中文摘要：

利用ASD地物光谱仪对不同浓度下小球藻、聚球藻及其混合藻的反射光谱进行测量,得到3组藻类在不同叶绿素a（Chl-a）浓度下的反射光谱,同时进行了Chl-a浓度测量。利用Matlab软件神经网络模型中的径向基函数对得到的高光谱数据进行高光谱曲线拟合,在拟合结果基础上,提取小球藻在652nm处、聚球藻在609nm处以及混合藻在652nm和609nm处的光谱吸收指数（SpectralAbsorptionFeatureParameter,SAFP）建立了两种单一藻类的定量模型,单一小球藻Chl-a最优定量模型为吸收峰深度模型Chl-a=10.059e713.97H,聚球藻的Chl-a最优定量模型为吸收峰斜率模型Chl-a=3×1012K2＋1×107K＋56.555。并在对两种单一藻类的定量模型研究基础上用光谱吸收指数对该两种藻的混合藻进行了Chl-a浓度分离。通过对比模型计算结果的均方根误差（RootMeanSquareError,RMSE）,除吸收峰对称度模型分离结果不太理想外,吸收峰深度分离模型、吸收峰斜率分离模型、吸收峰光谱吸收指数（SAI）分离模型的分离结果都很好,分离效果最好的为SAI分离模型。

英文摘要：

Based on the hyperspectral remote sensing,the hyperspectral characteristic of Chlorella vulgaris,Synechococcus sp and their mixed algae were obtained by using the ASD HandHeld Spec.The concentrations of Chl-a for the three groups of algae were measured.Radial basis function of neural network technique was used to fit the curve of algae.The fitted spectral values were used to calculate the spectral absorption feature parameter（SAFP）.The SAFP of Chlorella vulgaris at 652 nm and Synechococcus sp at 609 nm were used to establish the quantitative models of Chl-a.The optimal hyperspectral model of Chlorella vulgaris was Chl-a=10.059e713.97H using the depth of SAFP,and the optimal hyperspectral model of Synechococcus sp was Chl-a=3×1012K2＋1×107K＋56.555 using the slope of SAFP.Based on the models of two individual algae,the SAFP separation models were created to separate the mixed algae.The results of root mean square（RMSE）demonstrated that except the absorption peak symmetry separation model,the other SAFP separation models could get good results,and the best was spectral absorption index separation model.