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In this study, an arid grassland was selected, and the chlorophyll content of the leaf and canopy level was estimated based on Landsat-8 Operational Land Imager (OLI) data using the PROSAIL radiative transfer (RT) model. Two vegetation indices (green chlorophyll index, CIgreen, and greenness index, G) were selected to estimate the leaf and canopy chlorophyll content (LCC and CCC). By analysing the effect of soil background on the two indices, the LCC was divided into low and moderate-to-high levels. A different combination of the two indices was adopted at each level to improve the chlorophyll content estimation accuracy. The results suggested that the chlorophyll content estimated using the proposed method yielded a higher accuracy with coefficient of determination, R-2=0.84, root-mean-square error, RMSE=9.67 g cm(-2) for LCC and R-2=0.85, RMSE=0.43g m(-2) for CCC than that using CIgreen alone with R-2=0.62, RMSE=20.04 g cm(-2) for LCC and R-2=0.85, RMSE=0.71g m(-2) for CCC. The results also confirmed the validity of this approach to estimate the chlorophyll content in arid areas.