| 光谱学与光谱分析 |
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| Prediction of Cadmium Content in the Leaves of Navel Orange in Heavy Metal Contaminated Soil Using VIS-NIR Reflectance Spectroscopy |
| SHI Rong-jie1, 2, PAN Xian-zhang1*, WANG Chang-kun1, LIU Ya1, 2, LI Yan-li1, 2, LI Zhi-ting1, 2 |
1. Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
2. University of Chinese Academy of Sciences，Beijing 100049，China |
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Abstract Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium(Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700～730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R2) of linear regression model based on spectral index was nearly 0.8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R2CV of approximately 0.9. Furthermore, the standard normal variate transformation（SNV）in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.
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Received: 2014-08-17
Accepted: 2014-11-20
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Corresponding Authors:
PAN Xian-zhang
E-mail: panxz@issas.ac.cn
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[1] TIAN Guo-liang, BAO Pei-li, LI Jian-jun, et al(田国良, 包佩丽, 李建军, 等). Remote Sensing of Environment(环境遥感), 1990, 5(02): 140.
[2] REN Hong-yan, ZHUANG Da-fang, PAN Jian-jun, et al(任红艳, 庄大方, 潘剑君, 等). Journal of Geo-Information Science(地球信息科学), 2008, 10(03): 314.
[3] Liu M, Liu X, Li J, et al. International Journal of Applied Earth Observation and Geoinformation, 2012, 19(0): 12.
[4] MA Chao-fei, MA Jian-wen, HAN Xiu-zhen(马超飞, 马建文, 韩秀珍). Jouanal of Remote Sensing(遥感学报), 2001, 5(05): 334.
[5] Schuerger A C, Capelle G A, Di Benedetto J A, et al. Remote Sensing of Environment, 2003，84(4): 572.
[6] Kooistra L, Wehrens R, Leuven R S E W, et al. Analytica Chimica Acta, 2001, 446(1-2): 97.
[7] Kooistra L, Salas E A L, Clevers J G P W, et al. Environmental Pollution, 2004, 127(2): 281.
[8] Rosso P H, Pushnik J C, Lay M, et al. Environmental Pollution, 2005, 137(2): 241.
[9] Daughtry C S T, Walthall C L, Kim M S, et al. Remote Sensing of Environment, 2000, 74(2): 229.
[10] LIU Su-hong, LIU Xin-hui, HOU Juan, et al(刘素红，刘新会，侯 娟，等). Science in China Series E: Technological Sciences(中国科学E辑：技术科学), 2007, 37(5): 693.
[11] Sims D A, Gamon J A. Remote Sensing of Environment, 2002, 81(2-3): 337.
[12] Vogelmann J E, Rock B N, Moss D M. International Journal of Remote Sensing, 1993, 14(8): 1563.
[13] Zarco-Tejadaa P J, Berjón A, Millerc J R. Remote Sensing of Environment, 2004, 90(4): 463.
[14] Liu M, Liu X, Li M, et al. Biosystems Engineering, 2010, 106(3): 223.
[15] WANG Hui-wen, WU Zai-bin, MENG Jie(王惠文, 吴载斌, 孟 洁). Partial Least Squares Regression-Linear and Nolinear Methods(偏最小二乘回归的线性与非线性方法). Beijing：National Defense Industry Press（北京: 国防工业出版社）. 2006.
[16] CHI Guang-yu, LIU Xin-hui, LIU Su-hong, et al(迟光宇, 刘新会, 刘素红, 等). Environmental Science and Technology（环境科学与技术), 2005, 28(S1): 16.
[17] YAO Fu-qi, ZHANG Zhen-hua, YANG Run-ya, et al(姚付启, 张振华, 杨润亚, 等). Transactions of the Chinese Society of Agricultural Engineering（农业工程学报), 2009, 25(S2): 123.
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