The physiological and biochemical properties of ramie are the result of comprehensive influence of genetic basis and environmental conditions, which can reflect ramie growth under specific stress environment. Therefore, a fast, accurate and inexpensive method is needed to monitor the dynamic changes of ramie physicochemical property during the whole growth cycle. Unmanned aerial vehicle (UAV) remote sensing technology provides an effective means for monitoring crop growth in large field, which has been widely concerned and applied by virtue of its advantages of fast, non-destructive, timely and accurate. However, at present, there are few researches on the comprehensive evaluation of ramie physicochemical property by using UAV multi-spectral images. The UAV was equipped with a multi-spectral camera to acquire the multi-temporal canopy images of ramie. Then, the canopy orthophoto image was obtained by DJI terra, and the spectral and texture characteristic values of ramie plants were further extracted. Pearson correlation analysis (PCA) and recursive feature elimination (RFE) were used to screen the sensitive eigenvalues. Finally, based on multi-temporal remote sensing data, linear regression (LR), random forest regression (RF), support vector machines (SVM), partial least squares regression analysis (PLSR) and decision tree (DT) were used to estimate ramie physicochemical property, respectively. The results showed that there was a significant correlation between the ramie physicochemical property and spectral skewness parameters. Both PCA and RFE can improve the accuracy of the estimation model, but RFE had better performance. The accuracy of the LR-SAPD estimation model was 0.662. The R2 and RMSE of LR-RWC estimation model were 0.793 and 2.213%, respectively. The SVR-LAI model could better estimate ramie LAI (R2=0.737, RMSE was 0.630). In conclusion, an accurate, efficient, cost-effective and universal dynamic monitoring method for physicochemical property of field ramie was proposed.