Abstract: Abstract: Data assimilation technology, which can combine the advantages of remote sensing and crop growth models, has great potential in large-scale application of agricultural monitoring and yield forecasting. This review included 5 parts. And the first part introduced the framework of data assimilation system of crop growth model and remote sensing. The data assimilation system contained 3 basic components: dynamic model, observation data and assimilation algorithm. Taking the WOFOST model as an example, a schematic representation of assimilating remotely sensed data into a crop model was shown. The second part summarized the progress of data assimilation of crop growth model and remote sensing. The parameter optimization methods based on cost function and the sequential filtering methods based on estimation theory were two major groups of modern data assimilation strategies. The main difference between the two groups was that each subsequent observation for sequential filtering assimilation would only influence the change nature of the model from the current state; in contrast, parameter optimization methods adjusting the estimation using all of the available observations throughout the assimilation window. In general, MODIS data was the most commonly used remotely sensed data for large regional assimilation research, and data of Landsat TM, ETM+ and OLI were the major remotely sensed data used at regional scale. General models, like WOFOST, CERES, etc. were most widely used in agricultural data assimilation researches. The main object of these researches was food crops such as wheat, corn and rice. LAI (leaf area index) was most commonly used as the assimilation variable linking remote sensing and crop models. In addition, a number of studies found that time series of reflectance, vegetation index or backscattering coefficient could be directly assimilated into a coupled crop growth-radiative-transfer model to avoid the process of regional LAI retrieval. In general, yield estimation and forecast was the most important application. The third part discussed some key aspects affecting the assimilation accuracy, including 5 parts: 1) The pixel size for assimilation, which depended mainly on the specific application. However, heterogeneous, smallholder farming environments presented significant challenges for remotely sensed data assimilation for crop yield forecasting, as field size within these highly fragmented landscapes was often smaller than the pixel size of remote sensing products that were freely available. 2) The uncertainty of remote sensed parameter inversion, which needed to be quantitatively evaluated to ensure the accuracy of data assimilation. 3) The uncertainty of crop growth models, which caused by model structure, model parameters and weather driven data. 4) Data assimilation strategies and linking parameters. Two main data assimilation strategies, parameter optimization and sequential filtering methods, both had pros and cons. Therefore, more effective assimilation algorithms still needed to be developed. 5) The scale effect. Due to the variability of land cover and the complexity of the crop planting pattern in agricultural landscapes, the scale mismatch between the remotely sensed observations and the state variables of crop growth models remained a difficult challenge. The fourth part summarized the research trend of data assimilation for crop growth model and remote sensing. It included 5 directions: 1) from single assimilated parameter to multiple ones. 2) from single remotely sensed data to multiple ones, especially the combination of optical remote sensing and SAR(synthetic aperture radar). 3) from monitoring to forecasting. Based on former researches, an application framework combining data assimilation and numerical prediction was proposed. 4) from single crop growth model to multi-crop model coupling. 5) from single machine system to the high-performance parallel computing system, especially considering the recent advances in Google Earth Engine.