, MEI Hong-bo(杨 震, 胡光道, 梅红波). Science Technology and Engineering(科学技术与工程), 2015, 15(9): 32. [2] Liu Yaolin, Li Wei, Wu Guofeng, et al. Geo-spatial Information Science, 2011, 14(1): 10. [3] Stenberg B, Rossel R A V, Mouazen A M, et al. Advances in Agronomy, 2010, 107: 163. [4] Shi Tiezhu, Chen Yiyun, Liu Yaolin, et al. Journal of Hazardous Materials, 2014, 265: 166. [5] Song Yinxian, Li Fengling, Yang Zhongfang, et al. Applied Clay Science, 2012, 64: 75. [6] Sonia G, Noamen R, Saadi A. Journal of Geographic Information System, 2012, 4: 242. [7] Pascucci S, Belviso C, Cavalli R M, et al. Remote Sensing of Environment, 2012, 123: 139. [8] Summers D, Lewis M, Ostendorf B, et al. Ecological Indicators, 2011, 11(1): 123. [9] Moros J, de Vallejuelo S F O, Gredilla A, et al. Environmental Science ＆ Technology, 2009, 43(24): 9314. [10] Choe E, van der Meer F D, van Ruitenbeek F J A, et al. Remote Sensing of Environment, 2008, 112(7): 3222. [11] Siebielec G, McCarthy G W, Stuczynski T I, et al. Journal of Environmental Quality, 2004, 33: 2056. [12] Malley D F, Williams P C. Environmental Science ＆ Technology, 1997, 31(12): 3461. [13] Kemper T, Sommer S. Environmental Science ＆ Technology, 2002, 36(12): 2742. [14] WU Ming-zhu, LI Xiao-mei, SHA Jin-ming(吴明珠, 李小梅, 沙晋明). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(6): 1660. [15] XU Ming-xing, WU Shao-hua, ZHOU Sheng-lu, et al(徐明星, 吴绍华, 周生路, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2011, 30(2): 109. [16] WANG Wei, SHEN Run-ping, JI Cao-xiang(王 维, 沈润平, 吉曹翔). Remote Sensing Technology and Application(遥感技术与应用), 2011, 26(3): 348. [17] ZOU Xiaobo, ZHAO Jiewen, Malcolm J W, et al. Anal. Chim. Acta, 2010, 667(1-2): 14. [18] Whittingham M J, Stephens P A, Bradbury R B, et al. Journal of Animal Ecology, 2006, 75: 1182.
