Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants.
Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants.
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DOI：
10.1021/es405361n
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发表时间：
2014-05
影响因子：
11.4
通讯作者：
Tiezhu Shi;Huizeng Liu;Junjie Wang;Yiyun Chen;Teng Fei;Guofeng Wu
中科院分区：
文献类型：
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作者：
Tiezhu Shi;Huizeng Liu;Junjie Wang;Yiyun Chen;Teng Fei;Guofeng Wu
本研究的目的是探索利用水稻植株的反射光谱快速监测农业土壤砷污染的可行性和机理。为了提高预测精度，采用了多种数据预处理方法。采用偏最小二乘回归(PLSR)和归一化差分光谱指数(NDSI)回归方法对土壤As含量进行了预测。对于实验室光谱，采用Savitzky-Golay平滑(SG)、一阶导数和平均中心(MC)(预测均方根误差(RMSEP)=14.7 mg kg(-1)；r=0.64；剩余预测偏差(RPD)=1.31)得到了预测土壤As含量的最优PLSR模型。对于场谱，用SG、一阶导数和MC也得到了最优的PLSR模型(RMSEP=13.7 mg kg(-1)；r=0.71；RPD=1.43)。812和782 nm的NDSI预测精度为r=0.68，RMSEP=13.7 mg kg(-1)，RPD=1.36。这些结果表明，利用水稻植株的反射光谱监测农业土壤中的砷污染是可行的。预测机理可能是土壤As含量与水稻叶片或冠层细胞结构和叶绿素a/b含量之间的关系。
The objective of this study was to explore the feasibility and to investigate the mechanism for rapidly monitoring arsenic (As) contamination in agricultural soils with the reflectance spectra of rice plants. Several data pretreatment methods were applied to improve the prediction accuracy. The prediction of soil As contents was achieved by partial least-squares regression (PLSR) using laboratory and field spectra of rice plants, as well as linear regression employing normalized difference spectral index (NDSI) calculated from fild spectra. For laboratory spectra, the optimal PLSR model for predicting soil As contents was achieved using Savitzky-Golay smoothing (SG), first derivative and mean center (MC) (root-mean-square error of prediction (RMSEP)=14.7 mg kg(-1); r=0.64; residual predictive deviation (RPD)=1.31). For field spectra, the optimal PLSR model was also achieved using SG, first derivative and MC (RMSEP=13.7 mg kg(-1); r=0.71; RPD=1.43). In addition, the NDSI with 812 and 782 nm obtained a prediction accuracy with r=0.68, RMSEP=13.7 mg kg(-1), and RPD=1.36. These results indicated that it was feasible to monitor the As contamination in agricultural soils using the reflectance spectra of rice plants. The prediction mechanism might be the relationship between the As contents in soils and the chlorophyll-a/-b contents and cell structure in leaves or canopies of rice plants.