Land surface phenology, including not only the timing of phenophase transitions but also the entire seasonal cycle of surface reflectance and vegetation indices, is important for a wide range of applications including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and mapping land cover, land use, and land cover change. While methods to monitor and map phenology from coarse spatial resolution instruments such as MODIS are now relatively mature, the spatial resolution of these instruments is inadequate for many applications, especially where land use and land cover vary at scales of 10's of meters. To address this need, algorithms to map phenology at moderate spatial resolution (~30 m ground resolution) using data from Landsat have recently been developed. However, the 16-day repeat cycle of Landsat presents significant challenges for monitoring seasonal variation in land surface properties in regions where changes are rapid or where cloud cover reduces the frequency of clear-sky views. The ESA/EU Sentinel-2 satellites, which will provide moderate spatial resolution data at 5-day revisit frequency near the equator and 2-3 day revisit frequency in the mid-latitudes, will alleviate this constraint in many parts of the world. Further, by combining data from Sentinel-2 and Landsat, it should become possible to monitor large areas of the Earth's land surface at frequencies that were previously not possible. The goal of the research described in this proposal is exploit the combined observational capabilities of Landsat and Sentinel-2 to develop the algorithmic, methodological, and computational basis for moderate spatial resolution monitoring of land surface phenology. Specifically, we propose to develop algorithms that will use a combination of Landsat and Sentinel-2 data to: (1) quantify the timing and magnitude of land surface phenology events ("phenometrics") at 30-m spatial resolution, and (2) generate gap-filled time series of spectral vegetation indices that characterize the entire seasonal cycle of land surface phenology at fixed time steps. To help achieve these goals, we propose to collaborate with Prof. Lars Eklundh at Lund University in Sweden, who developed the widely used TIMESAT algorithm for estimating phenology and who is currently funded by the Swedish Space Agency to adopt TIMESAT for use with Sentinel-2. Results from this research will provide the foundation for operational production of multi-sensor land surface phenology data products at moderate spatial resolution. Further, by implementing our algorithms in TIMESAT, the proposed research will provide flexible tools that can be exploited by the user community for location-and application-specific needs.