Abstract: Abstract: Crop yield monitoring and forecast has great impact on food security, ecological environment, and farmers' incomes. Crop growth and yield monitoring and forecast by using crop growth model has the advantages of clear mechanism, high precision, and high monitoring frequency, but its monitoring scale is usually limited to land block level. Along with the fast development of remote sensing satellite technology, using remote sensing data combined with crop growth model to accurately monitor regional crop growth and yield in a large regional scale has gradually become an important means of regional level and even national level crop growth monitoring. However, due to differences in crop types, climatic conditions, soil conditions and monitoring areas, the regionalization and localization of crop growth model is the major bottleneck of crop growth monitoring by using crop growth model combined with remote sensing data, and it is urgent to conduct targeted studies on the identification of assimilation parameters of crop growth model, pre-processing of meteorological data, and the setting of crop parameters. Based on soil-water-atmosphere-plant model (SWAP), and by taking the major commodity grain production base of China, Northeast China Region as a study region, in this the paper, we conducted a study by taking the major crop of spring maize of the region as its target crop. Firstly, we used Landsat to obtain maize (Zea mays) planting area in the study area, and used it as the basic data for estimating the total maize yield in the study area. The overall accuracy of maize area classification was 93.2%, with R2 of 0.951 2. By considering the influence of latitude and altitude on temperature, in the study, we used the coKriging method in crop growth model meteorological data interpolation acquisition, so as to improve the precision of input parameters of the model. The result showed that the average standard error of minimum temperature of coKriging method was 0.31 ℃, while that of the Kriging method was 1.51 ℃. The average standard error of maximum temperature of coKriging method was 0.30 ℃, while that of the Kriging method was 1.14 ℃. In the study, leaf area index (LAI) and evapotranspiration (ET) were used as assimilative remote sensing data sources, and we proposed a novel method to adjust the LAI product of MODIS to make it closer to actual value. By optimizing maize irrigation and crop emergence date, we obtained spatial distribution result of maize yield of the study area of 2013. The monitoring result was compared with the statistical data. The R2 reached 0.939 4, with RMSE of 148 065 t, and MAE of 114 335 t. Moreover, the correlation coefficient of predicted yield and statistical yield reached 0.724 5, with RMSE of 598.5 kg/hm2, and MAE of 531.5 kg/ hm2。The study result showed that, using SWAP model, taking meteorological data spatial interpolation results obtained by using coKriging method as input data and assimilation of LAI and ET remote sensing, can effectively conduct corn yield remote sensing monitoring of the study region, which provided reference for the remote sensing monitoring and forecast of crop growth and productivity of the region.