Characterizing growth cycle events in vegetation, such as the timing of spring green-up, from massive spatiotemporal remote sensing datasets is desirable for a wide area of applications. For example, the timings of plant life cycle events are very sensitive to weather conditions, and are often used to assess the impacts of changes in weather and climate. Likewise, quantifying and predicting changes in crop greenness can have a large impact on agricultural strategies. However, due to the limitations of imaging spectrometers, a sensor with high temporal frequency of measurements must have lower spatial resolution, and vice versa. We develop a space-time dynamic linear modeling framework to fuse high temporal frequency data (MODIS) with high spatial resolution data (Landsat) to create daily, 30 meter resolution data products of vegetation greenness. Our method is able to handle the spatial change-of-support problem, as well as the high percentage of missing values in the data. Additionally, we introduce an approximate, computationally efficient procedure for parameter estimation which is scalable to the massive size of the data.