| 引用本文: | LIU Jlan-ping , ZHANG Yu-chao , QIAN Xin,ZHANG Ning-hong , YU Jian-qiao.Case Study on Application of Remote Sensing Technology to Monitoring Chlorophyll - a Levels in Taihu Lake[J].Environmental Monitoring and Forewarning,2009,1(2):33~36 |
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| 摘要: |
| 水质遥感技术在湖泊水质监测领域内的应用具有十分积极的意义。在总结现有水质遥感反演方法的基础上，选取了遥感指数法和神经网络法两种理论完全不同的反演方法，构建太湖叶绿素a与MODIS影像波段间的函数关系，并从反演能力和反演精度两个角度对上述方法进行了比较研究。结果表明，神经网络模型的非线性特征能够敏感地把握住叶绿素a浓度变化在反射波谱信息上的微小响应，较为成功地反演出叶绿素a与反射光谱信息间的非线性关系。神经网络模型的反演能力和反演精度均优于遥感指数方法，具有较好的应用前景。 |
| 关键词: 水质遥感技术 遥感指数 人工神经网络 太湖 |
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| 基金项目:江苏省环境监测科研基金项目（0811）;江苏省科技厅太湖水污染治理专项（BS2007157） |
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| Case Study on Application of Remote Sensing Technology to Monitoring Chlorophyll - a Levels in Taihu Lake |
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LIU Jlan-ping , ZHANG Yu-chao , QIAN Xin1, ZHANG Ning-hong , YU Jian-qiao2
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1.State Key Laboratory of Pollution Control and Resouree Reuse, Sehool of the Environment, Nanjing University, Nanjing 210093, China;2.Jiangsu Environmental Monitoring Center, Nanjing 210036, China
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| Abstract: |
| Based on the previous relevant studies, remote sensing index and artificial neural network were used to construct functional relationship between Chl - a concentrations in the Taihu Lake and spectrum reflectance of MODIS image. Comparison was made between performance of the two techniques in terms of retrieval ability and precision and the results indicated that artificial neural network could catch subtle responses in spectrum to changes in Chl - a concentration and depict the nonlinear relation between reflected spectrum and Chl -a concentrations relatively successfully. The artificial neural network proved to be a better technique than the remote sensing index in terms of retrieval ability and retrieval precision, therefore will have a promising perspective in water quality monitoring. |
| Key words: remote sensing of water quality remote sensing index artificial neural network Taihu Lake |
