Scaling ecosystem function: Novel approaches from MaxEnt and Multiresolution

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Upscaling measurements of ecosystem function from leaf to region to globe, and downscaling remote sensing observations and global model output to spatial scales of meters to kilometers, remain grand challenges in Earth system science. The overarching goal of this project is to improve estimates of carbon sequestration and surface-atmosphere water flux at higher spatial resolutions than have been possible in the past, namely the spatial scales at which land surface processes vary on the order of a tree or vegetation patch. In this project, satellite remote sensing data, measured carbon fluxes, and earth system models will be scaled to higher resolutions using techniques from the field of information theory. The approach will apply acknowledged mathematical approaches that have been used successfully in disciplines that range from astronomy to population ecology in a novel way to address questions of global importance: how are vegetation, carbon and water cycles linked across landscapes? High-resolution data sets from arctic and Great Plains ecosystems will be used to validate the scaling techniques and link high-resolution vegetation function to coarser-resolution satellite imagery.The results of this project can be used by scientists from a wide range of disciplines. The resultant algorithms, code and analysis programs will be made publicly available via university websites and open-source code repositories for broad dissemination. Lesson plans will be built into existing upper-level undergraduate and graduate courses. These teaching tools will likewise be made available over the web for students and practitioners. An overarching goal of this research is to improve estimates of carbon and water cycling from space.