Landsat imagery with a 30 m spatial resolution is well suited for characterizing mine-area-scale landscape structure and dynamics.While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties,the Landsat sensors revisit rate at 16 days is too long for its use for dynamic monitoring,especially considering the frequently cloud contamination.By contrast,MODIS has a high temporal resolution,covering the Earth up to multiple times per day,but MODIS data have spatial resolutions of 250 m,500 m,and 1000 m.By combining Landsat and MODIS data,we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS cquisitions,which is of great importance in coal area monitoring.In this research,we apply anddemonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model,STARFM) at Datong Coalfield,China.Based on a pair of ETM and MODIS images at the same day of 2002/09/25,we predicted the ETM images at the days of 2002/06/05,2002/10/11 and 2002/11/12 by employing the corresponding MODIS images.We compared,on a channel-by-channel basis,the surface reflectance values of the three real (observed) ETM images with the corresponding synthetic (predicted) imagery,and found that spatial details between them was of highly consistence and that vegetation phenomenon was well captured.Scatter plots between the observed and predicted images were closest to the 1:1 line,and the linear regression slopes were between 0.993 and 1.295 and R2 were between 0.739 and 0.908,indicating that they were highly correlated with each other.Reflectance values between them also showed no significant difference with the average differences falling into -0.0110~0.0009,absolute differences 0.0164~0.0369 and relative differences 0.4％~18.1%.