Abstract: Red edge parameters are widely used to inv ersely deduce crop parameters in quantitative remote sensing studies. Among them, the red edge position, as a very sensitive indicator for monitoring crop stress, is strongly correlated with crop biochemical components. Accurate estimation of the chlorophyll content of vegetation is of importance for studies on forest health, stress, and productivity estimation, as well as carbon cycle. In this article, several algorithms of red edge position were introduced firstly, their applications were compared, and the leaf chlorophyll content of vegetation was estimated through selecting its different sensitive bands. Then leaf spectral data from indoor spectra were extracted, four algorithms were used (first -order derivative spectrometry, first -order derivative spectrometry after smoothing, four point interpolation, and quintic polynomial fitting) to process spectral data and obtain red edge position variables. Finally the obtained variables were used to fit the chlorophyll content, and various regression models of these algorithms for estimating leaf chlorophyll content were established. The results show that all these established models are feasible to estimate chlorophyll content. Among them, the quintic polynomial fitting method is most accurate, but highly complex in obtaining the red edge position while the four point linear interpolation is next to it in accuracy, but simpler.