Chapter 13 Methods of Estimating Plant Productivity and CO2 Flux in Agro-Ecosystems – Liking Measurements, Process Models, and Remotely Sensed Information

Since both net primary production (NPP) and net ecosystem productivity (NEP) are the time-integrated values of CO2 exchange at the interface of plant, ecosystem, and atmosphere, continuous measurements of CO2 fluxes using methods such as eddy covariance may be the most direct and accurate approach. However, the geospatial assessment of key variables such as plant productivity and CO2 flux is essential because terrestrial ecosystems are quite heterogeneous. Remotely sensed information plays a crucial role for scaling up such ecosystem variables obtained by point measurements. Biophysical and ecophysiological process models have also an important role in the assessment and prediction of plant productivity and carbon flux, since they dynamically change interacting with many environmental variables. Despite the significant potential of these two methods, they both have limitations in ecological and ecophysiological applications; thus, synergistic linkage between the two methods is required. This chapter overviews the recent advancements in remote sensing of ecophysiological variables as a basis for such applications, and conducts methodological investigations on the synergy between remote sensing and process modeling based on some case studies. A case study based on airborne remote sensing data demonstrates the normalized difference vegetation index (NDVI) among various vegetation indices and may be useful enough for approximate assessment of plant productivity at an ecosystem scale. Another case study also shows that the soil surface CO2 flux (SSFCO2) is most closely related to the remotely sensed soil surface temperature, while air temperature is less well correlated and soil temperature and soil water content are poorly correlated. Remotely sensed surface temperature will provide useful information for investigation of CO2 transfer processes near the soil surface, as well as for quantitative assessment of ecosystem surface CO2 flux (ESFCO2). It is clearly shown that a synergy of remote sensing and a soil–vegetation–atmospheric transfer (SVAT) model; parameterization of the model with remote sensing signatures is promising for estimating important ecosystem variables such as biomass growth and ecosystem CO2 flux. This approach allows the effective use of infrequent and multisource remote sensing data.