Chlorophyll is a crucial indicator for assessing grasslands’ photosynthetic capacity and physiological condition.With its rich spectral information, hyperspectral remote sensing has become an important means for non-invasively estimating chlorophyll content in grasslands. However, there is a scale mismatch between the canopy hyperspectral data and the measured leaf chlorophyll values, leading to hyperspectral chlorophyll retrieval’s low accuracy. Therefore, this paper proposes a hyperspectral retrieval method for natural grassland Canopy Chlorophyll based on the green cover rate. The typical natural grassland in Hulunbuir, Inner Mongolia, was selected as the research object. The measured leaf chlorophyll relative content values were obtained by ASD hyperspectral spectrometer, SPAD chlorophyll meter, and mobile phone digital photos.The results indicate that the correlation between vegetation indices and SPAD ranges from -0.74 to 0.76, which is generally higher than the average correlation of SPAD pushed up from -0.63 to 0.50. Green cover media pushed the measured values of leaf chlorophyll relative content to the sample canopy scale. First derivative spectra and 42 common chlorophyll spectral indices were used to construct a hyperspectral retrieval model (SPAD) of natural grassland Canopy Chlorophyll based on green cover rate_ cover. The single variable optimal grassland Canopy Chlorophyll retrieval modelR²=0.689, RMSE=2.714, RPD=1.752; The best regression model of grassland Canopy Chlorophyll wasR²=0.833, RMSE=2.019, RPD=2.354. The results show that the hyperspectral retrieval accuracy of chlorophyll content in natural grassland canopy can be effectively improved by extrapolating the measured value of chlorophyll content in grassland leaves to the canopy scale based on the green cover rate.