Advancing Data-Centric Technologies for Better Understanding of Land Surface Phenology
Presenting Author: Thomas Maiersperger
Co-Author(s): Jeffery Morisette, Andrew Richardson, Luke Zachmann, Katharine Gerst, Colin Talbert, David Theobald, Theresa Crimmins, Jake Weltzin, Brian Miller, Katharyn Duffy, Aaron Friesz, Lee Marsh, Kyle Enns, Eric Stofferahn, Justin Koch, Rob Quenzer
In many ecological endeavors, the full benefit of observations taken from across a range of spatial scales is only realized through meaningful integration. Integration of point (tower), plot, and satellite data sets is challenged by inconsistent formats, observation protocols or variables, and differences in temporal resolution (and duration), and spatial resolution (and extent). Now, through this project, there is an opportunity to integrate a diverse set of phenology data streams in a way that is robust and accessible to the wider scientific community. Key technological advancements have made this possible, and have been achieved through three driving use cases: 1) testing the predictive capacity of gridded derived phenology products, 2) improving access to multi-scale phenology data to facilitate the validation of satellite-based phenology products, and 3) applying continuous integration services to obtain real-time information and forecast phenological parameters in space and time. This poster summarizes key results to date for each use case.