Chlorophyll content is an important indicator of crop health, plant productivity, and environmental stress. Real-time, fast and accurate acquisition of leaf chlorophyll content of crops is of significant for monitoring crop growth. Remote sensing is an effective way to retrieve leaf chlorophyll content of crops at regional and global scales. However, previous studies retrieving leaf chlorophyll content of crops does not fully consider the impact of underlying surface background, limiting retrieval accuracy. To this end, this paper aims at the inversion of rice leaf chlorophyll content from Sentinel-2 remote sensing satellite data using a look-up table based approach. The look-up table was simulated using the PRAOSAIL radiation transfer model. The applicability of chlorophyll indices (CI) calculated from the reflectance of the green band and different red-edge bands and the spectral index (Zarco and Miller, ZM) constructed by two different red edge bands in inverting leaf chlorophyll content was evaluated using field measurements. The greenness index (G) was integrated with CI and ZM to constrain the impact of background on the inversion of leaf chlorophyll content. The main findings of this study are: (1) The accuracy of leaf element content inversion based on the spectral index constructed in different bands is different, and CI₇₄₀ performed the best (R²=0.79, RMSE=9.02 μg·cm^-2), followed by ZM (R²=0.71, RMSE=10.53 μg·cm^-2), CI₇₀₅(R²=0.69, RMSE=9.17 μg·cm^-2), and CI₇₈₃(R²=0.67, RMSE=10.84 μg·cm^-2); (2) The inverted leaf chlorophyll content is significantly affected by the background, especially at the early stage of rice growth. The inverted leaf chlorophyll content was systematically lower than observations (mean relative error (MRE) in the range from -18.87% to -31.94%) owing to strong background interference; (3) CI/G and ZM/G can effectively eliminate the influence of background and improve the accuracy of rice leaf chlorophyll inversion. At the early stage of rice growth, inversion based on CI/G and ZM/G significantly improves agreement between inverted and observed leaf chlorophyll content (MRE in the range from 8.11% to 18.11%). These findings are of great significance for improving the inversion of leaf chlorophyll content under different leaf area index levels of rice from remote sensing data.