Dense time series of imagery from Landsat 8 and Sentinel 2 are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager (OLI) and the MultiSpectral Instrument (MSI) onboard Sentinel 2A and 2B, users will have access to moderate spatial resolution imagery with repeat frequencies that are more than three times higher than those available prior to the launch of Sentinel 2A. At the same time, the large data volumes and highdimensionality of blended time series from Landsat 8 and Sentinel 2 introduce substantial new challenges for users who wish to exploit these data sets. Land surface phenology (LSP) products, which synthesize the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide a simple and intuitive way to reduce data volumes and redundancy, while also furnishing rich feature sets that are useful to 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 land cover, land use, and land cover change mapping. Methods to monitor and map phenology from coarse spatial resolution instruments such as MODIS are mature and operational. However, the spatial resolution of MODIS is inadequate for many applications. The goal of this proposal is to create an operational land surface phenology product based on blended time series of Landsat 8 OLI and Sentinel 2A and 2B MSI data. To explain the motivation and methodological basis for our approach, the proposal includes four main elements. First, we summarize the empirical basis and justification for our proposed product. Second, we provide a formal definition for our proposed land surface phenology product, which includes a set of Science Data Sets that: (1) identify the timing of phenophase transitions, (2) provide the user community with reduced dimensionality image data sets that capture the primary modes of multispectral and multi-temporal variability and minimize temporal correlation in image time series, and (3) identify inseason anomalies in near real-time. In this way, our proposed product goes well beyond what current coarse spatial resolution LSP products provide, and is designed to support a wide and diverse user community. Third, we describe the algorithm that will be used for this effort, which has been developed and tested over the last several years, along with the input data requirements required to generate our proposed product. Fourth, we present results from our algorithm applied to blended time series of Landsat 8 and Sentinel 2A data that demonstrate the effectiveness and accuracy of our algorithm, along with a strategy for operational product validation. For initial implementation, we propose to generate our product at continental scale for North America at 30-meter spatial resolution using the Harmonized Landsat-Sentinel (HLS) data set that is being generated by NASA, and to distribute our results via the Land Processes DAAC. To support this effort, we will collaborate with Prof. Lars Eklundh at Lund University in Sweden, one of the pioneers of land surface phenology, who is funded in Europe to develop land surface phenology algorithms based on Sentinel-2, and with whom we have an ongoing and successful collaboration.