Assimilating Satellite Reflectance Data Into an Ecosystem Model to Constrain Estimates of Terrestrial Carbon Flux.

Earth Observation (EO) data provides a unique constraint to models of terrestrial vegetation growth. Such models are important tools for analysing biogeochemical cycles. Data Assimilation (DA) is a viable mechanism by which EO data may be used to constrain vegetation model behaviour. Assimilating high--level EO products, such as Leaf Area Index is an attractive option. There generally exists a linear relationship between the derived variable and the state vector of the model. This simplifies the implementation of the DA scheme and reduces computational overheads. However, high--level EO products often contain assumptions contrary to those in vegetation models and so this approach is unsatisfactory on a philosophical level. Furthermore, on a practical level it is often difficult to quantify error in high--level products; this is critical for well functioning DA schemes. This paper presents the alternative approach of assimilating so--called low--level EO products: in this case MODIS surface reflectance (MOD09). This is demonstrated with a simple ecosystem model (DALEC) and used to quantify the landscape scale carbon budget for a ponderosa pine site in Oregon. The assumptions made in the production of MOD09 from core satellite measurements do not generally conflict with those in vegetation models. Errors are also easier to quantify than for high--level products. To facilitate assimilation however, an observation operator is required to translate from the vegetation model to the surface reflectance. Bayesian calibration of DALEC coupled with an observation operator is undertaken using eddy--covariance data from a site on the Oregon transect, MOD09 reflectance, and field data. Using MOD09 data alone the model is re-calibrated for other nearby flux tower sites. In this fashion the utility of the low--level EO data to improve modelled estimates of the carbon budget is demonstrated. By extension it is possible to infer how well the EO data improves the carbon modelling at the landscape level.