## 1 Introduction  
Climate change is one of the great challenges of the 21 st century [1]; the average surface temperature has increased by 0.74 \(\,\mathrm{\SIUnitSymbolCelsius}\) over the past 100 years (1906-2005). Climate exerts the dominant influence on the spatial distribution of the major vegetation types on a global scale. In turn, vegetation cover affects the climate via the alteration of the physical characteristics of the land surface, including albedo, roughness, water conductivity and atmospheric gas composition [2]. The increasing concentration of greenhouse gases (GHG) in the atmosphere has been verified as the most important cause of global warming, which is a major environmental concern and a prominent research topic [3; 4; 5]. According to the World Data Center for Greenhouse Gases [6], the average global carbon dioxide (CO\({}_{2}\)) concentration in 2010 was 389.0 ppm, which is 11.9 ppm greater than in 2004; this figure has increased 39% from the pre-industrial global level of 280.0 ppm. Observations of GHG in the atmosphere are important for predicting global climate change. However, it is difficult to observe the global variation of GHG because the direct sampling of gases, especially in the upper atmosphere, requires great effort and cost [7]. The increased atmospheric CO\({}_{2}\) emissions from the burning of fossil fuels [8; 9] and land-use changes [10; 11; 12] are such that terrestrial and marine environments are able to absorb approximately one-half to three-quarters of the emitted CO\({}_{2}\)[13; 14]. To understand the relationship between land-cover changes and GHG concentration, great efforts have been made to measure the GHG emissions from forests and agricultural systems in recent years [11; 15; 16; 17], and numerous data from field measurements and laboratory studies have been accumulated. However, global and regional GHG emissions estimates are still far from reliable because of the large spatial and temporal variations of the emissions records on which they are based. There has been an intensive effort in recent years to measure and model the carbon cycles between the terrestrial biosphere and the atmosphere using a combination of modeling, remote sensing and observation data from eddy covariance flux towers. The eddy covariance technique offers an alternative for continuously and steadily assessing the long-term ecosystem carbon exchange. Furthermore, carbon budgets and the effects of environmental controls have been quantified with this technique for many forest types across the continent [11; 17; 18; 19; 20; 21]. However, these carbon estimates from the eddy covariance technique only represent fluxes at the scale of the tower footprint, ranging from a hundred meters to several kilometers. To quantify the net exchange of CO\({}_{2}\) over regions or continents, flux tower measurements need to be up-scaled to large areas [17]. In [22] the DeNitrification and DeComposition (DNDC) model, which focused on N\({}_{2}\)O and CO\({}_{2}\) emissions, was developed. However, this model was mainly based on the cropping practices and soil conditions in the U.S. and China and cannot be used directly for the global carbon cycle [9; 15]. An international network [23] has been formed to support such large-scale studies and provides standardized methods to develop \"optical scaling\" [24]. Today, more than 300 surface observation stations contribute to the WDCGG and World Meteorological Organization (WMO) by providing GHG data observed by flask sampling or _in situ_, measured worldwide at the Earth's surface, from towers, onboard aircraft in the atmosphere and in oceanic environments [25, 26]. Although ground-based measurements of GHG are highly accurate, they are sparse. Launching satellites to collect GHG data can solve this issue quite well.