Landsat 30m resolution observations provide sufficient spatial details for monitoring land surface and changes. However, the 16-day revisit cycle and cloud contamination have limited its use for studying global biophysical processes, which evolve rapidly during the growing season. Meanwhile, MODIS sensors aboard the NASA EOS Terra and Aqua satellites provide daily global observations valuable for capturing rapid surface changes. However, the spatial resolution of 250m to 1000m may not good enough for heterogeneous areas. To better utilize Landsat and MODIS data, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed (Gao et al., 2006). The STARFM algorithm uses spatial information from fine-resolution Landsat imagery and temporal information from coarse-resolution MODIS imagery to produce estimates of surface reflectance that are high resolution in both space and time. In essence, the collection of daily MODIS imagery and seasonal Landsat imagery allows the generation of synthetic daily Landsat-like views of the Earth’s surface. The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.