Synergy of Remote Sensing and Biophysical Process-based Modeling for Estimating Dynamics of CO<sub>2</sub> Flux in Agro-ecosystems

Abstract

The objective of this study was to investigate the potential of the synergy between the biophysical/ecophysiological models and remote sensing signatures for dynamic estimation of key biophysical variables at the ecosystems-atmosphere interface. We obtained a long-term and comprehensive data set of micrometeorological, plant, and remote sensing (optical and thermal domains) measurements over well-managed uniform agricultural fields. The net ecosystem CO2 flux (NEECO2) was measured by the eddy covariance method (ECM). A soil-vegetation-atmosphere transfer (SVAT) model was used to describe the energy balance, water budget and physiological processes that allowed simulation of the seasonal change of CO2 and water fluxes as well as biomass, photosynthesis, soil water, and surface temperatures. Remotely sensed spectral reflectance was useful to effectively tune the process-based model, so that biomass, evapotranspiration, and CO2 flux were accurately simulated. Simulated NEECO2 agreed well with those measured by ECM, while simulated biomass agreed well with independent measurements. The synergy of remote sensing and process-based modeling was quite effective in utilizing infrequent and multi-source remote sensing data. This approach has great potential for quantitative and dynamic assessment of multiple variables in terrestrial ecosystems.