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 dependant on water quality in Lake Taihu. Thus we developed a further 2-layer BP neural net model based on 4 input nodes, 7 hide nodes and 1 output node to for calculating 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 16 samples and suggests 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% from the BP model.. However, there were only 3 samples 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.