Estimating Global Terrestrial Carbon Fluxes from Satellite-Derived Biometric Data.
Bloom, A. Anthony; Williams, Mathew
University of Edinburgh, UNITED KINGDOM
Large uncertainties are associated with terrestrial net ecosystem exchange (NEE) estimates across continental scales. The assimilation of satellite-derived biometric data into carbon (C) cycle models can lead to an improved understanding of ecosystem C fluxes and ultimately to a reduction of estimated NEE uncertainty. We use a Monte Carlo model-data-fusion approach to assimilate a multitude of biometric data-streams, including Moderate Resolution Imaging Spectroradiometer (MODIS) derived Leaf Area Index (LAI), Advanced Land Observing Satellite (ALOS) derived above-ground biomass (AGB), plant-trait data, and Harmonized World Soil Database (HWSD) soil C estimates into the Data Assimilation Linked Ecosystem Carbon (DALEC) Model. In addition, we developed a series of ecological and dynamical constraints on DALEC model parameters: we find that in conjuncture with available remote sensing data, the implementation of these constraints results in a reduction in NEE uncertainty and an improved DALEC parameter estimation capability. We evaluated our model-data-fusion approach on a flux-tower scale, by comparing DALEC NEE fluxes against in-situ NEE measurements (AMERIFLUX network) across multiple biomes and plant-functional types (NEE bias = ± 1 gC m-2 day-1). By implementing our multiple constraint approach globally on a 1 ° x 1 ° resolution, we determined the magnitude and spatiotemporal distribution of major terrestrial C fluxes on a global scale. We anticipate that our global model-data-fusion approach will be an important step towards bridging the gap between remotely-sensed biometric data and the full ecosystem C cycle.