Abstract: Abstract: Combining the crop growth models with the remote sensing parameters, is important to realize the applications in the large spatial scale for the former, and also to improve the rationality and accuracy of inversion theory for the latter. Some research fields, such as the ecology, agriculture, resource investigation and global climate change, would use the data derived from the combination form. The overview includes 3 parts, i.e. the international crop growth model, the quantitative remote sensing parameters and the parametric methods. From the view of the spatial expansion for the application of the crop growth model, the development duration of crop growth models was divided into 3 stages: The construction of the mechanism models, the application in the point scale, and the application in the regional and global scale. In order to understand the situation and foundation of the inter-discipline combination from the crop growth simulation and the quantitative remote sensing, the paper describes 3 important contents. The first is overviewing the main simulation processes and the input and output parameters for the typical crop growth models. The second is summarizing the remote sensing inversion parameters which can be used as the initialization and driving data for the application of crop growth simulation models in the regional and global scale, establishing the corresponding relation between the simulation process of crop growth model and the parameters from the quantitative remote sensing. And the third is comparing 3 kinds of combination methods between the crop growth model and the parameters derived from the quantitative remote sensing, and emphasizing the differences, advantages and disadvantages for the 3 combination methods. Based on the contents mentioned above, 3 topics for discussion are proposed. The first topic is the application limitations of crop growth models in the large spatial scale and its relationship with quantitative remote sensing parameters. The second one is the influence of the scale effect from the input parameters when the crop growth model is used to simulate the crop growth in the larger region. And the third one is to discuss the development direction of combination methods. It is hopeful to provide a kind of thinking for combining the crop growth models with the parameters from the quantitative remote sensing through the overview, summary and discussion. And it is clearly concluded that the data from the quantitative remote sensing can provide initialization data for crop growth models to some extent in the regional and global scale, and the application in a large space scale is the direction of crop growth model. The conclusion shows further that it is important to pay attention to the scale problem of the model parameters, and that the data dis-matching for the same parameter from the crop growth and remote sensing can result in the huge error of estimation on the output data due to the difference of physical meaning from the 2 disciplines. Understanding that the data from the quantitative remote sensing could enhance the ability of simulating the crop conditions and yield at the large scale was also helpful to understand that the remote sensing had the ability of deriving the biochemical and biophysical information from the ground surface exactly. And furthermore, it is expected that the correct combination parameters should be chosen to deduce the propagation of error and uncertainty, the assimilation methods would still preserve the mainstream style for the combination, and following the increasing accuracy of data from the remote sensing and crop model, the fusion model for the model of crop growth and remote sensing can be constructed to play a greater application value in the environmental monitoring and agricultural production.