Estimating Dynamics of CO2 Flux in Agro-Ecosystems based on Synergy of Remote Sensing and Process Modeling —A Methodological Study

Abstract. Since terrestrial ecosystems are quite heterogeneous, the spatial assessment of key variables such as plant productivity and CO2 flux is essential. Remotely sensed information plays a crucial role in scaling up such ecosystem variables. This paper presents the results from investigations that explore useful relationships between remotely sensed information and important ecosystem variables such as soil surface CO2 flux and plant growth. A synergistic linkage between remote sensing and ecosystem models was also investigated based on long-term measurements of CO2 flux by the eddy covariance method. The soil surface CO2 flux was most closely related to the remotely-sensed soil surface temperature, while air temperature was less well correlated and soil temperature and soil water content were poorly correlated. The synergy of remote sensing and process-based modeling was quite effective in utilizing infrequent and multi-source remote sensing data. This approach should have great potential for quantitative and dynamic assessment of mul-tiple variables in terrestrial ecosystems