The present study is the first to attempt to apply the in situ hyperspectral data of G. uralensis canopy in visible-shortwave infrared region (Vis-SWIR) to estimate quantification of GA and LQ contents of glycyrrhiza uralensis. After first derivative preprocessing and feature bands selection by Wilks’ lambda stepwise method , partial least squares(PLS) regression with high performance liquid chromatography (HPLC) as reference was constructed to predict the value of GA and LQ contents, respectively. With the nine selected bands and PLS regression model, GA regression accuracy of R2 is 0.953, root mean square errors of calibration set (RMSEC) is 0.31, prediction accuracy R2 is 0.875 and root mean square errors of validation set (RMSEP) is 0.39; LQ regression accuracy of R2 is 0.932, RMSEC is 0.22, prediction accuracy R2 is 0.883 and RMSEP is 0.27; The results showed that our methods provided acceptable results and implied the ability of determining GA and LQ contents from remotely sensed data. It is recommended that an advanced study be conducted in field condition using airborne and/or spaceborne hyperspectral sensors.