基于Landsat影像的清河水库总悬浮物浓度反演模型研究
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  • 英文篇名:A Study of Inversion Modeling of Chlorophyll-A in Qinghe Reservoir Based on Landsat Satellites Data
  • 作者:阎孟冬 ; 杨国范 ; 殷飞
  • 英文作者:YAN Meng-dong;YANG Guo-fan;YIN Fei;College of Water Resources,Shenyang Agricultural University;Shenyang Energy Engineering Institute of Technology;Agricultural Science and Technology College;
  • 关键词:最小二乘支持向量机 ; 比值线性回归模型 ; 清河水库 ; 总悬浮物
  • 英文关键词:LS-SVM;;ratio of the linear regression model;;Qinghe Reservoir;;TSM
  • 中文刊名:ZNSD
  • 英文刊名:China Rural Water and Hydropower
  • 机构:沈阳农业大学水利学院;沈阳工学院能源与水利学院;吉林农业科技学院;
  • 出版日期:2016-12-15
  • 出版单位:中国农村水利水电
  • 年:2016
  • 期:No.410
  • 基金:辽宁省科学事业公益性研究基金项目“基于MODIS数据玉米覆盖下土壤水分监测研究”(2011005002);; 农业部公益性行业科研专项经费项目“北方主要作物抗旱节水综合技术研究与区域示范-辽西北耕地土壤墒情监测及其分布规律研究与应用子课题”(200903007)
  • 语种:中文;
  • 页:ZNSD201612016
  • 页数:5
  • CN:12
  • ISSN:42-1419/TV
  • 分类号:79-83
摘要
为了及时、准确的了解清河水库总悬浮物浓度情况,采用Landsat卫星OLI数据,通过SPSS软件分析计算OLI数据的单波段及波段组合与总悬浮物浓度之间的相关关系,选取相关系数最大者分别构建比值线性回归模型和非线性的最小二乘支持向量机模型(LS-SVM),对清河水库总悬浮物浓度进行了遥感定量反演研究。结果表明,相比于比值线性回归模型,LS-SVM模型将预测值与实际值的可决系数R2从0.686提高到0.88,平均相对误差从3.52%减小到3.16%,利用LS-SVM模型对总悬浮物浓度的反演精度显著提高。
        To timely and accurately monitor Qinghe Reservoir total suspended matter(TSM),with the OLI data of Landsat satellite and the calculation of SPSS,this paper analyzes the relevant relationship between TSMconcentration and single band or band combinations,the ratio of the linear regression model and nonlinear least squares support vector machine(LS-SVM) is established by taking the relation as the correlation coefficient of largest,then quantitative remote sensing inversion research is done on TSMconcentration of Qinghe Reservoir. Results indicate that the LS-SVMmodel can make predictions and actual value of determination coefficient R2 increase from 0.686 to 0.88 and the average relative error decreases from 3.52% to 3. 16% compared with the ratio linear regression model. Therefore,the LS-SVMmodel is used for the inversion precision of TSMincrease.
引文
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