Crop condition and natural vegetation monitoring require high resolution remote sensing imagery in both time and space - a requirement that cannot currently be satisfied by any single Earth observing sensor in isolation. The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery from coarse resolution sensors such as MODIS are typically superior to finer resolution data in terms of their revisit frequency, they lack spatial detail to capture surface features for many applications. The Landsat satellite series provides medium spatial resolution (30m) imagery which is well suited to capturing surface details, but a long revisit cycle (16-day) has limited its use in describing daily surface changes. Data fusion approaches provide an alternative way to utilize observations from multiple sensors so that the fused results can provide higher value than can an individual sensor alone. In this paper, we review the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and two extended data fusion models (STAARCH and ESTARFM) that have been used to fuse MODIS and Landsat data. The fused MODISLandsat results inherit the spatial details of Landsat (30 m) and the temporal revisit frequency of MODIS (daily). The theoretical basis of the fusion approach is described and recent applications are presented. While these approaches can produce imagery with high spatiotemporal resolution, they still rely on the availability of actual satellite images and the quality of ingested remote sensing products. As a result, data fusion is useful for bridging gaps between medium resolution image acquisitions, but cannot replace actual satellite missions.