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Surface reflectance can be derived from satellite measurements for the top of atmosphere and provides an important dataset for monitoring land change reliably. In this study, the Sentinel-2A surface reflectance was generated using the Sentinel-2 atmospheric correction (Sen2Cor) processor. To evaluate this dataset, surface data at 40 sites of the aerosol robotic network over North America from January 2016 to August 2017 were collected and processed. The surface reflectance reference was derived from the second simulation of the satellite signal in the solar spectrum-vector (6SV) code. The aerosol optical thickness (AOT), water vapor, surface reflectance, and three spectral indices generated by Sen2Cor were evaluated using the metrics including the accuracy, precision, and uncertainty ( A , P , and U ). The results show that due to the limitations of Sen2Cor aerosol retrieval algorithm, the Sentinel-2A AOT was significantly overestimated, with the relative accuracy over 160%. The Sen2Cor surface reflectance is generally overestimated, especially for the bright pixels, except for the cirrus band. For the 12 Sentinel-2A bands, the mean values of relative A , P , and U are 4.15%, 13.44%, and 14.92%, respectively. Among the three spectral indices, the normalized difference vegetation index performs best, with a correlation coefficient of 0.973 against the surface data. Furthermore, the Sen2Cor surface reflectance was compared with other satellite products. The mean correlation coefficient between Sentinel-2A and Landsat 8 surface reflectance is found to be 0.761. This study suggests that a better AOT retrieval is critical for improvement of Sen2Cor in the future.
Li et al. (2018) studied this question.