The objective of this study was to investigate the potential of synergy between biophysical/ecophysiological models and remote sensing for the dynamic estimation of biomass and net ecosystem exchange of CO2 (NEECO2). We obtained a long-term data set of micrometeorological, plant, and remote sensing measurements over well-managed uniform agricultural fields. The NEECO2 was measured using the eddy covariance method (ECM), and remote sensing signatures were obtained using optical and thermal sensors. A soil-vegetation-atmosphere transfer (SVAT) model was linked with remotely sensed signatures for the simulation of CO2 and water fluxes, as well as biomass, photosynthesis, surface temperatures, and other ecosystem variables. The model was calibrated and validated using an 8-year data set, and the performance of the model was excellent when all necessary input data and parameters were available. However, simulations using the model alone were subject to great uncertainty when some of the important input/parameters such as soil water content were unavailable. Dynamic optimization of parameter/input for the SVAT model using remotely sensed information allows us to infer the target parameters within the model or unknown inputs for the model through iterative optimization procedures. A robust relationship between the leaf area index (LAI) and the normalized difference vegetation index (NDVI) was derived and used for optimization. Our results showed that simulated biomass and NEECO2 agreed well with those measured using destructive sampling and the ECM, respectively. Remotely sensed information can greatly reduce the uncertainty of simulation models by compensating for insufficient availability of data or parameters. This synergistic approach allows the effective use of infrequent and multisource remote sensing data for estimating important ecosystem variables such as biomass growth and ecosystem CO2 flux.