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2020 , Vol. 37 >Issue 11: 141 - 148

DOI: https://doi.org/10.11988/ckyyb.20191003

信息技术应用

基于高光谱数据的汉江中下游典型河段水体悬浮物遥感反演

  • 肖潇 ,
  • 徐坚 ,
  • 赵登忠 ,
  • 程学军 ,
  • 李国忠 ,
  • 赵保成 ,
  • 徐健
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  • 1.长江科学院 空间信息技术应用研究所,武汉 430010;
    2.武汉大学 水利水电学院,武汉 430079
肖 潇(1986-),女,湖北武汉人,高级工程师,博士,主要从事水环境遥感研究。Email:danny_xiaoxiao@126.com

收稿日期: 2019-08-16

  修回日期: 2019-12-16

  网络出版日期: 2020-12-02

基金资助

国家重点研发专项(2018YFD1100405); 长江科学院中央级公益性科研院所基本科研业务费项目(CKSF2019410/KJ,CKSF2019411/KJ,CKSF2019528/KJ)

收起

Remote Sensing Retrieval of Total Suspended Solids Concentration for Typical Reach of Hanjiang River Using Hyperspectral Data

  • XIAO Xiao ,
  • XU Jian ,
  • ZHAO Deng-zhong ,
  • CHENG Xue-jun ,
  • LI Guo-zhong ,
  • ZHAO Bao-cheng ,
  • XU Jian
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  • 1. Spatial Information Technology Application Department, Yangtze River Scientific Research Institute,Wuhan 430010, China;
    2. School of Water Resources and Hydropower Engineering, Wuhan University,Wuhan 430079, China

Received date: 2019-08-16

  Revised date: 2019-12-16

  Online published: 2020-12-02

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摘要

选择汉江中下游典型河段作为研究区域,利用2012—2013年实测水质数据及高光谱数据,基于有效信息变量筛选和神经网络算法构建研究区水体悬浮物浓度高光谱反演模型,分析评价了模型性能与估测效果,讨论了研究区水体悬浮物浓度分布特征。研究结果表明:基于变量投影重要性指数和神经网络优势构建的高光谱反演模型在反演精度、稳定性和适应性方面表现出优异的性能;而对于基于简单相关性分析构建的单波段模型和比值模型而言,建模样本选择对模型精度有较大影响,导致模型反演精度、稳定性和适用性较差;汉江中下游典型河段水体总悬浮物浓度整体上在18.8~187.0 mg/L之间变化,季节性差异明显,即春、夏两季悬浮物浓度低于秋季。

关键词： 水体悬浮物; 高光谱; 悬浮物浓度; 遥感反演; 变量投影重要性指数; BP神经网络; 汉江中下游

本文引用格式

肖潇 , 徐坚 , 赵登忠 , 程学军 , 李国忠 , 赵保成 , 徐健 . 基于高光谱数据的汉江中下游典型河段水体悬浮物遥感反演[J]. 长江科学院院报, 2020 , 37(11) : 141 -148 . DOI: 10.11988/ckyyb.20191003

Abstract

A hyperspectral model of retrieving suspended solids concentration in typical reaches of the middle and lower Hanjiang River is constructed based on effective information variable selection and neural network algorithm according to the water quality and hyperspectral data measured in 2012-2013. The performance and estimation effectiveness of the model are analyzed and assessed, and the distribution characteristics of suspended solids concentration in the waters of the study area are discussed. Results demonstrate that the hyperspectral retrieval model based on variable importance in projection index and neural network advantage has excellent retrieval accuracy, stability and adaptability; in contrast, for the single-band model and ratio model based on simple correlation analyses, the selection of modeling samples has a huge influence on the accuracy of the model, leading to poor retrieval accuracy, stability and adaptability. The concentration of suspended solids in the typical reaches of the middle and lower Hanjiang River varies within 18.8-187.0 mg/L. Seasonal differences are quite obvious: the concentrations of suspended solids in spring and summer are lower than those in autumn.

Key words： suspended solids in water; hyperspectrum; concentration of total suspended solids; remote sensing retrieval; variable importance in projection index; BP neural network; middle and lower Hanjiang River

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