## 1 Introduction
In remote sensing studies, the Normalized Difference Vegetation Index (NDVI) computed from red and infra-red satellite channels is commonly used to estimate fPAR [14,15]. The final equation for satellite-based GPP estimation will be the following [16,17]:  
\[GPP\ = LUE\ *\ fPAR\ *\ PAR \tag{5}\]  
The amount of the solar radiation reaching the canopy (PAR) is usually derived from remotely sensed data or computed using common mathematical algorithms [18,19]. An important attribute of remotely sensed data is that vegetation properties are known over the entire area and estimates of GPP/NPP can be obtained for each pixel in the area to be studied. However, satellite estimates of GPP/NPP as well as other ecosystem fluxes may not be very accurate [20]. Operational monitoring of GPP/NPP at the global scale is now underway using imagery from the satellite-borne Moderate Resolution Imaging Spectrometer (MODIS) sensor [3,13]. The MODIS NPP algorithm incorporates separate estimation of gross primary production and autotrophic respiration to account for potential differences in light use efficiency between vegetation types due to differences in respiration costs and climate conditions. These biome- and climate-induced ranges of LUE are taken into account in the MODIS algorithm by incorporating the optimal biome-specific LUE parameter, derived from extensive set of observations, and linear ramp functions of minimum temperature and vapour pressure deficit. The lower and upper limits of these functions are biome-specific constants [13,16,21]. Since the year 2000, by using five different data products from MODIS and two constant datasets, namely a digital elevation model and global soil information, the aboveground biomass in terms of GPP/NPP is operationally being calculated as 8-day time steps and as an annual sum with a spatial resolution of 1-km. However, MODIS GPP/NPP products require extensive validation, which is being in progress using ground truth data from different regions, to be useful for scientific purposes. The ongoing validation of MODIS products reveals significant biome- and region-specific over- or underestimation of GPP/NPP. Thus, the validation study by [21] reported about moderate underestimation in the MODIS products at agricultural land, and strong overestimation at grassland. Despite of the fact that MODIS GPP/NPP products cover the entire land surface and are freely available, the use of them in many cases may not always be reliable. In these cases, estimation of GPP/NPP using remote sensing data from other satellite systems can be preferable. Many satellite-based products only yield GPP [22-24], which, however, is only one of the carbon cycle processes that affect carbon sequestration and by its definition does not include respiration. Respiration is often incorporated as a climate-dependent factor in carbon sequestration models, where it is commonly calculated using complex mathematical models driven by climatic data [3,17]. Thisapproach, being used creatively, may supply a feasible alternative to the estimation of ecosystem respiration based on cost-expensive and time-consuming ecosystem carbon flux measurements. In a number of recent international projects such as AMERIFLUX, EUROFLUX, FLUXNET, HAPEX, ecosystem carbon fluxes are investigated in different ecosystems [25,26]. The main deficiency of the ecosystem carbon flux measurements is that their results are local. The challenge for modellers, therefore, is to find bounds to the application of results from small-scale studies in the elucidation of large-scale processes. Spatial extrapolation of small-scale results of flux measurements and monitoring carbon fluxes at larger spatial scales is feasible through combining ecosystem flux measurements and remote sensed data. The approach combining remotely sensed and ecosystem-flux data has been discussed several times [16,17,20,26,27].