## 1 Introduction  
The exchanges of carbon dioxide (CO\({}_{2}\)) and water (H\({}_{2}\)O) between the land surface and atmosphere are key processes in understanding regional and global climate change. An accurate observation of these fluxes will deepen our understanding of land-atmosphere interaction and research on climate change [1]. To provide benchmark data for a model's calibration or validation [2, 3, 4, 5], the eddy covariance (EC) technique has been widely used as a micrometeorological tool to directly measure carbon and water fluxes at the ecosystem scale [6; 7; 8; 9]. Meanwhile, a global coverage of the EC flux sites, intergraded with remote sensing or process-based photosynthesis models, has greatly improved the quantifying and understanding of the dynamic process of large-scale carbon and water budgets over different ecosystems [10; 11; 12; 13; 14; 15; 16; 17]. An important requirement of utilizing EC observations is that the underlying surface of the EC measurements is uniform and the observation of EC represents the target vegetation type. However, it must be recognized that over naturally vegetated surfaces, variability and heterogeneity are the rules [18]. Due to the heterogeneous nature of the land surface and the variation of EC source areas, it is necessary to be fully aware of the spatial representativeness of EC measurements [19]. Therefore, the temporal variation of source area in EC measurements, and the spatial heterogeneity over the observed underlying surface, should be noticed. The source areas of EC flux can be calculated by footprint modeling, which is affected by the height of the EC flux measurement above the canopy, wind speed and direction, and the roughness, standard deviation of the crosswind velocity, atmospheric stability and the height of the boundary layer [20; 21; 22]. Meanwhile, satellite remote sensing products provide unprecedented opportunities for observing land surface information over the planet. Vegetation types, retrieved from remote sensing images, can be used as an indicator to detect the coverage of a flux measurement for a target vegetation type [23; 24; 25; 26; 27]. The vegetation indices calculated from remote sensing data can be used as a surrogate for vegetation production for a given ecosystem [28; 29; 30; 31]. Therefore, integrating flux footprint modeling with remote sensing images is a feasible way to detect the spatial representativeness of flux measurements [25; 26; 32]. The network of EC flux towers, commonly known as FLUXNET, has rapidly extended in recent years. However, the spatial representativeness of the EC measurements accounting for surface heterogeneity has not been fully considered. Although some works have evaluated the spatial representativeness of EC measurements [24; 25; 32; 33], studies on long-term changes in spatial representativeness are still desperately needed [26]. In China, large-scale forest plantations have become a dominant driver of greening and carbon sinks on the earth for at least the past two decades [17; 34]. Meanwhile, studies on the carbon and water fluxes of forest plantations have been widely conducted in different climate zones [35; 36; 37; 38; 39; 40; 41; 42; 43]. However, the importance of the spatial representativeness of the EC measurements in these studies is still not fully recognized. The footprint awareness of EC measurements is particularly vital in the monsoon area due to the seasonal reversal of wind direction. Therefore, it is critical to be aware of the long-term information on source areas when utilizing EC measurements to better understand carbon and/or water-cycling processes. In this study, a flux tower of a subtropical coniferous plantation with eight years (2003-2010) of continuous observation data was selected to calculate the footprint of the EC measurements. Then, integrated with remote sensing images, temporal changes in spatial representativeness were analyzed to examine the effects of spatial heterogeneity on the EC flux measurements.