光谱学与光谱分析 |
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Study of Spatial Interpolation of Soil Cd Contents in Sewage Irrigated Area Based on Soil Spectral Information Assistance |
CHEN Tao1, 2, CHANG Qing-rui1, 2*, LIU Jing1, 2 |
1. College of Resources and Environment, Northwest A & F University, Yangling 712100, China 2. Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling 712100, China |
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Abstract To acquire the accuracy distribution information of soil heavy metal, improving interpolation precision is very important for agricultural safety production and soil environment protection. In the present study, the spatial variation and Cokriging interpolation of soil Cd was studied in a sewage irrigation area. Fifty two soil samples were collected to measure the contents of soil total Cd (TCd), available Cd (ACd), pH, organic matter (OM), iron oxide (Fe2O3) and soil reflection spectrum. Through correlation analysis, it was found that TCd and ACd had a significant correlation with soil first-order differential spectrum (-0.585** at 759 nm and -0.551** at 719 nm, respectively), which were much higher than the correlation coefficients between soil Cd contents and other environmental variables (pH, OM and Fe2O3). The spatial patterns of soil Cd were predicted by Cokriging which used soil first-order differential spectrum as covariate. Compared with the Kriging, the root-mean-square error decreased by 8.22% for TCd and 20.09% for ACd, respectively; the correlation coefficients between the predicted values and measured values increased by 27.45% for TCd and by 53.13% for ACd, respectively. Meanwhile, the prediction accuracy improved by Cokriging with soil spectrum as covariate was still higher than by Cokriging with soil environment variables (OM and Fe2O3). Therefore, it was found that Cokriging was a more accurate interpolation method which could provide more precise distribution information of soil heavy metal. At the same time, soil reflection spectrum was shown to be more economic, time-saving and easier to acquire than these usual environment variables, which indicated that soil spectrum information is more suited as a covariate used in Cokriging.
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Received: 2012-12-22
Accepted: 2013-03-20
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Corresponding Authors:
CHANG Qing-rui
E-mail: changqr@nwsuaf.edu.cn
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[1] Burgess T M, Webster R. Journal of Soil Science, 1980, 31(2): 333. [2] Andronikov S V, Davidson D A, Spiers R B. Water Air and Soil Pollution, 2000, 120(1-2): 29. [3] Juang K W, Lee D Y. Journal of Environmental Quality, 1998, 27(2): 355. [4] PANG Su, LI Ting-xuan, WANG Yong-dong, et al(庞 夙, 李廷轩, 王永东, 等). Agricultural Sciences in China(中国农业科学), 2009, 42(8): 2828. [5] BAO Shi-dan(鲍士旦). Soil Agricultural Chemistry Analysis(土壤农化分析). Beijing: China Agriculture Press(北京: 中国农业出版社), 1999. [6] LI Min-zan(李民赞). Spectrum Analysis Technology and its Application(光谱分析技术及其应用). Beijing: Science Press(北京: 科学出版社), 2006. [7] XIE Xian-li, SUN Bo, HAO Hong-tao(解宪丽, 孙 波, 郝红涛). Acta Pedologica Sinica(土壤学报), 2007, 44(6): 982. [8] PU Rui-liang, GONG Peng(浦瑞良, 宫 鹏). Hyperspectral Remote Sensing and Application(高光谱遥感及其应用). Beijing: Higher Education Press(北京: 高等教育出版社), 2003. [9] Webster R, Oliver M A. Geostatistics for Environmental Scientists. Chichester: John Wiley and Sons Ltd, 2001. 1. [10] PAN Gen-xing, GAO Jian-qin, LIU Shi-liang, et al(潘根兴, 高建芹, 刘世梁, 等). Journal of Nanjing Agricultural University(南京农业大学学报), 1999, 22(2): 46. [11] Manta D S, Angelone M, Bellanca A, et al. The Science of the Total Environment, 2002, 300(1-3): 229. [12] Chen T, Liu X M, Li X, et al. Environment Pollution, 2009, 157(3): 1003. |
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