Leaf chlorophyll content is one of the most important biochemical variables for crop physiological status assessment, crop biomass estimation and crop yield prediction in precision agriculture. Vegetation indices were considered effective for chlorophyll content estimation. Although hyperspectral reflectance is proven to be better than multispectral reflectance for leaf chlorophyll content retrieval, the scarcity of available data from satellite hyperspectral sensors limited its application. It is highly desirable to develop methods for leaf chlorophyll content estimation based on broadband satellite data. In this study, nine broad band vegetation indices were tested for their potential for leaf chlorophyll content estimation. The PROSAIL model was used for sensitivity analysis of the selected vegetation indices. The results of the sensitivity analysis showed that both the chlorophyll vegetation index (CVI) and the triangular greenness index (TGI) had better performance in leaf chlorophyll content estimation. Both CVI and TGI were less sensitive to leaf area index (LAI) and more sensitive to leaf chlorophyll content than the other vegetation indices. Validation based on field measurements showed that CVI (R²=0.50, P<0.001) and TGI (R²=0.46, P<0.001) were the most appropriate indices for leaf chlorophyll content estimation. These results demonstrate the possibilities for retrieving leaf chlorophyll content using broadband satellite data in precision agriculture. The preliminary results of this study also shed light on future improvement of vegetation indices for leaf chlorophyll content estimation.