Biomass is an important indicator of ecosystem productivity, and it has a crucial influence on the formation and development of ecosystem structure. Shrubs are of the crucial component of the ecological system, and they are of great consequence to the ecological environment. In addition, shrubs are precious biological resources in arid and semi-arid region in the mountainous area, and shrubs are considerable associated tree species in the flat terrain area of urban at the same time. Actually, shrubs can grow well under drought and cold, no matter soil is luxuriant or not, dry or wet, that makes them play a major role in water and soil conservation, as well as ecological protection and restoration. Shrubs biomass is an important manifestation of the ecosystem productivity, and it has an enormous impact on the formation and development of the ecological system structure. Also, quantitative estimation of canopy biophysical variables, especially the biomass, is very crucial in different studies such as meteorology, agriculture and ecology. Meanwhile, remote sensing is an important data source to estimate the variables in large areas, and satellite based indices have been used in many researches to estimate biomass, leaf area index, and canopy cover. Today spectral signatures have been popular used in the remote sensing of vegetation variables. However, in areas of sparse vegetation covered, reflection of soil and rock can often greatly affect sensors' response to the ground vegetation canopy reflection, especially in mountainous areas, that makes separation of vegetation signals difficult. In this paper the authors tried to extract 10 different vegetation indices, respectively based on the HJ satellite data, high accuracy DEM data from ZY-Ⅲ satellite data to estimate Vitex negundo canopy biomass in the study area in Beijing Jundu Mountain area, combined with field sampling data. A least-squares regression fitting model is presented to express quantitative relationship between vegetation indices and Vitex negundo canopy biomass in the study area in Beijing Jundu Mountain area. The authors obtained a good fitting model through the contrast analysis of the different models. Then using the optimal result model to estimate the Vitex negundo canopy biomass and map the Vitex negundo canopy biomass distributions in the study area. The results show that the multiple linear regression model created in this paper has better retrieval accuracy and predictive capability, with a very significant correlation coefficient of 0.856, and standard error 58.5 g/m², prediction standard error 98.1 g/m², and the coefficient of determination was 0.865. However, biomass regression model is subject to the limitation of the season and environmental conditions. In the different seasons, with different geographical conditions, the results are different from one another. With different sample combinations, the proposed model results changed not much, which showed that in a certain time and geographical conditions, method proposed in this paper had a stable of repeatability. Remote sensing and estimation of Vitex negundo canopy biomass in Beijing mountainous area, provide new ideas to the use of remote sensing technology in shrub community biomass monitoring, and have special meaning in the research into the evolution of ecological environment, as well as energy cycling.