Lawn color is the most obvious indicator of ornamental lawn value. It is of great significance to explore the relationship between chlorophyll content of turfgrass and the hyperspectral reflectance. This relationship can be used to develop models to calculate the chlorophyll content for lawn quality evaluation purpose. In this study, three common lawn grass species—Festuca arundinacea CV. Hongxiang, Lolium perenne CV. Bailingniao and Poa pratensis CV. Kentucky was cultivated in pots. Measurements of chlorophyll content and hyperspectral reflectance were made during active growth period by tys-a3500 chlorophyll meter and SOC710VP imaging spectrometer to determine the relative chlorophyll content (SPAD) and spectral data of turf grass canopy, respectively. Person correlation analysis for each of the SPAD, 1/SPAD and log(1/SPAD) was conducted with a group of variables including vegetation index—10 G (green vegetation index), ARVI (atmospheric impedance difference vegetation index), VARI (visual pressure impedance index), NDVI705 normalized difference vegetation index (705), MSR705 red edge ratio vegetation index (improved), NDVI670 normalized difference vegetation index (670), CI (chlorophyll index), PSRI attenuation (vegetation index), RGI (relatively green index) and EVI ( Enhance the correlation of vegetation index). After screening the hyperspectral bands of vegetation index with the highest correlation with chlorophyll content, models were developed using the vegetation index based on these bands. After the best model was selecting through an accuracy test, the model was used to estimate the chlorophyll SPAD values change for turf grasses under different concentrations of heavy metals Pb2+ stress. The results are summarized as follows: (1) the overall trends of spectral curves of different turfgrass were not significantly different, but the reflectance (REF) of different species were different. At the band of 730~1 000 nm, there was no significant difference between “lark” perennial ryegrass and “red elephant” tallfestia REF, but the spectral characteristics of “Kentucky bluegrass” were unique with a higher REF. (2) among the 10 vegetation indexes, VARI, RGI and PSRI were extremely significantly correlated with 3 chlorophyll indexes of turfgrass, and the absolute value of correlation coefficient R2 was all greater than 0.65, indicating that it is feasible to estimate the chlorophyll content of turfgrass with these 3 vegetation indexes. (3) Stepwise regression analysis of vegetation index and chlorophyll index shows that in the single-factor regression model, the model determination coefficient (R2) of estimating 1/SPAD using vegetation index VARI, RGI and PSRI was above 0.626, which was generally higher than the estimation of SPAD and log(1/SPAD). In multiple linear regression, the model determination coefficient (R2) constructed by 10 vegetation indexes and chlorophyll index 1/SPAD was also the highest (0.817), showing that SPAD reciprocal form is applicable to be used the in model estimation of chlorophyll in turfgrass. (4) The best model selected from the models with a high determination coefficient (>0.7) through accuracy test was y1/SPAD=0.161xRGI+0.007xGI-0.054 (R2=0.817, RMSE=0.023).