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首页> 外文期刊>Environmental Science & Technology >Monitoring Arsenic Contamination in Agricultural Soils with Reflectance Spectroscopy of Rice Plants
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Monitoring Arsenic Contamination in Agricultural Soils with Reflectance Spectroscopy of Rice Plants

机译：水稻植株的反射光谱监测农业土壤中的砷污染

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摘要

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.
机译：这项研究的目的是探索可行性，并研究利用水稻植物的反射光谱快速监测农业土壤中砷（As）污染的机制。应用了几种数据预处理方法来提高预测精度。通过使用水稻植物的实验室光谱和田间光谱进行偏最小二乘回归（PLSR）以及使用根据田间光谱计算得出的归一化差异光谱指数（NDSI）进行线性回归，可以实现对土壤As含量的预测。对于实验室光谱，使用Savitzky-Golay平滑法（SG），一阶导数和均值中心（MC）获得了预测土壤砷含量的最佳PLSR模型（预测的均方根误差（RMSEP）= 14.7 mg kg〜（ -1）； r = 0.64；残留预测偏差（RPD）= 1.31）。对于现场光谱，还使用SG，一阶导数和MC（RMSEP = 13.7 mg kg〜（-1）； r = 0.71； RPD = 1.43）获得了最佳PLSR模型。此外，812和782 nm的NDSI的预测精度为r = 0.68，RMSEP = 13.7 mg kg〜（-1）和RPD = 1.36。这些结果表明，利用水稻植物的反射光谱监测农业土壤中的砷污染是可行的。预测机制可能是土壤中As含量与水稻叶片或冠层中叶绿素a / b含量和细胞结构之间的关系。

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  • 来源
    《Environmental Science & Technology》 |2014年第11期|6264-6272|共9页
  • 作者

    Tiezhu Shi; Huizeng Liu; Junjie Wang; Yiyun Chen; Teng Fei; Guofeng Wu;

  • 作者单位

    School of Resource and Environmental Science and Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079 Wuhan, China;

    School of Resource and Environmental Science and Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079 Wuhan, China;

    School of Resource and Environmental Science and Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079 Wuhan, China;

    School of Resource and Environmental Science and Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079 Wuhan, China;

    School of Resource and Environmental Science and Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079 Wuhan, China;

    Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geolnformation and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and College of Life Sciences, Shenzhen University, 518060 Shenzhen, China School of Resource and Environmental Science and Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, 430079 Wuhan, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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