Leaf area index (LAI) is related to photosynthesis, transpiration and biomass accumulating processes of vegetation closely. It is one of the important parameters of corn growth monitoring, disaster stress monitoring and yield prediction, as well as an important parameter of the radiative transfer model and crop growth model. Sentinel-2satelliteis the second satellite of the Global Monitoring for Environment and Security (GMES) plan. It has high spatial and temporal resolution, and visible and near-infrared bands resolution is 10 m, so sentinel-2 satellite is an ideal data source for agricultural remote sensing applications. The PROSAIL radiative transfer model is an effective way to retrievecorn canopy LAI using remote sensing images. However, there are some problems for LAI retrieval currently, including uncertainty of model inputs, difficulty in parameter adjustment, ill-posed inversion and low speed etc. Model inputs calibration can be used to acquire the exact value of model inputs in the uncertainty range of the observed reflectivity. Rich and accurate parameter information is provided to reduce the errors in parameter retrieval. In this paper, a sentinel-2A satellite image was used as the data source, and Markov Chain Monte Carlo (MCMC) method was used to calibrate model inputs. The spectral reflectance uncertainty of 5% was added to obtain the posterior value probability distribution of each parameter, to optimize the parameter setting in the retrieval process and improve the accuracy of LAI retrieval. The results showed that: (1) The sensitive model inputs of the PROSAIL model were LAI, chlorophyll content of leaves and leaf structure coefficient within visible and near-infrared bands. Taking these three parameters as variables in the look-up table could effectively retrieve LAI, and the determination coefficient of retrieval accuracy reached 0.7. (2) MCMC method could be used to calibrate the PROSAIL model input and acquire each model input distribution in the study area. The posterior parameter distribution was close to the actual situation, indicating the feasibility and effectiveness of using the MCMC method for parameter calibration. (3) Input calibration could effectively improve the LAI retrieving accuracy, especially in reducing retrieval deviation and outliers. After inputs calibration, the average error of LAI retrieval decreased from 20% to 8%, while the estimation accuracy increased from 76% to 90%. These results showed that the model inputs calibration of the PROSAIL model by MCMC could improve the LAI retrieval accuracy and reduce the retrieval deviation. It provided a reference for improving the retrieval accuracy of crop canopy parameters by the PROSAIL radiative transfer model.