Chlorophyll content (SPAD) is a vital index for crop growth evaluation, which can monitor the growth of crops and is crucial for agricultural management, so it is important to estimate SPAD quickly and accurately. In this study, the remote sensing images of the jointing, flagging, and flowering stages were acquired using UAV hyperspectral for winter wheat. The vegetation indices and red edge parameters were extracted to explore the ability of vegetation indices and red edge parameters to estimate SPAD. Firstly, the vegetation indices and red edge parameters were correlated with the SPAD of different fertility stages. Then, the SPAD was estimated based on the vegetation indices, vegetation indices combined with red edge parameters , and using partial least square regression (PLSR) method. Finally, the SPAD distribution map was produced to verify the validity of the model. The results showed that (1) most of the vegetation indices and red edge parameters were correlated with SPAD at highly significant levels (0.01 significant) in all three major reproductive stages; (2) the SPAD estimation model constructed from individual vegetation index had the best performance for LCI among vegetation indexes (best R²=0.56, RMSE=2.96, NRMSE=8.14%) and Dr/Dr_min performed best (best R²=0.49, RMSE=3.18, NRMSE=8.76%); (3) SPAD estimation model based on vegetation indices combined with red edge parameters was the best and better than SPAD estimation model based on vegetation indices only. Meanwhile, both models reached the highest accuracy at the flowering stage as the fertility stage progressed, with R² of 0.73 and 0.78, RMSE of 2.49 and 2.22, and NRMSE of 5.57% and 4.95%, respectively. Therefore, based on the vegetation indices combined with the red edge parameters, using the PLSR method can improve the estimation effect of SPAD, which can provide a new method for SPAD monitoring based on UAV remote sensing, and also provide a reference for agricultural management.