The Vegetation Index method is often used in remote sensing estimation of vegetation Fraction of Absorbed Photosynthetically Active Radiation（FAPAR）

but the estimation accuracy of the Vegetation Index method is often affected by "saturation" of spectral reflectance in the"red band absorption peak." The article is aimed at developing new parameters to improve the estimation precision of FAPAR

particularly in average or high vegetation coverage. Given that the hyperspectral absorption characteristic parameters can be used to interpret the spectral absorption feature details of ground object

we develop the automatic recognition method of the characteristic absorption peakof the hyperspectral curve based on the differential and envelope removal methods to identify thecharacteristic absorption peaks sensitive to FAPAR. We extract the hyperspectral absorption characteristic parameters based on the continuum removal method and Spectral Absorption Index（SAI）. By combining the characteristic absorption peaks with hyperspectral absorption characteristic parameters

we build a hyperspectral absorption characteristic parameters model to estimate the FAPAR of natural grassland canopies. The results are listed below:（1） The hyperspectral absorption characteristic parameters have a high correlation with the FAPAR

and the SAI of the "red band absorption peak" is the most sensitive parameter to the change of FAPAR. Compared with the reflectivity of the "red band absorption peak" and Normalized Difference Vegetation Index（NDVI）

saturation in the high vegetation coverage level was significantly improved.（2） The best estimation model is the logarithmic equation

which takes the SAI of the "red band absorption peak" as a variable. Compared with the NDVI model

the prediction accuracy of FAPAR exhibits varying degrees of improvement while in the average or high vegetation coverage level. The hyperspectral absorption characteristic parameters can remedy

to some extent

the defects caused by the"saturation" problem of the Vegetation Index in estimating FAPAR

can be used as a new inversion parameter of vegetation FAPAR

and can monitor the FAPAR of natural grassland in the average or high vegetation coverage level.