基于TM数据和神经网络模型的太湖叶绿素A浓度遥感定量监测

【作者】 吕恒； 江南； 罗潋葱；

【机构】 中国科学院南京地理与湖泊研究所；

【摘要】 本文利用TM(ETM)数据与准实时的地面采样数据,建立了太湖的叶绿素浓度反演模型。分析了经6S模型校正后的波段反射率比值与叶绿素A浓度相关性,表明TM3/(TM1+TM4) 与叶绿素浓度的相关性最好,并以此建立了太湖的叶绿素浓度线性反演模型LnChlA=-9.247*(TM1+TM4)/(TM2+TM3) -27.903*TM3/(TM1+TM4) +24.518,但由于太湖是典型的二类水体,光谱特征复杂,这一简单的线性反演精度并不高,因此,建立了由4个输入节点、7个隐含节点、1个输出节点的两层BP神经网络模型反演太湖的叶绿素浓度,结果表明神经网络模型的反演精度要远远高于线性反演模型,从16个测试样本来看,利用神经网络模型反演叶绿素浓度,最大相对误差仅为35.43%,预测相对误差小于30%的有15个点,占了总测试样本93.75%,而线性反演模型预测相对误差在30%以下的仅有3个点,这表明对于复杂的关系,神经网络具有非常出色的映射能力。更多还原

【Abstract】 Based on TM (ETM) data and in-situ measurements of chlorophyll-a concentration (Chl-a) in Lake Taihu, analysis was conducted to decide the correlation between Chl-a and the ratios of different reflectance corrected by the 6S model. The results show Chl-a is closely related to TM3/(TM1+TM4) and the inverse model to infer Chl-a in Lake Taihu can be written as Ln(Chl-a)=-9.247*(TM1+TM4)/(TM2+TM3) -27.903*TM3/(TM1+TM4) +24.518. However, the accuracy of this model can not be assured due to the complexity of spectral reflectance strongly depending water quality in Lake Taihu. Thus a further 2-layer BP neural net model based on 4 input nodes, 7 hide nodes and 1 output node was made to decide Chl-a in the lake. The derived results reveal that the BP model has much higher accuracy than the linear model. A test was made based on the chosen 16 samples and the results suggest that the maximum relative error (RE) of BP model was only 35.43%. Of all the samples, 15 ones had a RE of less than 30%, accounting for 93.7% of the total samples. However, there were only 3 with RE less than 30% from the results derived from the linear model. The comparison shows that the BP model has high availability for inferring chl-a of surface water having complex spectral reflectance.更多还原