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Aiming at the problem of the missing shadow information of remote-sensing images existing in the karst peak and depression area, 14 relations of SGS(Sequential Gaussian simulation) for NDVI(Normal different vegetation index) of Guohua ecological experimental area were obtained in Guangxi Zhuang Autonomous Region. The simulation results were compared with ordinary Kriging. Validation results indicated that the NDVI of the study area was impacted intrinsic factors and performed strong spatial autocorrelation. With the increase of the number of simulations, the correlation coefficient between the simulation and verify values increased, and the mean absolute error (MAE) and root mean square error (RMSE) reduced. Along with the increase of simulation times, SGS simulation precision was gradually improved, and the precision of SGS was higher than that of OK (Ordinary Kriging) interpolation when simulation times were more than 50 times. Using SGS methods to predict the missing shadow information can provide a new idea and method for evaluation and ecological reconstruction of the karst rock desertification.