The Landsat satellites have been providing earth observation data continuously since early 1970s, and form a cornerstone for mid-resolution remote sensing. Continuity of Landsat-like data is a critical need within the Earth Science community. However, the failure of the Scan-Line Corrector (SLC) mechanism on Landsat 7 in 2003 and increasing age of Landsat 5 have threatened this continuity. While a new Landsat Data Continuity Mission (LDCM) satellite will begin operation in 2011, the difficulties in maintaining Landsat continuity have highlighted the need to combine the capabilities of existing international sensors to provide a more robust observational record. Combining different mid-resolution sensors can also provide more frequent observations throughout the growing season for monitoring rapid vegetation phenological changes. In this presentation, I will present our recently developed general empirical relation model (GERM) that uses MODIS surface reflectance as reference data set to normalize multiple mid-resolution sensor data to a consistent data stream. I will also present the Spatial and Temporal Adaptive Reflectance Fusion Model (StarFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications which require high resolution in both time and space. The MODIS daily 500m surface reflectance and the 16-day repeat cycle Landsat ETM+ 30m surface reflectance are used to produce a synthetic “daily” surface reflectance product at ETM+ spatial resolution.