Abstract
Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (101–102 km2), regional (103–106 km2) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. 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°44´48" N, 115°04´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. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g C m−2) by 25–30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20–25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 g C m−2 day−1 (non-optimized, ~43.5% of mean measured daily value) to 1.18 g C m−2 day−1 (optimized, ~22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.
Highlights
Growing interest in climate change has stimulated recent research that aims to quantify components of the natural carbon (C) cycle
The remotely sensed gross primary productivity (GPP) using the optimized algorithm can explain 92% of the seasonal variations of eddy-covariance technique (EC) observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle
Most months showed elliptical shapes of the footprint distributed along the NNWSSE prevailing wind directions while the seasonal variations in size and orientation of footprint for QYZ were obvious
Summary
Growing interest in climate change has stimulated recent research that aims to quantify components of the natural carbon (C) cycle. EC measurements are a rich source of information on temporal variability and environmental controls of CO2 exchange between the atmosphere and terrestrial ecosystems (Law et al, 2000). These global EC datasets provide investigators with opportunities and information to (1) explore emergent-scale properties by quantifying how the metabolism of complex ecosystems respond to perturbations in climate variables on diurnal, seasonal, interannual and decadal time scales and elucidate physical and biological controlling factors (Law et al, 2000; Baldocchi, 2008); (2) examine the carry-over effects that may be introduced by either favourable or deleterious. The available EC data have been rapidly accumulating, much of this information is of limited use because of the difficulties/uncertainties in (i) assessing/interpreting the associated measuring biases of EC data and (ii) upscaling of the EC fluxes at the ecosystem (typically less than 1–3 km for each site) to larger scales, e.g. landscape and regional scales
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