Abstract: With China Remote Sensing career advancement, a large number of independent researches and development of satellite have launched. Among a new generation of high-resolution satellites, GF-1 stands out. It sets high spatial resolution, multi-spectral and high temporal resolution in a fusion technology with strategic significance. To explore Chinese GF-1 satellite images' adaptability of agricultural growth monitoring, its images for the region of Xuchange China for maize growth were compared with the same period of Landsat-8 satellite images in three aspects of sensor spectral response characteristics, the accuracy of empirical regression model and LAI space consistency. There were a total of 24 sampling points for the study. First, graphs described the sample located pixels' spectral reflectance of near-infrared band, red band, green band and blue band of the two types of sensors. It directly reflected the spectral reflectance differences between sensors in the same place, and differences between maize in different area. The reflectance of near-infrared and red band of Landsat-8 was higher compared with GF-1. The blue and green band's reflectance of GF-1 was similar to that of Landsat-8. The linear correlation of two sensors' reflectivity could be calculated at the same time. Second, four bands of two types of images were separately combined into seven kinds of normalized difference vegetation index to further eliminate the influence of atmospheric correction process. Like NDVI, the red band was replaced by blue or green or three visible bands' combination of two by two or sum of them. Then, the empirical regression models were used to calculate the ability of inversing LAI among the vegetation index. Based on comparison of R2 and RMSE among models, high fitting models were selected. The optimal model for Landsat-8 was based on BRNDVI, it was an index model. The best model for GF-1was based on NDVI, and model type was an index model. The reserved samples were used to test model's fitting accuracy. The final result showed a good correlation between inversed LAI and measured LAI for all images. Third, LAI distribution of Xuchang district was reversed by the optimal model of two images, due to the variance in spatial resolution, GF-1 data did downscale process by resampling to 30 m scale. In total, maize LAI spatial distribution in two images was more consistent, and had a west to east high transition trend. For further research, the range for LAI unified into <3.0, ≥3.0-4.0, ≥4.0-5.0, ≥5.0 pixels is needed in a visual display. High values greater than 5.0 were concentrated in Xuchang county, Yanling county and the eastern half of Changge city, the two distributions were more consistent; <3.0 pixels were rarely low in both. There were difference in the distribution of Yuzhou, western Changge city and Xiangcheng County, ≥4.0-5.0 range had a wider distribution in Landsat-8 product of LAI, and ≥3.0-4.0 pixels were more in GF-1 LAI product. In this paper, the application indicated that GF-1 satellite's high time resolution provides more chances to get cloudless data, and high spatial and spectral resolution features and it can replace the traditional medium resolution remote sensing of agricultural growth monitoring data to a certain extent. This research shows that GF-1is an important data source and the data's application in other areas of agriculture is the focus of future research.