Phyllostachys edulis is one of the most important and intensively managed forest resources in southern China， Chlorophyll Content Index（CCI） is a crucial indicator of plant health and growth. It is of great significance to realize remote sensing inversion of chlorophyll content in Moso Bamboo forest to monitor the health degree of it. Firstly， three ways of transform including HSV （Hue-Saturn-value） transform、GS （Gram-Schmidt Pan Sharpening spectral Sharpening method） transform and PCA （Principal Component Analysis） were used to make sure that Landsat 8 multispectral image and Unmanned Aerial Vehicle （UAV） high resolution single-band image data were fused well together. Secondly， 8 kinds of vegetation cover indices were then constructed based on multi-source remote sensing data， moreover， three machine learning models including K-nearest Neighbor （KNN） regression， Random Forest （RF） regression as well as CatBoost regression were applied to ensure vegetation index and chlorophyll content could be fitted. Finally， the inversion model of chlorophyll unit content in Moso Bamboo forest was then established. The results indicated that ：（1） In terms of fusion effect， it turned out that GS was the optimal model cause various evaluation parameters derived from it such as mean value、standard deviation、mean gradient joint entropy and spatial frequency were all the highest， which were 73.407 8、80.672 9、29.699 2、9.765 5 and 74.876 9， respectively. （2） In the validation set based on fused multispectral data， Landsat 8 multispectral data and UAV data， RF algorithm turned to be the best algorithm（RF algorithm's corresponding R² is 0.687 6、0.576 1、0.425 4， respectively， while the corresponding RMSE were 2.918 4 μg/cm²、3.559 5 μg/cm²、3.974 5 μg/cm²_， respectively）. （3） The inversion effect of chlorophyll content could be better when based on fusion data than Landsat 8 data and UAV data. This study coupled with multi-source remote sensing data to realize remote sensing retrieval of chlorophyll content in Phyllostachys pubesculus forest， which can provide scientific reference for dynamic monitoring of phyllostachys pubesculus forest health.