Spatial and temporal fusion of remote sensing data is a technology that can generate dense time series and high spatial resolution data whose spatial resolution is similar with high spatial resolution date, and temporal resolution is the same as the one with high temporal resolution data. This paper presented a new spatial and temporal data fusion model (STDFM) for blending Landsat and MODIS surface reflectance. Temporal change information was detected from sequence coarser resolution surface images, and new high resolution reflectance was predicted from former high resolution reflectance. This algorithm was tested in red and near-infrared MODIS and Landsat ETM+ images, and over a study area in Jiangning country, Nanjing, Jiangsu, China. Results showed that STDFM was able to produce images very similar with actual observed images. The correlation coefficient r between synthetic imageries and actual observations was 0.939. The correlation coefficient r between NDVI calculated by synthetic imageries and actual observations was 0.938.