Growing interest in climate change has stimulated recent research that aims to quantify components of the natural carbon (C) cycle. The eddy-covariance technique (EC) is commonly used to directly measure the CO₂, water vapour and energy exchange between the atmosphere and terrestrial ecosystems. In order to use EC flux measurements to provide estimates of components of the natural C cycles at landscape (10¹– 10² km²), regional (10³–10⁶ km²), or hemispheric to global land surface (10⁷–10⁸ km²) scales, they must be reasonably “upscaled” using either models and/or remote-sensing measurements(e.g. Earth observation – EO – data).

Prof./Dr. CHEN Baozhang at State Key Laboratory of Resource and Environment Information System (LREIS), Institute of Geographic Sciences and Natural Resource Research (IGSNRR), Chinese Academy of Sciences  and his team developed a data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26^o44’48’’ N, 115^o04’13’’ E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study.

The performance of a new algorithm based on the advanced vegetation indices using the LANDSAT data was evaluated with assistance of a footprint model. A good agreement between predicted with optimized parameters and EC measured weekly gross ecosystem productivity (GPP) in 2004 in an evergreen needleleaf forest at QYZ (R² = 0.92, p <0.001) indicates that there exist a good quantitative relationship between the LANDSAT vegetation indices and CO₂ flux data, in terms of the seasonal phase and magnitude of photosynthesis.

These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modeling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.

The research has recently been published in Biogeosciences, 2010 (7): 2943–2958, doi:10.5194/bg-7-2943-2010.