Abstract: The ultraviolet spectrum has significant applications in the fields of global auroral detection,marine oil spill,atmospheric glow,etc. Surface reflectance is important background data in the research. However,the existing satellite data resources are relatively insufficient to meet the application needs. In this study,a machine learning-based on XGBoost algorithm is proposed for simulating surface reflectance data in the near-ultraviolet (N-UV) (350～400 nm) spectral channel. Firstly,Sentinel-2 MSI 2,3 and 4 channels are selected as the data source and the spectral of vegetation,water,soil and other typical features are extract based on the USGS spectral database,then equivalently calculated to the corresponding channels. Secondly,the correlation analysis between the data source and the channel to be simulated is carried out. The correlation coefficients between Sentinel-2 MSI 2,3 and 4 channels and the channels to be simulated are all greater than 0.88,which indicates that the N-UV surface reflectance simulation can be carried out based on this data source. Thirdly,based on the typical spectral data set after the equivalent calculation construct XGBoost regression model to simulate the N-UV channel surface reflectance. Results indicate that the coefficient of determination (R²) of all the channel models is above 0.91,the root mean square error (RMSE) is less than 0.076,the mean absolute error percentage (MAPE) is within 20%,and the standard deviation of the above three accuracy indicators for different categories of samples is within 0.021 2,which shows that the model has high accuracy and robustness. Finally,based on the Sentinel-2 MSI 2,3 and 4 channels image data,the simulated images of surface reflectance at 355,365,375,385 and 395 nm are generated,and the images better reflect the spectral characteristics of the surface.

Key words: near-ultraviolet, surface reflectance simulation, machine learning, XGBoost