The invasive species Spartina alterniflora show a significant coexistence zonation pattern with local Phragmites australis in different mixture ratio, increasing the difficulty to monitor their distribution directly by remote sensing. Canopy chlorophyll content (CCC) is an important indicator to monitor the growth and physiological status. The objective of this study was to estimate CCC under different mixture ratio. Five spectral indices were selected and combined via back propagation (BP) neural network model for estimating CCC. Combining multi-indices yielded better results (R² = 0.7729, RMSE = 53.01 ug.cm^-2) on average than the best single spectral index (R² = 0.7190, RMSE = 63.53 ug.cm^-2) without distinguishing interspecies competition, with a total increase of 7.5% in the R² and a decrease of 16.56% in the RMSE. Meanwhile, when considering interspecies competition, the estimating results obtained by the BP neural network model achieved a further improvement of the R² value, ranging from 3.57% to 20.37%, while the prediction error reduced at varying degrees (maximum reduction of 23.78%). The results indicate that combining multi-indices by BP neural network model can alleviate the influence of interspecies competition and achieve higher estimating accuracy.