B14B-01
Reducing the uncertainty in the projection of the terrestrial carbon cycle by fusing models with remote sensing data

Monday, 14 December 2015: 16:00
2004 (Moscone West)
Shawn Serbin1,2, Alexey N Shiklomanov3, Toni Viskari3,4, Ankur R Desai5, Philip A Townsend6 and Michael Dietze3, (1)Stony Brook University, Ecology and Evolution, Stony Brook, NY, United States, (2)Brookhaven National Laboratory, Biological, Environmental & Climate Sciences, Upton, NY, United States, (3)Boston University, Boston, MA, United States, (4)Brookhaven National Laboratory, Upton, NY, United States, (5)University of Wisconsin Madison, Madison, WI, United States, (6)University of Wisconsin, Madison, WI, United States
Abstract:
Modeling global change requires accurate representation of terrestrial carbon (C), energy and water fluxes. In particular, capturing the properties of vegetation canopies that describe the radiation regime are a key focus for global change research because the properties related to radiation utilization and penetration within plant canopies provide an important constraint on terrestrial ecosystem productivity, as well as the fluxes of water and energy from vegetation to the atmosphere. As such, optical remote sensing observations present an important, and as yet relatively untapped, source of observations that can be used to inform modeling activities. In particular, high-spectral resolution optical data at the leaf and canopy scales offers the potential for an important and direct data constraint on the parameterization and structure of the radiative transfer model (RTM) scheme within ecosystem models across diverse vegetation types, disturbance and management histories. In this presentation we highlight ongoing work to integrate optical remote sensing observations, specifically leaf and imaging spectroscopy (IS) data across a range of forest ecosystems, into complex ecosystem process models within an efficient computational assimilation framework as a means to improve the description of canopy optical properties, vegetation composition, and modeled radiation balance. Our work leverages the Predictive Ecosystem Analyzer (PEcAn; http://www.pecanproject.org/) ecoinformatics toolbox together with a RTM module designed for efficient assimilation of leaf and IS observations to inform vegetation optical properties as well as associated plant traits. Ultimately, an improved understanding of the radiation balance of ecosystems will provide a better constraint on model projections of energy balance, vegetation composition, and carbon pools and fluxes thus allowing for a better diagnosis of the vulnerability of terrestrial ecosystems in response to global change.