Region were applied to simulate the satellite spectral reflectance of domestic highresolution satellite GF-1,〖JP〗 and then eighteen broad vegetation indices which were sensitive to the chlorophyll content were obtained based on the simulation reflectance. The relationships between SPAD values and eighteen vegetation indices were analyzed at different growth stages of winter wheat, and the most related vegetation indices were selected to construct the remote sensing monitoring model of SPAD value for leaf by regression analysis. Finally, the models for wheat greenup stage were used to estimate the SPAD value for winter wheat leaf through GF-1 satellite data. The results showed that the SPAD values were highly related with the TGI index in greenup, booting and whole growth periods. The correlation coefficients were -0.742, -0.740 and -0.483, respectively. The SPAD values were significantly related with SIPI and GNDVI indices in jointing and grain filling stage, and the correlation coefficients reached to 0.788 and 0.745, respectively. The GNDVI, GRVI and TGI indices kept a good relationship with leaf SPAD values in each growth period at the 0.01 probability level. All the regression models proposed by GNDVI, GRVI and TGI indices performed well, especially the RandomForest regression model (SPAD-RFR). The best prediction results appeared at the jointing stage of winter wheat. It concluded that SPAD-RFR regression model based on the GF-1 satellite imagery data could effectively monitor the SPAD value for winter wheat leaf in the study area.