Vegetation nitrogen content is an important indicator of vegetation growth, which plays an important role in fields of ecosystem monitoring including ecosystem health, net primary ecosystem, the disturbance of ecosystem, as well as in the precision agriculture management.Remote sensing inversion of vegetation nitrogen content currently relies on the hyperspectral/multispectral data. The inversion methods can be categorized into that based onvegetation indices, regression analysis (e.g. partial least squares regression) and radiation transfer models separately. Current satellite-based inversion of vegetation nitrogen content is limited to a small area, uniformed in species of vegetation and the environmental condition (e.g. climatecondition, topography et al). As a result, the inversion works poor for complex ecosystems. In order to meet the requirements of increasingly meaningful research projects such as global nitrogen deposition and the response of ecosystems to human activities, current methods of vegetation nitrogen content inversion need further development. It may be a potential solution to carry out research on the standardization of vegetation spectrum of different types of plants, as well as under different environmental conditions to generate more general or even universal inversion methods of vegetation nitrogen content. On the other hand, comprehensive utilization of multiple data from various sources (e.g. microwave remote sensing and unmanned aerial vehicle remote sensing data) will be an alternative solution to multi-scale simultaneous monitoring, which helps to improve remote sensing's routine monitoring capability of regional and worldwide vegetation nitrogen content.